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GPT-5.5 Pricing: How Much Does It Cost in 2026?

CometAPI03 — Thu, 07 May 2026 15:33:54 +0000

OpenAI released GPT-5.5 on April 23, 2026, positioning it as a "new class of intelligence" optimized for agentic workflows—autonomous multi-step tasks like coding, web browsing, data analysis, and complex problem-solving.

The model rolled out quickly to ChatGPT Plus, Pro, Business, and Enterprise users, with API access following shortly. However, the pricing sparked immediate debate: standard GPT-5.5 costs $5 per 1M input tokens and $30 per 1M output tokens—exactly double the rates of GPT-5.4 ($2.50/$15). The Pro variant jumps to $30/$180.

Is this premium justified by superior performance, or should users stick with previous versions or alternatives?

CometAPI can help you access frontier models like GPT-5.5 more efficiently and cost-effectively (20% discount).

What Is GPT-5.5? Key Features and Improvements

GPT-5.5 builds on the GPT-5 family (initially launched in 2025) with enhanced agentic capabilities. It excels at long-horizon tasks, tool use, and maintaining coherence over extended sessions.

Core Specifications (as of late April 2026):

Context Window: Up to 1M tokens (ideal for large codebases, documents, or research).
Output Limit: Up to 128K tokens in many configurations.
Multimodal: Strong text, code, and tool integration; improved reasoning chains.
Modes: Standard and "Fast" mode (1.5x faster generation at 2.5x cost in Codex); Pro tier for highest accuracy.
Availability: ChatGPT (Plus/Pro tiers default or selectable), Codex, and API (Responses/Chat Completions).

Major Improvements Over GPT-5.4:

Better autonomous agent performance (e.g., debugging, spreadsheet filling, multi-tool orchestration).
Gains on key benchmarks: +11.7 percentage points on ARC-AGI-2, +8.1 on MCP Atlas, +7.6 on Terminal-Bench 2.0.
Potential token efficiency: Completes some complex tasks with fewer tokens, partially offsetting the price hike.

OpenAI claims it represents a step toward more reliable "computer use" agents, reducing human oversight in professional workflows.

That matters because price alone does not tell the whole story. A model can be “expensive” on paper and still be cheaper in practice if it reduces debugging time, lowers hallucination risk, or cuts back-and-forth on a high-value task. GPT-5.5 is exactly the kind of model that sits in that category.

GPT-5.5 Pricing Breakdown: ChatGPT Plans and API Costs

Consumer/ChatGPT Subscriptions (May 2026)

Free/Go: Limited or no GPT-5.5 access (GPT-5.3 or lower in most cases).
Plus ($20/mo): GPT-5.5 Thinking mode with baseline limits (e.g., ~160 messages/3h). Good for individuals.
Pro ($100–$200/mo tiers): GPT-5.5 Pro with 5x–20x higher usage, ideal for heavy users.
Business/Enterprise: Custom or per-seat (~$20/user annual), with admin controls and higher limits.

Break-even Analysis: For heavy users, the $20 Plus plan can be more economical than raw API calls. One estimate places the break-even around 1,379 messages/month on GPT-5.5 (assuming typical token usage of ~0.0145 per message). Heavy users (46+ messages/day) benefit from subscriptions.

For most users, Plus delivers strong value. Pro shines for power users exhausting limits daily.

API Pricing (Standard gpt-5.5)

Input: $5.00 / 1M tokens
Cached Input: $0.50 / 1M tokens
Output: $30.00 / 1M tokens
Context Window: 1M tokens (API); 400K in Codex
Long Context (>272K): 2x input / 1.5x output for the session
Batch/Flex: 50% off standard
Priority: 2.5x standard
GPT-5.5 Pro: $30 input / $180 output (much higher accuracy for complex tasks)

Real-World Cost Examples:

A 10K input / 2K output coding task: ~$0.11 (standard).
Enterprise-scale workloads (millions of tokens daily) can reach thousands of dollars monthly, though efficiency gains may mitigate this.

Pricing has escalated steadily: GPT-5 started lower, GPT-5.4 at $2.50/$15, now doubled again in weeks. GPT-5.5 is 2x more expensive per token, but OpenAI claims ~40% fewer output tokens for Codex/agentic tasks, leading to ~20% effective cost increase for many workloads.

GPT-5.5 vs GPT-5.4: The Real Price Gap

GPT-5.4 is OpenAI’s lower-cost frontier model for coding and professional work. Its standard API price is $2.50 per 1M input tokens and $15.00 per 1M output tokens, with the same 1,050,000-token context window and the same 128,000 max output tokens listed on the model page. In simple terms, GPT-5.5 costs about 2x GPT-5.4 on both input and output tokens, while keeping the same headline context and output limits.

That is the heart of the decision. If GPT-5.5 produces noticeably better code, better reasoning, fewer revisions, or cleaner final outputs, the extra cost can be trivial. If it does not, GPT-5.4 is the better buy because you get the same context window and output ceiling for half the price.

A concrete example makes the trade-off easier to see. For a request with 100,000 input tokens and 20,000 output tokens, GPT-5.5 costs about $1.10, while GPT-5.4 costs about $0.55. That is only a 55-cent difference for one request, but at scale the spread gets large fast.

That said, OpenAI explicitly says GPT-5.5 is “more intelligent and much more token efficient” than GPT-5.4, and that in Codex it has been tuned to deliver better results with fewer tokens for most users. That means raw price alone does not tell the whole story; a model that takes fewer turns, fewer retries, and fewer tokens to complete a task can be cheaper in practice even with a higher sticker rate.

Comparison table: GPT-5.5 vs GPT-5.4

Metric	GPT-5.5	GPT-5.4	What it means
Standard input / output	$5 / $30 per 1M tokens	$2.50 / $15 per 1M tokens	GPT-5.5 costs more, but aims to return stronger results.
Batch / Flex input / output	$2.50 / $15 per 1M tokens	$1.25 / $7.50 per 1M tokens	Same relative gap, but better for non-urgent workloads.
Priority input / output	$12.50 / $75 per 1M tokens	$5 / $30 per 1M tokens	For urgent work, but it gets expensive fast.
SWE-Bench Pro (public)	58.6%	57.7%	Small but real coding improvement.
Terminal-Bench 2.0	82.7%	75.1%	Better agentic coding and terminal execution.
GDPval	84.9%	83.0%	Better on professional-work tasks.
FinanceAgent v1.1	60.0%	56.0%	Better for finance-like workflows.

Price vs Competitor: GPT-5.5, Claude, and Gemini

Here is the comparison that matters most for buyers. Claude Opus 4.7 starts at $5 per 1M input tokens and $25 per 1M output tokens, and Anthropic says it features a 1M context window. Google’s Gemini 2.5 Pro is priced at $1.25 input / $10 output on the standard tier for prompts at or under 200K tokens, with higher rates above that threshold, and it supports a 1,048,576-token input limit and 65,536-token output limit.

That means GPT-5.5 is not the cheapest premium model on the market. It is more expensive than Gemini 2.5 Pro on standard pricing, and slightly more expensive than Claude Opus 4.7 on output tokens. But GPT-5.5 still competes hard because of the combination of context window, output ceiling, and OpenAI’s positioning for coding and professional work.

A fair apples-to-apples example: with 100,000 input tokens and 20,000 output tokens, GPT-5.5 costs about $1.10, GPT-5.4 about $0.55, Claude Opus 4.7 about $1.00, and Gemini 3.1 Pro is lower. That makes Gemini the lowest-cost option in this slice, GPT-5.4 the best-value OpenAI option, and GPT-5.5 the premium OpenAI option.

Comparison Table: GPT-5.5 vs. GPT-5.4 vs. Key Competitors

Model	Standard input	Standard output	Context window	Max output	Best fit
GPT-5.5	$5.00 / 1M	$30.00 / 1M	1,050,000	128,000	Premium coding, professional work
GPT-5.4	$2.50 / 1M	$15.00 / 1M	1,050,000	128,000	Lower-cost coding and business tasks
Claude Opus 4.7	$5.00 / 1M	$25.00 / 1M	1,000,000	Not stated on cited pricing page	Complex coding, agentic work
Gemini 3.1 Pro	$2 （<20 $2 / $12 (<200,000 tokens) $4 (>200,000 tokens)	$12 (<200,000 tokens) $18 (>200,000 tokens)	1,048,576	65,536	Multimodal, long-context, budget-conscious teams

Competitor Snapshot (per 1M tokens, flagship models):

Claude Opus 4.7: ~$5 input / $25 output (cheaper on output).
Gemini 3.1 Pro: Often lower (e.g., ~$2/$12 range for similar tiers).
Open-source/DeepSeek alternatives: Fractions of the cost (e.g., <$1 combined).

Is GPT-5.5 Worth It?

Yes, if the work is high-value enough. GPT-5.5 makes sense when you are paying for outcomes rather than tokens: shipping code faster, reducing error-prone iterations, producing better agentic workflows, or improving output quality in customer-facing systems. OpenAI explicitly frames GPT-5.5 as the premium coding/professional model, which is the right lane for those use cases.

No, if you are generating a lot of routine content, testing prompts, or running workflows where raw token cost matters more than model quality. In those scenarios, GPT-5.4 usually gives you the better cost-performance ratio because it keeps the same context window and output limit at half the price.

There is also a real competitor angle. If your workload is dominated by long context and budget pressure, Gemini 3.1 Pro becomes extremely attractive on standard pricing. If you care about a strong coding model with aggressive caching and batch savings, Claude Opus 4.7 is a serious option.

For these use cases:

Complex agentic coding (Codex, autonomous agents).
Long-horizon projects requiring planning and tool use.
Professional/knowledge work where quality and reduced human review time justify the premium.
Teams already in the OpenAI ecosystem (seamless integration).

No (or use sparingly), for:

Simple Q&A, content generation, or high-volume chat (stick to GPT-5.4 mini or cheaper alternatives).
Budget-constrained startups (effective 2x pricing hurts at scale without efficiency gains).

ROI Calculation Example:

Assume a coding task: GPT-5.4 uses 100K output tokens ($1.50). GPT-5.5 uses 60K ($1.80) but completes 30% faster with fewer fixes → net savings in developer time. At scale (thousands of tasks), this compounds.

Break-even: If GPT-5.5 saves >20-30% in tokens + significant review time, it pays for itself quickly for power users.

When GPT-5.5 Is the Right Buy

GPT-5.5 is most defensible for product teams, software teams, and agencies that need a premium model for code generation, debugging, reasoning-heavy workflows, or final-pass quality. The model’s pricing is high enough that it should not be your default “cheap text generator,” but it is reasonable as a top-tier lane in a mixed-model stack.

A practical rule of thumb is this: use GPT-5.5 when one avoided mistake is worth more than the per-request difference versus GPT-5.4. If a bug fix, support escalation, or lost conversion is expensive, the premium model can pay for itself very quickly. That is especially true in code review, agent orchestration, customer support drafts, and internal automation. This is an inference from the price spread and the model positioning, not a vendor guarantee.

When GPT-5.4 or a Competitor Is Smarter

GPT-5.4 is the obvious default if you want an OpenAI model but do not need the very top tier. It is cheaper, has the same headline context and output limits, and is already positioned by OpenAI as the more affordable option for coding and professional work.

Claude Opus 4.7 is compelling when you want a frontier coding model with a 1M context window and you value Anthropic’s cost controls. Anthropic says Opus 4.7 starts at $5/$25 and offers up to 90% savings with prompt caching and 50% savings with batch processing, which can materially change the economics for repeated or large workflows.

Gemini 2.5 Pro is the most aggressive value play in this comparison. Google describes it as its state-of-the-art multipurpose model for coding and complex reasoning, and the published standard price for smaller prompts is dramatically lower than GPT-5.5. For many teams, that makes Gemini a strong “first model to test” before moving to a premium OpenAI lane.

How to Access GPT-5.5 Cheaper: Enter CometAPI

For many users and developers, direct OpenAI pricing isn't the most economical path. As a developer-friendly platform, CometAPI offers reliable access to GPT-5.5 alongside competitors. Benefits include competitive pricing through routing, detailed analytics, fallback mechanisms to avoid downtime, and support for large-scale API usage. Check CometAPI for current GPT-5.5 endpoints, SDK compatibility, and special offers

CometAPI Advantages:

GPT-5.5: Around $4/$5 per 1M (input/output) with discounts (up to 20%+ reported across models).
GPT-5.5 Pro: Competitive at ~$24/$30 range.
Pay-as-you-go, no subscriptions required for core access.
Free credits/tokens for new users, unified API for switching between OpenAI, Anthropic, Grok, DeepSeek, Llama, etc.
Transparent dashboard, high reliability, and support for high-volume usage.

Code Examples: Testing GPT-5.5 Efficiency

Here's Python code using the OpenAI SDK (or compatible via CometAPI) to compare costs and usage. Always monitor actual token usage.

import os
from openai import OpenAI
import tiktoken  # For rough token estimation

client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))  # Or CometAPI key for compatibility

def estimate_cost(input_text, output_tokens_estimate, model="gpt-5.5"):
    enc = tiktoken.encoding_for_model("gpt-5.5")  # Approximate
    input_tokens = len(enc.encode(input_text))
    if model == "gpt-5.5":
        input_cost = (input_tokens / 1_000_000) * 5.00
        output_cost = (output_tokens_estimate / 1_000_000) * 30.00
    elif model == "gpt-5.4":
        input_cost = (input_tokens / 1_000_000) * 2.50
        output_cost = (output_tokens_estimate / 1_000_000) * 15.00
    else:
        input_cost = output_cost = 0
    return input_tokens, input_cost + output_cost

# Example usage
prompt = "Write a detailed agentic script for automating data migration with error recovery..."
input_toks, est_cost_55 = estimate_cost(prompt, 80000, "gpt-5.5")  # Assume 80K output
_, est_cost_54 = estimate_cost(prompt, 120000, "gpt-5.4")  # More tokens for older model

print(f"GPT-5.5 Est. Cost: ${est_cost_55:.4f} for ~{input_toks} input tokens")
print(f"GPT-5.4 Est. Cost: ${est_cost_54:.4f}")

Run A/B tests on your workloads—track tokens via API responses (usage field) to validate efficiency claims.

Strategies to Maximize Value and Minimize Costs

Prompt Engineering & Caching: Use cached inputs heavily ($0.50/M).
Batch Processing: 50% savings.
Hybrid Workflows: GPT-5.5 for critical steps; cheaper models (GPT-5.4 mini, Gemini) for routine.
Monitoring: Implement token tracking and alerts.
Alternatives via Aggregators: Platforms like CometAPI allow seamless switching or fallback, often with better rates, unified billing, and optimization features tailored for high-volume users on CometAPI.

Conclusion: Is GPT-5.5 Worth It?

Yes, for specific high-value use cases where agentic intelligence and reliability deliver outsized returns (e.g., professional coding, complex automation). The doubled price is partially offset by capabilities and efficiency, but it's not a blanket upgrade for everyone.

For most users and developers: A strategic mix—GPT-5.5/Pro for critical tasks, cheaper models for volume—delivers the best results. Platforms like CometAPI make this easy and affordable, offering near-official performance at lower effective costs with broader choice.

CometAPI Integration Tip: Replace the client initialization with your CometAPI endpoint/key for unified access to multiple providers, potential lower latency, or bundled pricing. CometAPI often provides competitive routing and monitoring tools to optimize spend across GPT-5.5, alternatives, and caching.

GPT Image 2 Vs Nano Banana 2: Which is Better is 2026

CometAPI03 — Thu, 07 May 2026 15:30:58 +0000

In the rapidly evolving world of AI image generation, April 2026 marked a pivotal moment. OpenAI launched ChatGPT Images 2.0 powered by the gpt-image-2 model, immediately claiming the top spot on major leaderboards and sparking intense debates across Reddit, YouTube, and AI communities. Meanwhile, Google's Nano Banana 2 (built on Gemini 3.1 Flash Image architecture), released earlier in February 2026, had already set high standards for speed and photorealism.

For developers and businesses seeking cost-effective, unified access to both models (and 500+ others including LLMs, video generators, and more), platforms like CometAPI offer a single API endpoint that simplifies integration, reduces vendor lock-in, and often provides competitive pricing compared to direct providers.

What Is GPT Image 2? OpenAI's State-of-the-Art Image Model

GPT Image 2 (officially tied to ChatGPT Images 2.0) represents OpenAI's most advanced native image generation and editing model as of April 2026. Unlike earlier DALL·E series models, it integrates deeply with ChatGPT's reasoning capabilities, enabling "thinking" modes that allow web search, multi-image generation from one prompt, and enhanced instruction following.

Key Features and Improvements:

Superior Text Rendering: Reports indicate near-perfect accuracy (up to 99.2% in some tests), making it ideal for UI mockups, logos, posters, and any image requiring legible text, including multilingual support (English primary, with improvements in Chinese, Hindi, etc.).
Spatial Logic and Composition: Excels at complex multi-element scenes, precise object placement, and structural control. It handles dense compositions, iconography, and subtle stylistic constraints better than predecessors.
Image Editing: Strong performance in single- and multi-image editing, preserving identity and following detailed instructions.
Resolution and Flexibility: Supports flexible aspect ratios (e.g., 3:1 wide to 1:3 tall) and high-fidelity outputs up to 4K in some workflows.
Reasoning Integration: Can double-check outputs, generate variations, or create coherent sets (e.g., multi-panel comics or marketing assets in different sizes).

Launch Impact: Within hours of release, GPT Image 2 topped the Image Arena leaderboard with an Elo score around 1,512 on text-to-image tasks, creating a reported 242-point gap over the previous leader (Nano Banana 2 at ~1,360 in pre-launch or competing benchmarks). This is described as the largest gap in Arena history.

What Is Nano Banana 2? Google's Fast, Photorealistic Contender

Nano Banana 2, Google's latest image generation model (technically Gemini 3.1 Flash Image), launched around February 26, 2026. It bridges the gap between the high-fidelity "Pro" tier (Nano Banana Pro) and ultra-fast Flash performance, combining advanced reasoning, world knowledge, and production-ready speed.

Key Features and Strengths:

Generation Speed: Significantly faster—often 3-5 seconds per image versus longer times for heavier models. This makes it ideal for rapid iteration, high-volume production, and real-time applications.
Photorealism and Aesthetics: Frequently praised for cinematic lighting, hyper-realistic textures, natural skin tones, and atmospheric depth, it produces "more realistic" results in direct comparisons, avoiding the overly polished look of some OpenAI outputs.
Real-Time Grounding: Integrates Google Search for up-to-date knowledge, enabling timely images (e.g., current events or trending styles). Supports 4K resolution and strong subject/character consistency across multiple objects (up to 5 characters or 14 objects reported in tests).
Editing and Control: Excellent for photo editing, style blending, and maintaining consistency with reference images. Includes SynthID watermarking for AI-generated content.
Text Rendering: Improved over earlier versions but generally trails GPT Image 2 in precision for complex or dense text layouts (strong for infographics).
Market Positioning: Nano Banana 2 emphasizes efficiency for professional workflows like product mockups, ad variations, social media assets, and video frame generation. It delivers "Pro-level" quality at Flash speeds, making it highly cost-effective for scale.

Head-to-Head Comparison: GPT Image 2 vs Nano Banana 2

Community benchmarks, LM Arena data, GitHub rigs judged by Claude Opus, and YouTube side-by-sides reveal a clear split in strengths rather than a outright winner.

1. Text Rendering and UI/Branding Tasks

GPT Image 2 Wins Decisively: Near-flawless text accuracy, layout hierarchy, and iconography. Ideal for mockups, logos, menus, posters, or any text-heavy content. One analysis noted 99.2% accuracy versus lower rates for competitors.
Nano Banana 2: Solid improvements but can struggle with dense or stylized text. Better suited for simpler overlays or when photorealism takes priority.
Use Case Winner: GPT Image 2 for branding and professional design assets.

2. Photorealism, Lighting, and Artistic Quality

Nano Banana 2 Often Preferred: Delivers more natural, cinematic results with superior textures and lighting. Reddit users frequently comment that Nano Banana outputs look "more realistic" or less "AI-polished."
GPT Image 2: Strong photorealism with excellent detail, but some testers find it overly refined or painting-like.
Use Case Winner: Nano Banana 2 for photography-style images, portraits, product visuals, or atmospheric scenes.

3. Prompt Adherence, Spatial Logic, and Complex Compositions

GPT Image 2 Excels: Superior structural control, object placement, and following nuanced instructions. Handles multi-object scenes and logical consistency better in blind tests.
Nano Banana 2: Strong reasoning via Gemini architecture, with good consistency for characters and objects, aided by real-time search.
Use Case Winner: GPT Image 2 for intricate scenes or precise creative direction.

4. Speed and Iteration

Nano Banana 2 Dominates: 3-5 seconds typical generation time enables fast workflows. GPT Image 2 can be slower, especially in reasoning/thinking modes (up to 10-30+ seconds in some reports).
Use Case Winner: Nano Banana 2 for high-volume or time-sensitive tasks.

5. Image Editing and Reference Image Handling

Both perform well, but GPT Image 2 shines in precise, instruction-based edits. Nano Banana 2 excels at style transfer and maintaining consistency with references while being faster.
Community tests show mixed results; some prefer Nano Banana for realistic edits.

6. Cost and Accessibility

Nano Banana 2 generally offers better speed-to-cost ratio for volume.
GPT Image 2 may command a premium for its precision and reasoning depth.
Developer Tip: Using an aggregator like CometAPI allows seamless switching between models (and others like Midjourney, Flux variants, or video tools) via one API key, optimizing for cost and performance without managing multiple accounts. CometAPI supports unified access to frontier image models, often with transparent pricing and easy integration for apps, automation (n8n, Make), or production pipelines.

Comprehensive Comparison Table: GPT Image 2 vs Nano Banana 2

Metric	GPT Image 2 (OpenAI)	Nano Banana 2 (Google Gemini 3.1 Flash)	Winner / Notes
Text Rendering	Excellent (99.2% accuracy, dense text/UI)	Good (improved, strong for infographics)	GPT Image 2
Photorealism	Very High (polished, detailed)	Superior (natural lighting, textures)	Nano Banana 2
Speed	Medium (slower in thinking mode)	Very Fast (3-5 sec typical)	Nano Banana 2
Spatial Logic/Composition	Superior (precise control)	Strong (good consistency)	GPT Image 2
Prompt Adherence	Excellent (reasoning integration)	Very Good (real-time search grounding)	Tie / Task-dependent
Image Editing	Strong precise instruction following	Fast, consistent with references	GPT for precision; Nano for speed
Resolution	Up to 4K, flexible ratios	4K production-ready	Tie
Elo / Leaderboard	~1,512 (top spot post-launch)	~1,360 (strong contender)	GPT Image 2 (larger gap reported)
Best For	Branding, UI, complex scenes, text-heavy	High-volume, photorealistic, rapid iteration	Depends on needs
Pricing signal	gpt-image-2 is $8 input and $30 output per 1M tokens	Gemini 2.5 Flash Image pricing shows $0.30 per 1M tokens for input and about $0.039 per 1024×1024 output image on standard tier.	CometAPI offers a 20% discount on API pricing and playGround testing.
API Access via CometAPI	Available through unified endpoint	Available through unified endpoint	CometAPI for easy switching

Real-World Use Cases and Community Feedback

YouTube and Reddit tests (e.g., "GPT Image 2 vs Nano Banana 2 using reference images") show subjective preferences: some favor Nano Banana's realism, others GPT's control. Blind tests judged by Claude often lean toward GPT Image 2 overall, but individual prompts vary.

Latest news (as of April 28-29, 2026) shows continued buzz: OpenAI's release has users testing multi-image outputs and web-grounded generations, while Google iterates on Nano Banana consistency. The gap remains a hot topic, with some calling it a "tie" in specific niches and others declaring GPT Image 2 the new king.

Use Cases

Marketing & Social Media: Nano Banana 2's speed wins for quick asset variations and trending visuals. GPT Image 2 for polished campaign materials with accurate branding text.
Product Design & E-commerce: GPT Image 2 for mockups and UI; Nano Banana 2 for lifestyle product shots.
Content Creation (Blogs, Books): GPT Image 2 for illustrative covers or infographics requiring text.
Development & Automation: Both integrate well via APIs. CometAPI users report streamlined workflows, consolidating image generation with LLMs and video models (e.g., Veo, Kling) under one key—reducing overhead for apps or pipelines. One user highlighted switching from separate platforms for images and text to CometAPI for efficiency.

Limitations and Considerations

GPT Image 2: Higher potential cost and latency in advanced modes; occasional "over-polished" aesthetic; still evolving multilingual support.
Nano Banana 2: May lag in ultra-precise text or highly complex spatial logic; relies on ecosystem (Gemini) for full features.
Ethical/Safety: Both include watermarks (SynthID for Google). Always review provider policies on commercial use and copyright.
Censorship/Guardrails: Vary; test sensitive prompts carefully.

How to Access and Integrate: Recommendation for Developers

Direct access is available via OpenAI API/ChatGPT for GPT Image 2 and Gemini for Nano Banana 2. However, for production-scale or multi-model needs, CometAPI stands out as a robust solution. It aggregates 500+ models—including the latest image generators—through a single, developer-friendly API.

Why Choose CometAPI for GPT Image 2 and Nano Banana 2?

Unified Interface: Switch models with minimal code changes.
Cost Optimization: Often competitive rates; monitor usage across image, text, and video in one dashboard.
Scalability: Supports high-volume generation, automation tools (n8n, Make), and custom pipelines.
Ease of Use: Comprehensive docs, API keys, and support for popular models beyond these two (e.g., Midjourney, Stable Diffusion variants).

Sign up at CometAPI, obtain your API key, and start testing both models side-by-side in your workflows. Many users consolidate traffic to reduce management overhead while accessing frontier capabilities affordably.

Final Verdict: Which Should You Choose?

There is no universal winner in GPT Image 2 vs Nano Banana 2—it depends on your priorities:

Choose GPT Image 2 for precision, text accuracy, branding, complex compositions, and when reasoning depth matters most.
Choose Nano Banana 2 for speed, photorealism, high-volume output, and atmospheric, natural-looking images.
Best Strategy: Use both via a unified platform like CometAPI. Test prompts relevant to your use case, monitor costs, and iterate. The 2026 AI image landscape rewards flexibility.

Ready to experiment? Head to CometAPI to access GPT Image 2, Nano Banana 2, and hundreds of other AI models through one powerful API. Optimize your creative and production pipelines today

How to use GLM-5.1 with Claude Code

CometAPI03 — Tue, 28 Apr 2026 16:51:34 +0000

The AI coding assistant market changed dramatically in 2026. For nearly a year, many developers treated Claude Code as the gold standard for agentic development workflows. It was trusted for repository understanding, terminal operations, multi-file refactoring, and autonomous debugging.

But there was one major problem: Claude Code itself is excellent—but Claude model costs are expensive.

That changed when Z.ai released GLM-5.1, a new flagship model optimized specifically for agentic engineering.

Unlike traditional “chat models,” GLM-5.1 was built for:

long-horizon coding tasks
stepwise execution
process adjustment
terminal-heavy engineering workflows
multi-stage autonomous problem solving

Z.ai explicitly states that GLM-5.1 is “further optimized for agentic coding workflows such as Claude Code and OpenClaw.”

This is a major shift. Instead of replacing Claude Code, developers can now keep the Claude Code workflow they love while swapping in a significantly cheaper model backend.

CometAPI simplify access to GLM-5.1 alongside 500+ other models through a single unified API, helping you avoid vendor lock-in and optimize expenses.

What Is GLM-5.1?

Z.ai positioned GLM-5.1 as a model "towards long-horizon tasks," building on GLM-5 (released February 2026). It features a massive 754B-parameter architecture (with Mixture-of-Experts efficiency) and enhancements in multi-turn supervised fine-tuning (SFT), reinforcement learning (RL), and process-quality evaluation.

Core strengths include:

Autonomous execution: Up to 8 hours of continuous work on a single task, including planning, coding, testing, refinement, and delivery.
Stronger coding intelligence: Significant gains over GLM-5 in sustained execution, bug fixing, strategy iteration, and tool use.
Open-source accessibility: Released under the permissive MIT License, with weights available on Hugging Face (zai-org/GLM-5.1) and ModelScope. Supports inference via vLLM, SGLang, and more.
API availability: Accessible via api.z.ai, CometAPI, and compatible with Claude Code, OpenClaw, and other agentic frameworks.

Why Developers Care About GLM-5.1

The biggest reason is simple:

It is much cheaper than Claude Opus while approaching similar coding performance.

Some published benchmark reports show:

Claude Opus 4.6: 47.9
GLM-5.1: 45.3

This places GLM-5.1 at roughly 94.6% of Claude Opus coding performance while often costing dramatically less. ([note（ノート）][4])

For startups and engineering teams running thousands of agent loops per month, this difference is enormous.

Cost is no longer a minor optimization.

It becomes infrastructure strategy.

Latest Benchmarks: How GLM-5.1 Stacks Up

GLM-5.1 delivers state-of-the-art results on key agentic and coding benchmarks, often matching or exceeding frontier models:

SWE-Bench Pro (real-world GitHub issue resolution with 200K token context): 58.4 — outperforming GPT-5.4 (57.7), Claude Opus 4.6 (57.3), and Gemini 3.1 Pro (54.2).
NL2Repo (repository generation from natural language): Substantial lead over GLM-5 (42.7 vs. 35.9).
Terminal-Bench 2.0 (real-world terminal tasks): Wide margin improvement over predecessor.

Across 12 representative benchmarks covering reasoning, coding, agents, tool use, and browsing, GLM-5.1 shows balanced, frontier-aligned capabilities. Z.ai reports overall performance closely matching Claude Opus 4.6, with particular strength in long-horizon autonomous workflows.

Comparison Table: GLM-5.1 vs. Leading Models on Key Coding Benchmarks

Benchmark	GLM-5.1	GLM-5	GPT-5.4	Claude Opus 4.6	Gemini 3.1 Pro	Qwen3.6-Plus
SWE-Bench Pro	58.4	55.1	57.7	57.3	54.2	56.6
NL2Repo	42.7	35.9	41.3	49.8	33.4	37.9
Terminal-Bench 2.0	Leads	Baseline	-	-	-	-

(Data sourced from Z.ai official blog and independent reports; scores as of April 2026 release. Note: Exact Terminal-Bench figures vary by evaluation setup.)

These results position GLM-5.1 as one of the strongest open-weight options for agentic engineering, closing the gap with proprietary models while offering local deployment flexibility and lower long-term costs.

What Is Claude Code? Why Pair It with GLM-5.1?

Claude Code is Anthropic's agentic coding CLI tool (released in preview 2025, generally available 2025). It goes beyond autocomplete: you describe a feature or bug in natural language, and the agent explores your codebase, proposes changes across multiple files, executes terminal commands, runs tests, iterates based on feedback, and even commits code.

It excels in multi-file edits, context awareness, and iterative development but traditionally relies on Anthropic's Claude models (e.g., Opus or Sonnet) via their API.

Why switch or augment with GLM-5.1?

Cost efficiency: Z.ai's GLM Coding Plan or third-party proxies often provide better value for high-volume agentic workloads.
Performance parity: GLM-5.1's long-horizon strengths complement Claude Code's agent loop, enabling longer autonomous sessions without frequent human intervention.
Compatibility: Z.ai explicitly supports Claude Code via an Anthropic-compatible endpoint (https://api.z.ai/api/anthropic).
Open-source freedom: Run locally or via affordable providers to avoid rate limits and data privacy concerns.
Hybrid potential: Combine with Claude models for specialized tasks.

Users report seamless integration, with GLM backends handling full agentic workflows (e.g., 15+ minute sessions) reliably.

How to Use GLM-5.1 with Claude Code

Core Architecture

Claude Code expects Anthropic-style request/response behavior.

GLM-5.1 commonly exposes:

OpenAI-compatible endpoints
provider-specific APIs
hosted cloud APIs
self-hosted deployments

This creates a compatibility problem.

The solution is an adapter layer.

Architecture Flow

Claude Code
↓
Adapter / Proxy Layer
↓
GLM-5.1 API Endpoint
↓
Model Response
↓
Claude Code Tool Loop Continues

This is the standard production approach.

Setup Method 1: OpenAI-Compatible Proxy

Most Common Production Setup

A proxy translates: Anthropic → OpenAI

and then OpenAI → Anthropic

This allows Claude Code to work with any OpenAI-compatible provider.

Examples include:

Claude Adapter
Claude2OpenAI
custom gateways
internal infrastructure proxies

Anthropic itself also documents OpenAI SDK compatibility for Claude APIs, showing how provider translation layers have become normal practice.

Typical setup:

export ANTHROPIC_BASE_URL=https://your-adapter-endpoint.com
export ANTHROPIC_API_KEY=your-api-key
export MODEL=glm-5.1

Your adapter handles the rest.

This allows Claude Code to believe it is talking to Claude while the actual inference happens on GLM-5.1.

Setup Method 2: Direct Anthropic-Compatible Gateway

Cleaner Enterprise Setup: Some providers now offer direct Anthropic-compatible endpoints. This removes translation overhead and improves reliability. This is where CometAPI becomes particularly valuable.

Step-by-Step: How to Set Up GLM-5.1 with Claude Code

1. Install Claude Code

Ensure you have Node.js installed, then run:

npm install -g @anthropic-ai/claude-code

Verify with claude-code --version.

2. Get Your GLM-5.1 Access

Options:

Official Z.ai API: Sign up at z.ai, subscribe to GLM Coding Plan, and generate an API key at https://z.ai/manage-apikey/apikey-list.
Local deployment: Download weights from Hugging Face and run with vLLM or SGLang (requires significant GPU resources; see Z.ai GitHub for instructions).
CometAPI (recommended for ease): Use services with Anthropic-compatible endpoints.

Z.ai provides a helpful coding-helper tool: npx @z_ai/coding-helper to auto-configure settings. Sign up at CometAPI and get the API key, then use glm-5.1 in your claude code.

Quick integration recommendation:

Sign up at CometAPI.com and obtain your API key.
Set ANTHROPIC_BASE_URL to CometAPI's Anthropic-compatible endpoint.
Specify "GLM-5.1" (or the exact model ID) as your default Opus/Sonnet model.
Enjoy unified billing and access to the full model catalog for hybrid workflows.

CometAPI is particularly valuable for teams or power users running Claude Code at scale, as it aggregates the latest models (including GLM-5.1) and reduces operational overhead. Many developers already use it for Cline and similar agentic tools, with official discussions on GitHub highlighting its developer-friendly design.

3. Configure settings.json

Edit (or create) ~/.claude/settings.json:

{
  "env": {
    "ANTHROPIC_AUTH_TOKEN": "your_CometAPI_api_key_here",
    "ANTHROPIC_BASE_URL": "https://api.cometapi/v1",
    "API_TIMEOUT_MS": "3000000",
    "ANTHROPIC_DEFAULT_OPUS_MODEL": "GLM-5.1",
    "ANTHROPIC_DEFAULT_SONNET_MODEL": "GLM-5.1"
  }
}

Additional tweaks: Increase context handling or add project-specific configs in .claude directories.

For isolated setups, tools like cc-mirror allow multiple backend configurations.

4. Launch and Test

Run claude-code in your project directory. Start with a prompt like: "Implement a REST API endpoint for user authentication with JWT, including tests."

Monitor the agent as it plans, edits files, runs commands, and iterates. Use flags like --continue for resuming sessions or --dangerously for advanced operations.

5. Local or Advanced Deployments

For fully private setups:

Use Ollama or LM Studio to run GLM-5.1 locally, then proxy to Claude Code.
Configure vLLM with FP8 quantization for efficiency on high-end hardware.

Community videos and GitHub gists detail Windows/macOS/Linux variations, including environment variable setups for fish/zsh shells.

Troubleshooting tips:

Ensure API key has sufficient quota (monitor peak/off-peak billing).
Extend timeouts for long-horizon tasks.
Skip onboarding with "hasCompletedOnboarding": true in config.
Test with small tasks first to validate model mapping.

Optimizing Performance and Costs with GLM-5.1 in Claude Code

Real-world usage data:

Developers report processing millions of tokens daily with GLM backends, achieving cost savings versus pure Anthropic usage.
Long sessions benefit from GLM-5.1's stability; one user noted 91 million tokens processed over days with consistent results.

Best practices:

Structure prompts with clear CLAUDE.md files for architecture guidelines.
Use tmux or screen for detached long-running sessions.
Combine with test oracles and progress tracking for scientific or complex engineering tasks.
Monitor token usage—agentic loops can consume context quickly.

Cost comparison (approximate, based on 2026 reports):

Direct Anthropic Opus: Higher per-token rates for heavy use.
Z.ai GLM Coding Plan: Often 3× quota multiplier but lower effective cost, especially off-peak.
Price hikes on some GLM plans (e.g., Pro subscriptions) have pushed users toward alternatives.

Why Use CometAPI for GLM-5.1 and Claude Code Integration?

For developers seeking simplicity, reliability, and broad model access, CometAPI.com stands out as a unified gateway to 500+ AI models—including GLM-5.1 from Zhipu, alongside Claude Opus/Sonnet variants, GPT-5 series, Qwen, Kimi, Grok, and more.

Key advantages for your Claude Code workflow:

Single API key: No need to manage separate credentials for Z.ai, Anthropic, or others. Use OpenAI-compatible or Anthropic-compatible endpoints.
Competitive pricing: Often 20-40% savings versus direct providers, with generous free tiers (e.g., 1M tokens for new users).
Seamless compatibility: Route Claude Code traffic through CometAPI's endpoints for GLM-5.1 without complex proxy setups.
Multi-model flexibility: Easily A/B test GLM-5.1 against Claude Opus 4.6 or others by switching model names in your settings.json.
Enterprise features: High uptime, scalable rate limits, multi-modal support, and real-time access to new releases.
No vendor lock-in: Experiment with local models or switch providers instantly.

Best Practices for Using GLM-5.1 in Claude Code

1. Keep Tasks Long-Horizon

GLM-5.1 performs best when given:

full implementation goals
multi-step objectives
repository-level tasks

instead of micro-prompts.

Bad:

“Fix this one line”

Good:

“Refactor authentication flow and update tests”

This matches its design philosophy.

2. Use Explicit Permission Boundaries

Claude Code’s permission system is powerful but must be controlled carefully.

Recent research shows permission systems can fail under ambiguity-heavy tasks. ()

Always define:

allowed directories
deployment boundaries
production restrictions
destructive command limits

Never rely on defaults.

3. Manage Context Aggressively

Context engineering is now a real discipline.

Studies show unnecessary tabs and excessive file injection are major invisible cost drivers. ()

Use:

context compaction
selective file inclusion
repo summarization
instruction files

This improves both cost and accuracy.

4. Separate Planning from Execution

Best production pattern:

Planner Model

Claude / GPT / GLM high reasoning mode

↓

Executor Model

GLM-5.1

↓

Validator Model

Claude / specialized test layer

This multi-model routing often outperforms single-model workflows.

Common Mistakes

Mistake 1: Using Subscription Workarounds

Some developers attempt to use consumer Claude subscriptions instead of API billing.

This creates account risk and violates provider policies. I strongly recommends proper API-key-based usage rather than subscription hacks.

Avoid shortcuts,and use production-grade architecture.

Mistake 2: Treating GLM-5.1 Like ChatGPT

GLM-5.1 is not optimized for “chatting.”

It is optimized for:

autonomous engineering
coding loops
tool use
terminal workflows

Use it like an engineer, not like a chatbot.

Advanced Tips and Comparisons

GLM-5.1 vs. GLM-5: GLM-5.1 offers ~28% coding improvement in some evaluations, better long-horizon stability, and refined post-training that reduces hallucinations by significant margins.

Hybrid setups: Use GLM-5.1 for heavy lifting (long sessions) and route specific reasoning steps to Claude or other models via multi-provider configs.

Potential limitations:

Peak-hour quota multipliers on official plans.
Hardware requirements for fully local runs.
Occasional need for prompt engineering in edge cases (though improved over GLM-5).

GLM-5.1's "fantastic" for C++ and complex projects, often outperforming expectations in sustained reasoning, In some tasks, it can match Claude Opus 4.6, and its basic performance is comparable to Claude Sonnet 4.6.

Comparison Table

Attribute	GLM-5.1	Claude Opus 4.6	DeepSeek V4	GPT-5.5
Agentic coding optimization	Excellent	Excellent	Strong	Strong
Claude Code compatibility	Excellent	Native	Requires adapter	Requires adapter
Cost efficiency	Very High	Low	Very High	Medium
Long-horizon task performance	Excellent	Excellent	Strong	Strong
Open-weight availability	Yes	No	Partial	No
MIT License	Yes	No	No	No
Terminal-heavy workflows	Excellent	Excellent	Good	Good
Vendor lock-in risk	Low	High	Medium	High

GLM-5.1 is particularly attractive because it combines:

near-top-tier coding performance
open deployment flexibility
significantly lower cost

This combination is rare.

Conclusion: Elevate Your Coding Workflow Today

Integrating GLM-5.1 with Claude Code unlocks powerful, autonomous software engineering at competitive prices. With SOTA SWE-Bench Pro performance, 8-hour task endurance, and easy setup via Anthropic-compatible API, this combination is a game-changer for 2026 developers.

For the smoothest experience—especially if you want access to GLM-5.1 plus hundreds of other top models without juggling keys—head to CometAPI. Their unified platform, generous free tier, and cost savings make it the recommended choice for scaling agentic coding projects reliably.

Start experimenting today: Install Claude Code, configure your GLM-5.1 backend (via Z.ai or CometAPI), and let the agent build. The era of long-horizon AI engineering is here—make it part of your toolkit.

GPT 5.5: What It Is, Key Features, Benchmarks, How to Use It

CometAPI03 — Tue, 28 Apr 2026 15:58:00 +0000

OpenAI released GPT-5.5 on April 23, 2026, describing it as its "smartest and most intuitive model yet" and a major step toward agentic AI that handles complex, multi-step work with minimal guidance. This latest frontier model builds on the rapid iteration seen in the GPT-5 series (following GPT-5.4 just weeks earlier), emphasizing improved reasoning, tool use, coding, research, data analysis, and computer operation. It aims to shift users from micromanaging prompts to assigning "messy, multi-part tasks" that the model plans, executes, verifies, and completes autonomously.

CometAPI now supports the GPT-5.5 series（GPT-5.5 API and GPT-5.5 Pro API）.

What Is GPT-5.5? Core Architecture and Advancements

GPT-5.5 is OpenAI's latest proprietary large language model in the GPT-5 family, internally codenamed "Spud" in some reports. It is a ground-up advancement focused on agentic capabilities—the ability to understand high-level goals, break them down, use external tools, navigate ambiguity, self-correct, and persist until task completion.

Key improvements over predecessors like GPT-5.4 include:

Enhanced contextual understanding and reduced hallucinations, allowing it to handle longer, more complex workflows.
Better efficiency: Matches GPT-5.4's per-token latency while using significantly fewer tokens for equivalent tasks in tools like Codex.
Stronger safeguards: OpenAI applied its most robust safety measures to date, including red-teaming for cybersecurity and biology risks. The model meets "High" risk classification but stays below the "Critical" threshold for severe harm.
Modalities: Primarily text with strong vision and tool-use integration; no native image/audio/video output mentioned in the launch.

OpenAI positions GPT-5.5 as moving beyond chatbots toward "a new way of getting work done on a computer," powering everything from autonomous coding agents to research assistants.

A variant, GPT-5.5 Pro, targets even higher-accuracy scenarios (e.g., advanced math, scientific research, or complex enterprise tasks) and is available to higher-tier users.

What GPT-5.5 does better

1) Agentic coding and debugging

GPT-5.5 is strongest in coding-related work. The launch materials describe it as the model’s strongest agentic coding system to date, with 82.7% on Terminal-Bench 2.0 and 58.6% on SWE-Bench Pro. OpenAI also says it outperforms GPT-5.4 on an internal long-horizon engineering benchmark called Expert-SWE. The signal here is not just better code generation; it is better problem decomposition, more persistent debugging, and stronger end-to-end task completion.

For product teams, that matters because coding tasks rarely end at the first answer. They involve context retention, iterative fixes, environment changes, tests, and verification. GPT-5.5 is being tuned for exactly that kind of workflow, especially inside Codex, where the model is framed as handling implementation, refactors, debugging, testing, and validation more reliably than earlier versions.

2) Computer use and tool orchestration

GPT-5.5 also shows gains in computer-use tasks. On OSWorld-Verified, it scores 78.7%, compared with 75.0% for GPT-5.4. That matters because many real business tasks are not “chat” tasks at all; they are browser tasks, desktop tasks, and multi-tool tasks. In the release notes, OpenAI emphasizes that GPT-5.5 can move across tools until the task is finished, which is exactly the kind of capability enterprises want for automation, support, and internal operations.

3) Research, analysis, and knowledge work

The model is also positioned for knowledge work. On GDPval, which evaluates agents on work across many occupations, GPT-5.5 scores 84.9%, versus 83.0% for GPT-5.4. On BixBench, it scores 80.5% versus 74.0%, suggesting a meaningful improvement in scientific and data-analysis style workflows. The release materials additionally describe stronger performance in online research and in document-heavy work such as spreadsheets and structured analysis.

That makes GPT-5.5 relevant for roles that blend writing, analysis, and tool use: analysts, product managers, operations teams, revenue teams, technical writers, and research-oriented builders. The model’s value is not that it answers harder trivia questions. Its value is that it can help move a workstream forward with less intervention.

4) Efficiency and Reduced Hallucinations

Users report fewer factual errors in long tasks. The model self-corrects and verifies outputs more consistently.

5) Multimodal and Creative Tasks

\While focused on text/agentic work, it integrates with vision and other modalities where supported in the ChatGPT interface.

GPT-5.5 benchmark comparison table

Area	GPT-5.5	GPT-5.4	What it suggests
Terminal-Bench 2.0	82.7%	75.1%	Better command-line execution and multi-step coding workflows.
SWE-Bench Pro	58.6%	57.7%	Modest but real improvement in resolving real GitHub issues end to end.
OSWorld-Verified	78.7%	75.0%	Stronger computer-use and desktop automation performance.
GDPval	84.9%	83.0%	Better performance on professional knowledge-work tasks.
BrowseComp	84.4%	82.7%	Better web research and browsing-style task handling.

The bigger story is not one score in isolation. It is the pattern across coding, browsing, computer use, and professional task suites. GPT-5.5 is showing gains where agents actually break: tool coordination, context retention, and task persistence.

GPT-5.5 vs Previous Models and Competitors: Comparison Table

Here's a side-by-side comparison based on available data (as of late April 2026):

Aspect	GPT-5.5 (OpenAI)	GPT-5.4 (OpenAI)	Claude Opus 4.7 (Anthropic)	Gemini 3.1 Pro (Google)
Release Date	April 23, 2026	~March 2026	Recent 2026 variant	Recent 2026 variant
Strength	Agentic tasks, messy prompts, computer use	Strong baseline reasoning	Safety-focused, long context	Multimodal integration
Coding/Agentic	Superior single-pass completion, tool chaining	Good, but requires more guidance	Competitive	Strong in some benchmarks
Research/Data	Excellent autonomous synthesis	Improved over 5.3	Very strong	Good with search integration
Efficiency (Tokens)	Fewer tokens for complex tasks	Baseline	Efficient	Varies
Context Window	Up to 1M tokens (API)	Smaller	Large	Large
Cyber Risk	"High" (with safeguards)	Lower	Emphasizes safety	Varies
Availability	ChatGPT paid tiers + API	Similar	Subscription/API	Via Google platforms

Compared to Anthropic's Claude Opus 4.5/4.7 or Google's Gemini, GPT-5.5 claims leadership in agentic coding and computer use. It beats many benchmarks while offering seamless integration into the OpenAI ecosystem (ChatGPT + Codex + API). Versus GPT-4o, the jump in coding (SWE-Bench) and reasoning is dramatic. Versus GPT-5.4, gains are incremental but meaningful in efficiency and reliability—ideal for production agents.

GPT-5.5 edges out in intuitive, hands-off execution for real-work scenarios. Competitors may lead in specific niches (e.g., multimodal depth or extreme safety tuning). Always test in your workflow, as benchmarks don't capture every use case.

GPT-5.5 Pro: when the higher tier matters

GPT-5.5 Pro is not just a branding add-on. GPT-5.5 Pro improves on several difficult workloads, including BrowseComp at 90.1%, GDPval at 82.3%, FrontierMath Tier 1–3 at 52.4%, and FrontierMath Tier 4 at 39.6%. The launch post also says early testers used GPT-5.5 Pro more like a research partner, critiquing manuscripts over multiple passes, stress-testing arguments, and working across code, notes, and PDF context.

That makes the distinction between GPT-5.5 and GPT-5.5 Pro fairly practical. The base model is the general workhorse. The Pro tier is for harder, slower, more accuracy-sensitive work where multi-pass reasoning and deeper exploration matter more than raw speed.

How to Use GPT-5.5: Step-by-Step Guide

1. Via ChatGPT Interface

Subscribe to Plus ($20+/month), Pro ($100+/month for Pro variant), Business, or Enterprise.
Select GPT-5.5 (or GPT-5.5 Pro) in the model picker.
For best results: Provide high-level goals rather than micromanaging steps. Example prompt: "Research the latest trends in renewable energy storage, analyze key papers, create a comparison spreadsheet, and draft a 10-page executive summary with citations."
Use built-in tools (web browsing, data analysis, code interpreter) for agentic flows.
Enable "Thinking" or reasoning modes where available for deeper analysis.

ChatGPT plan access snapshot

Plan	GPT-5.5 Thinking	GPT-5.5 Pro
Free	No	No
Go	No	No
Plus	Expanded	No
Pro	Unlimited	Yes
Business	Flexible	Flexible
Enterprise	Flexible	Flexible

2. Via OpenAI API (Now Available)

Pricing:

GPT-5.5: $5 / 1M input tokens, $30 / 1M output tokens (1M context).
GPT-5.5 Pro: $30 / 1M input, $180 / 1M output.
Batch/Flex: ~50% off standard rates; Priority: 2.5x. Cached input significantly cheaper (~$0.50).

Model IDs: gpt-5.5, gpt-5.5-pro (with reasoning.effort parameters: none/low/medium/high/xhigh).

Example Python code using official SDK:

Pythonfrom openai import OpenAI 
client = OpenAI(api_key="your_key") response = client.chat.completions.create
( model="gpt-5.5", messages=[{"role": "user", "content": "Your complex task here..."}], temperature=0.7, max_tokens=4096 )

Leverage streaming, tool calling, and function calling for agents. Set reasoning effort for balance between speed and depth.

Integrating GPT-5.5 with CometAPI: Cost-Effective and Flexible Access

For developers and businesses seeking reliable, affordable access without managing multiple vendor keys, CometAPI provides an excellent solution. CometAPI offers a unified OpenAI-compatible REST API that aggregates 500+ models, including the latest OpenAI releases like GPT-5.5 series, alongside alternatives from Anthropic, Google, and others.

The price is 20% of the official price.

Why Choose CometAPI for GPT-5.5?

Cost Savings: Access GPT-5.5 and similar models at 20-40% lower pricing than official channels, with no vendor lock-in. New users often receive free tokens.
Seamless Compatibility: Point your existing OpenAI SDK to https://api.cometapi.com/v1 and swap model names—no code rewrites needed.
Reliability: Enterprise-grade infrastructure with high availability, global CDN, and support for streaming, tool calls, and large contexts.
Flexibility: Switch between GPT-5.5, GPT-5.5 Pro, or competitors (e.g., Claude Opus variants) by changing a single parameter. Ideal for A/B testing or fallback strategies.
Easy Integration: Works with frameworks like LangChain, LlamaIndex, or custom agents. Example setup mirrors the official SDK but uses your CometAPI key and base URL.

Getting Started with CometAPI:

Pythonfrom openai import OpenAI 
client = OpenAI( api_key="your_cometapi_key", base_url="https://api.cometapi.com/v1" ) # Then use model="gpt-5.5" or other supported IDs

Explore the model catalog for GPT-5.5 variants and combine with other top models for hybrid workflows.
Monitor usage via the dashboard for cost optimization.

For teams building on CometAPI, you can experiment with GPT-5.5 immediately, compare performance/cost in real time, and optimize workflows without vendor lock-in. It's particularly valuable for enterprises in regions like Hong Kong seeking stable, high-performance AI infrastructure.

Visit CometAPI today to explore pricing, supported models, and integration guides. Many users find it the most practical way to harness GPT-5.5's power without the full brunt of direct OpenAI costs or complexity.

GPT-5.5 vs GPT-5.4: should you upgrade?

For most teams, the upgrade question is not “Is GPT-5.5 better?” The data already points to yes. The more useful question is whether the improvement is big enough for your workload. If your tasks are short, transactional, or heavily template-based, GPT-5.4 may still be sufficient. If your tasks involve code changes, browser actions, long research chains, or repeated tool use, GPT-5.5 is the more compelling choice because that is where the benchmark lift is strongest.

There is also a cost-quality tradeoff to consider. GPT-5.5’s API pricing is higher than older mainstream models, but it is being positioned as a model that needs fewer tokens per task because it gets to the right output faster and with less supervision. That does not make it cheap; it makes it potentially more efficient on completed work rather than on raw token consumption alone.

Best Practices for Optimal Results

Prompting: Start with clear goals and constraints. Let the model plan. Use follow-ups for refinement.
Agent Building: Chain calls with tool definitions (e.g., web search, code execution, database queries).
Monitoring: Track token usage and costs for production. Implement self-verification loops.
Iteration: Test on smaller tasks first; scale to full workflows.
Safety: Respect rate limits and content policies; the model includes strong safeguards against misuse.

Early users note that GPT-5.5 requires less prompt engineering than predecessors, rewarding natural language instructions.

You can access GPT-5.4 and GPT-5.5 at a cheaper price through CometAPI and switch between them at any time.

Conclusion: Is GPT-5.5 Worth It in 2026?

GPT-5.5 marks another acceleration in OpenAI's cadence toward truly useful agentic AI. Its strengths in autonomous task completion, coding, and knowledge work make it a powerful tool for professionals and developers—backed by strong benchmark gains and efficiency improvements. However, the higher pricing underscores the need for strategic access.

For most users and teams, combining ChatGPT/Codex for exploration with a flexible gateway like CometAPI for production delivers the best balance of performance, cost, and reliability. Start experimenting today: sign up for ChatGPT Pro/Plus to try GPT-5.5 directly, then integrate via CometAPI for scalable applications.

Kling 3.0 vs Veo 3.1: The Ultimate 2026 AI Video Generator Showdown

CometAPI03 — Mon, 20 Apr 2026 15:58:31 +0000

TL;DR

Kling 3.0 currently leads with native 4K multi-shot storytelling, superior camera control. Veo 3.1 excels in photorealistic physics, native audio synchronization, and Google ecosystem integration, making it ideal for cinematic or enterprise projects. For most users, the winner depends on priorities: Kling 3.0 for speed, consistency, and cost; Veo 3.1 for premium realism and audio.

Introduction

In 2026, AI video generation has evolved from experimental clips into professional-grade production tools. Two frontrunners dominate the landscape: Kling 3.0 from Kuaishou (released February 5, 2026) and Google’s Veo 3.1 (major updates October 2025–March 2026, with Lite tier).

Creators, marketers, filmmakers, and developers now ask the same question: Which model delivers the best results for your workflow?

Access both models affordably through a unified API like CometAPI (Veo 3.1 and Kling 3.0), which offers 20–40% lower pricing than official vendors with one-key integration.

Quick Feature Comparison

Feature	Kling 3.0 (Pro)	Veo 3.1 (Standard/Fast)	Winner
Max Resolution	Native 4K, 60fps options	4K (upscaling), 24fps cinematic	Kling 3.0
Video Duration	3–15s multi-shot (coherent scenes)	8–15s+ (extensions for longer)	Kling 3.0 (storytelling)
Multi-Shot/Narrative	Built-in AI Director (2–6 shots)	Scene extension + references	Kling 3.0
Character Consistency	Elements 3.0 (excellent)	Ingredients to Video (strong)	Kling 3.0
Native Audio	Multilingual dialogue, lip-sync, SFX	Best-in-class 48kHz sync & ambient	Veo 3.1 (sync) / Kling (multilingual)
Camera Control	Superior prompt adherence (pan, crane, POV)	Strong cinematic terms	Kling 3.0
Physics/Realism	Strong motion & physics	Industry-leading textures & lighting	Veo 3.1
Prompt Adherence	Excellent for structured prompts	Top-tier for complex descriptions	Tie
ELO Benchmark (Artificial Analysis, 2026)	1,249 (Pro) / 1,222 (Standard)	~1,225	Kling 3.0

Pros & Cons

Kling 3.0

Pros: Multi-shot storytelling, character consistency, 4K value, fast iteration for social/UGC.
Cons: Occasional audio quirks in complex multilingual scenes.

Veo 3.1

Pros: Photorealism, best native audio, Google integration, reliable physics.
Cons: Higher cost for max quality, shorter default clips without extensions, ecosystem lock-in.

What Is Kling 3.0?

Kuaishou’s Kling 3.0, launched February 5, 2026, represents a leap to a unified Multi-modal Visual Language (MVL) architecture. It processes text, images, audio, and video in a single model, enabling native 4K output, multi-shot generation (up to 15 seconds with 2–6 coherent shots), physics-aware motion, and built-in multilingual audio with lip-sync.

Key Innovations:

Multi-Shot AI Director: Structured prompts generate complete scenes with camera moves, transitions, and character consistency across cuts—no manual stitching required.
Elements 3.0: Create reusable characters, products, or assets for perfect consistency across videos.
Native Audio & Lip-Sync: Supports English, Chinese, Japanese, Spanish, and more, with dialogue, sound effects, and ambient noise generated simultaneously.
Resolution & Duration: Native 4K (Ultra tier), up to 15 seconds per generation (custom duration control), 1080p standard with 60fps options in Pro.
Image-to-Video Excellence: Top-rated for cinematic motion from reference images.

What Is Veo 3.1?

Google DeepMind’s Veo 3.1 (iterative updates from October 2025, with 4K enhancements in January 2026 and Lite tier in March) focuses on broadcast-ready quality, native audio, and seamless integration with Gemini, Vertex AI, and Google Flow.

Key Innovations:

Native Audio Pipeline: Generates synchronized 48kHz dialogue, sound effects, and ambient soundscapes in one pass—widely regarded as industry-leading for audiovisual sync.
Ingredients to Video: Up to 4 reference images for precise character/style control, plus scene extension for longer narratives (>60 seconds via chaining).
Physics & Realism: Exceptional prompt adherence, lighting, textures, and motion simulation; native vertical (9:16) support for Shorts/TikTok.
Variants: Standard (max quality, 4K), Fast (2.2x speed), Lite (budget 720p/1080p at ~50% cost).
Resolution & Duration: Up to 4K, typically 8–15+ seconds per clip (extensions available), 24fps cinematic default.

Motion Quality: The Physics Test

Kling 3.0: The Narrative Director

Kling's core strength is multi-shot coherence. When you prompt "camera starts close on coffee cup, pulls back to reveal café," Kling 3.0 executes the choreography with director-level precision.

Standout capabilities:

Camera movement vocabulary: Tracks complex motion like "dolly zoom" or "crane shot descending through tree canopy."
Object permanence: A red scarf stays red across 10-second clips, even as lighting changes.
Multi-element scenes: Handled "crowded subway + reflections on windows + depth-of-field shift" without object melting.

Trade-off: Motion is smooth but slightly slower-paced than real-world physics. Think "cinematic" vs "documentary." Good for commercials, awkward for sports footage.

Veo 3.1: The Physics Purist

Veo prioritizes photorealistic motion dynamics. Fabric drapes naturally, water splashes with correct velocity, smoke diffuses with real-world turbulence.

Where it dominates:

Lighting consistency: Veo's Standard mode maintains shadow directionality across scene cuts—something Kling still struggles with.
Sub-frame detail: Hair movement, cloth wrinkles, particle systems all render with sub-pixel accuracy.
Fast mode trade-offs: Veo Fast sacrifices some texture detail for 2x speed but retains motion coherence.

Weakness: Struggles with abstract camera moves. Prompting "spiral ascent around monument" often degrades into generic pan-up.

Prompt cost differences: First-Pass Success Rate

This is where real costs diverge from pricing sheets.

Veo 3.1: The Literal Interpreter

Veo 3.1 achieves higher first-pass accuracy on detailed prompts. When you specify "golden hour lighting, soft shadows, 35mm depth," Veo delivers without retry loops.

Estimated First-Pass Success: ~70-80% for complex prompts (based on production testing).

Implication: While Veo's per-second cost is higher, you're paying for reduced iteration. Veo's prompt adherence can reduce rework by 20-40% compared to Kling in multi-constraint scenarios.

Kling 3.0: The Creative Interpreter

Kling often improvises on ambiguous prompts—sometimes brilliantly, sometimes frustratingly.

Example:

Prompt: "Cyberpunk street, neon rain"
Kling delivers: Stunning neon reflections, but adds flying cars you didn't request.

Estimated First-Pass Success: ~50-60% for strict commercial briefs requiring exact specifications.

When to use: Exploratory creative work where "happy accidents" are valuable. For locked storyboards, budget 2-3 iterations.

Performance Benchmarks & Supporting Data

Independent tests (February–April 2026) across 100+ prompts show:

ELO Rankings: Kling 3.0 Pro holds #1 overall; its family dominates top 15. Veo 3.1 ranks #5 but leads in audio-specific categories.
Camera Movement Tests (Curious Refuge): Kling 3.0 won 4/5 scenarios (pan, tracking, POV, handheld) due to better prompt fidelity.
Audio-Visual Sync: Veo 3.1 edges ambient/environmental; Kling leads dialogue & multilingual lip-sync.
Generation Speed: Veo 3.1 Fast/Lite is quicker for iteration; Kling Pro delivers higher quality per second but may take longer for complex multi-shots.
Consistency Across Frames: Kling’s Elements system outperforms in character reuse; Veo shines in environmental realism.

Real-world example prompt test: “Cinematic tracking shot of a cyberpunk detective walking through neon Tokyo rain, multi-shot with close-up dialogue, 10 seconds, 4K.”

Kling 3.0: Flawless multi-shot transitions, natural lip-sync, consistent face.
Veo 3.1: Superior rain physics and lighting, but occasional minor drift in extended audio.

Pricing Transparency: The Real Engineering Cost

Many evaluations focus on per-second pricing—this creates decision bias. Here's the corrected framework:

Market Benchmarks (April 2026)

Model	Resolution	Price (USD/sec)	Notes
Veo 3.1 Fast	720p/1080p	~$0.15	Rapid prototyping
Veo 3.1 Standard	1080p+	~$0.40	High-quality + audio
Kling 3.0	Standard	~$0.12–0.15	Varies by API provider

Surface-Level Math (Misleading)

Veo Fast (5-sec clip): ~$0.75
Veo Standard (5-sec clip): ~$2.00
Kling 3.0 (5-sec clip): ~$0.70

The Real Formula: Total Cost of Ownership

Actual Cost = Base Price × Retry Rate × Volume

Scenario: You need 100 clips for a product launch.

Key insight: Kling's competitive unit price gets eroded by higher retry rates on precision-critical tasks. Veo's premium often translates to lower total delivery cost when deadlines are tight.

CometAPI Advantage: Unified access to both at 20–40% lower official pricing, pay-as-you-go, no vendor lock-in. Switch models with one line of code. Real-time dashboards track spend. Ideal for scaling—e.g., a 10-second 4K clip with audio costs significantly less than direct vendor rates.

Resolution & Output Quality

Kling 3.0: Native 4K, Future-Proof

Max resolution: 1080p standard, 4K experimental (via API flags).
Aspect ratios: 16:9, 9:16, 1:1—native support without cropping.
Frame rates: 24/30fps standard, 60fps in beta.

Use case: If you're delivering to cinema-grade clients or planning 8K upscaling pipelines, Kling's 4K native output is critical.

Veo 3.1: 1080p+, Optimized for Streaming

Max resolution: 1080p+ (exact upper limit undisclosed, but tests show consistent quality up to 1440p).
Audio integration: Standard mode includes synchronized audio—Kling requires separate audio workflows.
Compression: Better optimized for web delivery (smaller file sizes, perceptually lossless).

Trade-off: No 4K native. If you need ultra-high-res, Kling wins. For social/web content, Veo's compression efficiency matters more.

How to Access Kling 3.0 & Veo 3.1 via CometAPI: Developer Recommendations

For bloggers, agencies, or SaaS builders on ComeTAPI.com (CometAPI), the platform is the smartest entry point. One API key unlocks 500+ models (including Kling 3.0 Pro/Omni and Veo 3.1 variants) at discounted rates, with OpenAI-compatible SDK support and a playground for instant testing. No more juggling keys or waiting for vendor approvals—perfect for rapid prototyping or production scaling.

Python Integration Example (OpenAI-Compatible SDK)

import openai

client = openai.OpenAI(
    api_key="YOUR_COMETAPI_KEY",  # Get free at https://www.cometapi.com/
    base_url="https://api.cometapi.com/v1",
)

response = client.chat.completions.create(
    model="kling-3-0-pro",  # Or "veo-3-1-standard", "veo-3-1-fast", "kling-3-0-omni"
    messages=[{
        "role": "user",
        "content": "Generate a 10-second multi-shot video: A futuristic chef cooking in a flying kitchen, dramatic crane shot to close-up dialogue, cyberpunk style, 4K, native audio with sizzling sounds and voiceover."
    }],
    # Additional params for video: duration, aspect_ratio, etc. (check playground for exact)
)

print(response.choices[0].message.content)  # Returns video URL or generation ID

Start in the CometAPI Playground to compare outputs side-by-side without spending credits. Monitor costs live—ideal for optimizing long-tail content pipelines. Developers report 30%+ savings and faster iteration versus direct APIs.

Decision Framework: Which Tool for Which Job?

Choose Kling 3.0 if:

✅ You need multi-shot narrative control (ads, trailers, storytelling)
✅ 4K/future-proof output is non-negotiable
✅ Your team values API flexibility over vendor ecosystem
✅ You're okay with 2-3 iterations for complex prompts
✅ Budget is tight and you can absorb retry costs with time

Choose Veo 3.1 if:

✅ You need photorealistic physics (product demos, architectural walkthroughs)
✅ First-pass accuracy is critical (tight deadlines, fixed budgets)
✅ You're already in Google Cloud ecosystem
✅ Audio sync is required (Veo includes it, Kling doesn't)
✅ You prioritize web-optimized output over max resolution

Hybrid Strategy (Advanced Teams):

Use Kling for concept exploration (cheap iterations, creative variance)
Use Veo for final delivery (high fidelity, client-facing assets)
Route tasks via feature flags: Narrative → Kling / Product shots → Veo

Use CometAPI to A/B test both in the same pipeline—e.g., Kling for initial drafts, Veo for final polish.

Conclusion: Which Should You Choose in 2026?

Kling 3.0 is the narrative architect—it understands story beats, camera language, and multi-element choreography. Its 4K output and API accessibility make it ideal for indie studios and experimental workflows. But you'll pay with iteration time.

Veo 3.1 is the physics perfectionist—it renders reality with obsessive accuracy and minimizes rework through superior prompt adherence. Veo 3.1 remains unbeatable for audio-driven cinematic work and enterprise polish.

The smartest strategy? Leverage CometAPI for unified, discounted access to both—test, iterate, and scale without limits.

Ready to build? Sign up for your free CometAPI key today and start generating professional videos with Kling 3.0 or Veo 3.1 in minutes.

DeepSeek v4 is now available on the web: How to access and test it

CometAPI03 — Mon, 13 Apr 2026 15:59:41 +0000

In a move that has sent ripples through the global AI community, DeepSeek has quietly rolled out a gray-scale test of its highly anticipated V4 model on the web. Leaked interface screenshots reveal a transformative three-mode system—Fast, Expert, and Vision—positioning DeepSeek V4 as a multimodal powerhouse with deep-reasoning capabilities that could rival or surpass leading models like Claude Opus and GPT-5 variants.

This isn't just another incremental update. With rumored 1 trillion parameters, a 1 million token context window powered by novel Engram memory architecture, and native image/video processing, DeepSeek V4 promises to deliver enterprise-grade performance at consumer-friendly costs. Whether you're a developer building agents, a researcher tackling complex analysis, or a business seeking cutting-edge multimodal AI, this guide covers everything you need to know.

At CometAPI, we’ve been tracking DeepSeek’s evolution closely. As a unified AI API platform offering DeepSeek V3.2 and earlier models at up to 20% off official pricing with seamless OpenAI-compatible endpoints, we’re excited for V4’s integration. Later in this post, we’ll show how CometAPI can future-proof your workflows once V4 goes fully live.

What Is DeepSeek V4?

DeepSeek V4 represents the next evolution in the Chinese AI lab’s flagship V-series. Building on the success of DeepSeek-V3 and V3.2—which introduced hybrid thinking/non-thinking modes and strong agentic capabilities—V4 scales dramatically in size, intelligence, and versatility.

Industry analysts estimate V4 as a Mixture-of-Experts (MoE) model exceeding 1 trillion total parameters, with only ~37-40 billion active per token for efficiency. This architecture, refined from V3’s MoE foundation, activates specialized “experts” dynamically, slashing inference costs while boosting performance on coding, math, and long-context tasks.

Key differentiators include:

Native multimodal support (text + images + video).
Ultra-long context up to 1M tokens via Engram conditional memory.
Domestic hardware optimization—V4 is designed to run primarily on Huawei Ascend chips, reflecting China’s push for technological self-reliance.

DeepSeek has a track record of open-sourcing models under Apache 2.0, making V4 potentially one of the most accessible frontier models. Leaked benchmarks suggest it could hit 90% on HumanEval and 80%+ on SWE-bench Verified, putting it in direct competition with Claude Opus 4.5/4.6 and GPT-5 Codex variants. V4 is not a simple incremental update — it represents a full product-matrix redesign with tiered modes for different user needs, similar to Kimi’s Fast/Expert stratification but with added Vision.

Latest Updates Regarding DeepSeek V4

As of April 2026, DeepSeek V4 is in limited gray-scale testing rather than a full public launch. Multiple programmers and Weibo influencers shared screenshots of the updated chat interface on April 7-8, showing a dramatic overhaul from the previous dual-option (Deep Thinking R1 / Smart Search) layout.

The new UI introduces a prominent mode switcher with three options:

Fast Mode (default, unlimited daily use for casual tasks).
Expert Mode.
Vision Mode.

V4 will leverage Huawei’s latest silicon, with a full launch expected “in the next few weeks” from early April.

Fast Mode (also called Instant) is default and unlimited for daily use. Expert Mode emphasizes deep thinking and shows higher token throughput in some tests (~64 tokens/s vs. ~49 for Fast). Vision Mode enables direct image/video upload and analysis.

Some early testers report 1M context and updated knowledge cutoff (post-2025 data); others note Expert still feels like optimized V3.2 with 128K limits — confirming the gradual nature of gray-scale.

The company has remained silent on official naming, but the interface changes, multimodal hints, and alignment with earlier leaks (three-model suite on domestic chips) strongly indicate these are V4 variants in testing. Full launch is widely expected “this month” (April 2026).

What Is the New Functional Architecture of DeepSeek V4? (Quick Version vs. Expert Version Speculation)

Leaked details point to a sophisticated three-tiered architecture that separates everyday efficiency from high-stakes reasoning and multimodal processing.

Fast Mode (Quick Version): Optimized for instant responses and high-throughput daily dialogue. Analysts believe this routes to a lightweight distilled variant or a smaller active-parameter slice of the MoE model. It supports file uploads and basic tasks with minimal latency—perfect for quick queries or prototyping. Unlimited daily use makes it ideal for casual users.

Expert Mode (Deep Reasoning Version): Widely speculated to be the true “DeepSeek V4” core. It emphasizes multi-step reasoning, domain-specific enhancements, visualization of thought processes, and strengthened citation tracing. Insiders link it to the “new memory architecture” (Engram conditional memory) detailed in papers signed by DeepSeek’s leadership. Engram separates static knowledge (O(1) hash lookups) from dynamic reasoning, enabling stable 1M-token contexts without exploding compute costs. Early testers report superior logic stability and self-correction on complex problems.

Vision Mode: The multimodal flagship, capable of native image/video understanding and generation. Unlike traditional VLMs bolted onto text models, speculation suggests a “deep unified world model” architecture—potentially integrating visual tokens directly into the MoE routing for seamless cross-modal reasoning.

This Quick-vs-Expert split allows DeepSeek to serve both mass-market users (Fast) and power users (Expert/Vision) without compromising either experience. Full commercialization may introduce quotas on Expert/Vision while keeping Fast free/unlimited.

DeepSeek V4’s Visual and Expert Mode by Gray-Scale Test

The gray-scale exposure has been the biggest catalyst for excitement. I test:

Expert Mode triggers longer internal “thinking” (visible chain-of-thought in some views) and produces more accurate, cited outputs.
Vision Mode automatically engages when images are attached, redirecting prompts for analysis or generation.

These features align with DeepSeek’s published research on manifold-constrained hyper-connections (mHC) and DeepSeek Sparse Attention (DSA)—innovations that stabilize training at trillion-parameter scale and improve long-horizon agentic tasks.

Expert Mode may already be running an early V4 checkpoint, explaining the perceived intelligence jump. Vision Mode’s separation suggests it’s not a simple add-on but a core architectural pillar.

How to Access and Use DeepSeek V4 on the Web: Step-by-Step Guide

Accessing the gray-scale version is straightforward but currently limited:

Visit the official platform: Head to chat.deepseek.com or platform.deepseek.com and log in with your DeepSeek account (free signup available).
Look for the mode selector: If you’re in the gray-scale cohort, you’ll see the new Fast/Expert/Vision buttons. Not everyone has it yet—rollout is phased.
Select your mode:

Start with Fast Mode for everyday chats.
Switch to Expert Mode for complex reasoning, coding, or research.
Upload images/videos to trigger Vision Mode.

Prompt effectively: For Expert, use detailed instructions like “Think step-by-step and verify your logic.” For Vision, describe images precisely (e.g., “Analyze this chart for trends and generate a summary table”).
Monitor limits: Fast is unlimited; Expert and Vision may have daily quotas during testing.

Pro tip: Enable web search or file uploads where available for richer context.

If gray-scale access isn’t available yet, you can still use DeepSeek-V3.2 (the current production model) on the same site. Full V4 rollout is imminent—monitor CometAPI.

How to Integrate DeepSeek V4 into Your Workflow via API

While web access is great for exploration, production use demands reliable APIs. Official DeepSeek API currently serves V3.2 (128K context), but V4 endpoints are expected soon.

Enter CometAPI: As a one-stop AI API aggregator, CometAPI already delivers DeepSeek V3, V3.1, V3.2, and R1 models with OpenAI-compatible endpoints, 20% lower pricing, free starter credits, usage analytics, and automatic failover across providers. No code changes needed when V4 drops—we’ll add it seamlessly.

Quick setup on CometAPI:

Register at cometapi.com.
Generate an API key (sk-xxx).
Use base URL https://api.cometapi.com and model names like deepseek-v4-expert (once live).
Example Python call:

  from openai import OpenAI
  client = OpenAI(api_key="your_cometapi_key", base_url="https://api.cometapi.com")
  response = client.chat.completions.create(
      model="deepseek-v4-expert",  # or vision variant
      messages=[{"role": "user", "content": "Your prompt here"}]
  )

CometAPI’s playground lets you test V4 modes side-by-side with Claude or GPT without switching dashboards. For businesses, this means lower costs, predictable billing, and no vendor lock-in—ideal for scaling agentic workflows or multimodal apps.

Key Capabilities and Benchmarks of DeepSeek V4

Leaked data paints an impressive picture:

Coding: ~90% HumanEval, 80%+ SWE-bench Verified (projected to match or beat Claude Opus 4.6).
Reasoning: Enhanced MATH-500 (~96%) and long-context Needle-in-Haystack (97% at 1M tokens).
Multimodal: Native image/video understanding plus SVG/code generation far superior to V3.
Efficiency: MoE keeps costs low; Engram memory reduces VRAM needs by ~45% vs. dense models.

Real-world tests in Expert Mode show stronger self-correction and repository-level coding compared to V3.2.

How Does DeepSeek V4 Compare to Other Leading AI Models?

Feature	DeepSeek V4 (projected)	Claude Opus 4.6	GPT-5.4 Codex
Parameters (total/active)	~1T / ~37B	Undisclosed	Undisclosed
Context Window	1M tokens	200K-256K	~200K
Multimodal (native)	Yes (Vision Mode)	Yes	Yes
Coding (SWE-bench)	80%+	80.9%	~80%
Pricing (est. output)	Very low (open trajectory)	High	High
Open Weights	Likely	No	No

V4’s edge lies in cost-performance and open accessibility, making frontier AI available to smaller teams.

What Are Practical Use Cases for DeepSeek V4?

Software Development: Expert Mode for multi-file refactoring, bug detection, and full repo analysis.
Multimodal Analysis: Upload charts, diagrams, or videos for instant insights (Vision Mode).
Agentic Workflows: Long-context memory powers autonomous research agents.
Content & Design: Generate accurate SVG/code from descriptions; analyze visual data.
Education/Research: Step-by-step explanations with verifiable citations.

Why Choose CometAPI for DeepSeek V4 and Beyond?

For developers and enterprises, the web chat is a starting point—but scalable production requires robust infrastructure. CometAPI delivers exactly that: discounted DeepSeek access today (V3.2 at $0.22–$0.35/M tokens) and a clear migration path to V4. Features like prompt caching, analytics, and multi-model routing reduce costs by 20-30% while eliminating downtime risks. Whether you’re building the next AI agent or embedding vision capabilities, CometAPI ensures you’re ready the moment V4 API drops.

Conclusion

By offering frontier-level multimodal intelligence for free with tiered modes, DeepSeek is democratizing advanced AI while optimizing for domestic compute. This pressures Western labs on both performance and price, accelerating the entire industry toward more efficient, accessible models.

DeepSeek V4 isn’t just an upgrade—it’s a blueprint for efficient, accessible superintelligence. Start experimenting on the web today, and prepare your stack with CometAPI for seamless scaling tomorrow.

Claude Mythos Preview is coming: Can I use this top-of-the-line model now?

CometAPI03 — Thu, 09 Apr 2026 15:56:52 +0000

Claude Mythos Preview is Anthropic’s newest and most capable frontier AI model, representing a striking leap beyond previous Claude models like Opus 4.6. Announced on April 7, 2026, as part of Project Glasswing, it is a general-purpose language model with unprecedented strengths in agentic coding, complex reasoning, and especially cybersecurity tasks. Unlike earlier Claude releases available to the public via API or chat interfaces, Mythos Preview remains in a tightly gated research preview. It is not offered for general use due to its extraordinary ability to autonomously discover and chain high-severity vulnerabilities—including zero-days in major operating systems, web browsers, and foundational software.

For ordinary users using the Claude API, I recommend CometAPI. It aggregates the strongest models from different domains, including the Claude 4.6 series, and offers a pay-as-you-go pricing model, with API prices significantly lower than the official prices.

In this comprehensive guide, we break down exactly what Claude Mythos Preview is, its benchmark dominance in programming, reasoning, security, and AI R&D, how it identifies and exploits vulnerabilities through chain attacks, who can access it today, practical use cases for partners, and what ordinary users might (or might not) expect in the future.

What Is Claude Mythos Preview?

Claude Mythos Preview is Anthropic’s most advanced AI model to date—a new “Mythos” class that sits above the existing Opus tier in their lineup. It builds on the Claude family’s constitutional AI principles but delivers a qualitative “step change” in capabilities, particularly in autonomous agentic behaviors. Internally referenced during development (with early leaks mentioning “Capybara”), it excels at long-horizon tasks requiring deep code understanding, multi-step reasoning, and self-directed tool use.

Key differentiators include:

Agentic autonomy: It can run in isolated environments, hypothesize bugs, execute tests, debug, and output full proof-of-concept (PoC) exploits with minimal human guidance.
Scale and efficiency: Handles massive codebases, long contexts (up to millions of tokens via compaction), and complex chains of reasoning far beyond previous models.
Cybersecurity specialization (emergent, not fine-tuned): Downstream from superior coding and reasoning, it has already identified thousands of high-severity vulnerabilities across every major OS and browser.

Anthropic describes it as “the most cyber-capable model we have released,” saturating nearly all internal and known external evaluations. It is positioned not as a consumer chatbot but as a transformative tool for software security in the AI era.

Why Isn’t Claude Mythos Preview Publicly Released?

Anthropic made the deliberate decision not to release Claude Mythos Preview for general availability. The primary reason: its capabilities pose an unacceptable offensive cybersecurity risk if placed in the wrong hands. The model can autonomously discover zero-day vulnerabilities and develop sophisticated, chained exploits at a speed and scale that collapses the traditional “discovery-to-exploitation” window from months (or years) to minutes or hours.

Anthropic: “Claude Mythos Preview’s large increase in capabilities has led us to decide not to make it generally available. Instead, we are using it as part of a defensive cybersecurity program with a limited set of partners.”

Specific risks include:

Non-experts could generate working exploits overnight.
Autonomous end-to-end attacks on small-scale enterprise networks with weak postures.
Potential for proliferation to malicious actors, amplifying cybercrime costs (already estimated at ~$500 billion annually globally).

Instead of broad release, Anthropic launched Project Glasswing—a collaborative defensive initiative with Big Tech, cybersecurity firms, and open-source maintainers. The goal is to give defenders a head start by patching vulnerabilities before they are widely exploited. Anthropic has committed $100 million in usage credits and $4 million in donations to open-source security efforts.

This is the first time Anthropic has withheld a frontier model entirely from public access, underscoring the seriousness of the capability jump.

Claude Mythos Preview Benchmark Data Overview

Claude Mythos Preview demonstrates consistent, often dramatic improvements over Claude Opus 4.6 (and competitors like GPT-5.4 Pro or Gemini 3.1 Pro). Below are key benchmarks extracted from Anthropic’s System Card and Project Glasswing announcement. All scores use standardized harnesses with memorization filters applied where relevant.

Programming & Coding Skills

Mythos Preview sets new records in software engineering tasks requiring real-world code editing, debugging, and agentic workflows.

Benchmark	Claude Mythos Preview	Claude Opus 4.6	Improvement	Notes
SWE-bench Verified	93.9%	80.8%	+13.1%	500 problems; memorization-filtered
SWE-bench Pro	77.8%	53.4%	+24.4%	731 problems
SWE-bench Multilingual	87.3%	77.8%	+9.5%	297 problems
SWE-bench Multimodal	59.0%	27.1%	+31.9%	Internal harness
Terminal-Bench 2.0	82.0% (92.1% extended)	65.4%	+16.6%	Agentic terminal tasks

Claude Mythos Preview shows exceptional performance in coding benchmarks:

SWE-bench Pro: 77.8% (vs. 53.4% in Opus 4.6)
SWE-bench Verified: 93.9% (vs. 80.8%)
Terminal-Bench 2.0: 82.0% (vs. 65.4%)

These benchmarks measure real-world engineering tasks such as debugging, patching, and repository-level reasoning.

The results indicate that Mythos Preview is not just generating code—it is functioning as a software engineer.

Reasoning & Mathematical Skills

Massive gains in graduate-level and competition-grade problems.

Benchmark	Claude Mythos Preview	Claude Opus 4.6	Improvement	Notes
USAMO 2026	97.6%	42.3%	+55.3%	Proof-based; 6 problems
Humanity’s Last Exam (HLE, no tools)	56.8%	40.0%	+16.8%	2,500 questions
HLE (with tools)	64.7%	53.1%	+11.6%	Web/code tools
GPQA Diamond	94.6%	91.3%	+3.3%	Graduate-level science
GraphWalks BFS (long context)	80.0%	38.7%	+41.3%	256K–1M tokens

In reasoning benchmarks:

GPQA Diamond: 94.6%
Humanity’s Last Exam (with tools): 64.7%

These scores demonstrate strong performance in complex, multi-step reasoning tasks, particularly when external tools are involved.

Cybersecurity & Security Skills

The standout category. Mythos Preview saturates prior tests and excels at real vulnerability reproduction and exploitation.

Benchmark	Claude Mythos Preview	Claude Opus 4.6	Improvement	Notes
CyberGym	83.1% (0.83 pass@1)	66.6% (0.67)	+16.5%	1,507 targeted vuln tasks
Cybench	100% pass@1	Lower (not specified)	—	35 challenges
Firefox 147 Exploitation	Dramatically higher (reliable PoCs)	2/several hundred attempts	Qualitative leap	Proof-of-concept from crashes

The most important benchmark category is security:

CyberGym: 83.1% (vs. 66.6% in Opus 4.6)

This reflects the model’s ability to:

Identify vulnerabilities
Understand exploit mechanics
Reproduce real-world attack scenarios

This is the key reason the model is considered high-risk.

AI R&D Capabilities

Mythos Preview accelerates research tasks dramatically (e.g., 399.42× speedup on kernel optimization vs. Opus 4.6’s 190×). It also leads in multimodal agentic benchmarks like OSWorld (79.6% vs. 72.7%) and BrowseComp (86.9%, using 4.9× fewer tokens).

These numbers confirm Mythos Preview as the clearest “leap” in frontier AI history according to Anthropic.

How Claude Mythos Preview Works: Finding Vulnerabilities and Executing Chain Attacks

Mythos Preview’s cybersecurity prowess stems from its agentic coding loop rather than specialized training. In a typical workflow:

Launch in an isolated container with target source code.
Hypothesize potential bugs based on code review.
Execute, debug, and iterate using tools.
Output a ranked bug report + working PoC exploit.

Real-world examples:

27-year-old OpenBSD TCP SACK DoS (1998 RFC): Race condition + signed integer overflow leading to NULL pointer write. Found after ~1,000 runs (~$50 compute).
16-year-old FFmpeg H.264 out-of-bounds write (2003): Survived 5 million fuzzer runs and human review.
Linux kernel privilege escalation: Chains 2–4 vulns (out-of-bounds, use-after-free) → bypass KASLR → read kernel structs → heap spray → root.
Web browser sandbox escape: JIT heap spray chaining read/write primitives to escape renderer + OS sandboxes.
FreeBSD NFS RCE: 17-year-old stack overflow → 20-gadget ROP chain split across RPC packets → remote root without authentication.

It also excels at N-day exploits (given only a CVE + git hash) and black-box binary testing. Partners report it solved private cyber ranges end-to-end—tasks previously estimated at 10+ hours for human experts.

By using tool can track whether the neural features corresponding to concepts such as "cheating," "reward hacking," and "pain" are highlighted within the model, and can also translate the internal state into human language, similar to a mind-reading machine. Quantitative data shows that in 7.6% of rounds, the model internally recognizes that it is being evaluated. Most of the time, it chooses not to express it.

The original words of system card are: "Claude Mythos Preview is the best-aligned of any model that we have trained to date by essentially all available measures." and "when it does perform misaligned actions on rare occasions, these can be very concerning."

Use Cases for Claude Mythos Preview

Partners are deploying Mythos Preview for:

Proactive vulnerability scanning of first-party and open-source codebases.
Black-box binary analysis and endpoint hardening.
Penetration testing and red-team simulations.
Accelerated patch development for critical infrastructure (OS kernels, browsers, crypto libraries, etc.).
Daily-scale analysis (e.g., AWS reviewing 400 trillion network flows).

Open-source maintainers gain tools to fix bugs that survived decades of traditional testing. The net result: shorter disclosure-to-patch cycles and fewer exploitable flaws in production systems.

Who Can Access Claude Mythos Preview Now?

Access is strictly limited to Project Glasswing participants:

Launch partners: Amazon Web Services, Apple, Broadcom, Cisco, CrowdStrike, Google, JPMorganChase, Linux Foundation, Microsoft, NVIDIA, Palo Alto Networks.
Additional organizations: ~40 more responsible for critical software and open-source infrastructure.
Platforms: Claude API, Amazon Bedrock (US East), Google Cloud Vertex AI, Microsoft Foundry.
Pricing: Free $100M usage credits initially; afterward $25 per million input / $125 per million output tokens.
OSS route: Maintainers can apply via Claude for Open Source program.

Security professionals may later apply to a Cyber Verification Program. General public and ordinary users have no access at launch.

What Can Ordinary Users Use It For?

Currently, nothing—Claude Mythos Preview is unavailable to individual users, developers, or businesses outside the gated program. Anthropic plans to incorporate safer derivatives of its capabilities into future public Claude models (e.g., next Opus releases) with enhanced safeguards. For now, ordinary users continue using Claude 4 family models for coding, reasoning, and general tasks while the industry leverages Mythos Preview defensively.Claude Opus 4.6 as the most intelligent broadly available model for agents and coding, and Claude Sonnet 4.6 as the best combination of speed and intelligence.

For everyday work, that means Mythos Preview is best understood as a signal of where Claude’s capabilities are heading, not as a tool most people can try right now. For ordinary users, the actionable applications remain the familiar ones: coding help, reasoning support, research assistance, document analysis, and workflow automation through public Claude products. The difference is that Mythos Preview shows how far the underlying model family can go when Anthropic allows it to operate in a restricted, security-focused setting.

Claude Opus 4.6 and Sonnet 4.6 APIs are available on CometAPI at a 20% discount.

Comparison table: Claude Mythos Preview vs. Opus 4.6

Benchmark / capability	Claude Mythos Preview	Claude Opus 4.6	Why it matters
SWE-bench Pro	77.8%	53.4%	Stronger agentic coding
Terminal-Bench 2.0	82.0%	65.4%	Better terminal and tool execution
SWE-bench Multimodal	59.0%	27.1%	Better mixed text/code/image workflows
SWE-bench Multilingual	87.3%	77.8%	Better cross-language coding
SWE-bench Verified	93.9%	80.8%	Stronger software repair performance
GPQA Diamond	94.6%	91.3%	Slightly stronger reasoning
Humanity’s Last Exam, no tools	56.8%	40.0%	Better hard reasoning under constraint
Humanity’s Last Exam, with tools	64.7%	53.1%	Better tool-augmented reasoning
BrowseComp	86.9%	83.7%	Better agentic search
OSWorld-Verified	79.6%	72.7%	Better computer-use tasks
CyberGym	83.1%	66.6%	Much stronger security-vulnerability reproduction
OSS-Fuzz-style testing	10 tier-5 hijacks	1 tier-3 result in the cited comparison	Larger exploit capability leap

Conclusion

Claude Mythos Preview is not just another incremental model—it is a paradigm-shifting system that redefines what AI can achieve in cybersecurity while raising profound questions about safe deployment. By keeping it gated and channeling its power into Project Glasswing, Anthropic has taken a principled stand: the most powerful tools should first protect the systems we all rely on. For the moment, Mythos Preview belongs to a small circle of vetted defenders; for everyone else, it is a preview of the next phase of AI capability.

You can use the Claude API in CometAPI to prepare for the arrival of Claude Mythos. Ready?

Claude Mythos(Opus 5) Leaked: What happened and What to expect

CometAPI03 — Fri, 03 Apr 2026 15:19:24 +0000

As of March 29, 2026, the “Claude Mythos” story is less about a finished public launch and more about a leaked preview of what looks like Anthropic’s next big step. Thecompany accidentally exposed draft blog content in a publicly searchable data cache, revealing an unreleased model that Anthropic described as a “step change” and “the most capable we’ve built to date.” Anthropic confirmed it is developing and testing the model with a small group of early access customers.

That matters because Anthropic’s current public model lineup still centers on Claude Opus 4.6, Claude Sonnet 4.6, and Claude Haiku 4.5. In other words, the leak is not a confirmed public product launch; it is a leaked glimpse of the next tier Anthropic may be preparing.

Currently, CometAPI already provides APIs for cutting-edge Claude models, such as Claude Opus 4.6 and Claude Sonnet 4.6. Once Claude Mythos is available on CometAPI, you can perform comparative tests against top models from Gemini and OpenAI. CometAPI aggregates the best models.

What Is Claude Mythos?

Claude Mythos is Anthropic’s most advanced AI model to date, described in leaked internal documents as “by far the most powerful AI model we’ve ever developed.” It introduces a new performance tier—internally referred to as “Capybara”—that sits above the company’s existing Opus lineup, which until now represented the pinnacle of Claude’s capabilities.

Anthropic’s current model family follows a clear hierarchy:

Opus: Largest, most capable, and most expensive (e.g., Claude Opus 4.6 and the earlier Opus 4.5 released in November 2025).
Sonnet: Balanced speed and intelligence.
Haiku: Fastest and most cost-effective for lightweight tasks.

Mythos/Capybara breaks this mold as a significantly larger, more compute-intensive model. Draft blog posts explicitly state it is “larger and more intelligent than our Opus models—which were, until now, our most powerful.” The name “Mythos” was chosen to evoke “the deep connective tissues that link together knowledge and ideas,” signaling deeper, more integrated reasoning across domains.

This is not a minor incremental update. Anthropic’s spokesperson confirmed that the company is “developing a general purpose model with meaningful advances in reasoning, coding, and cybersecurity” and considers it “a step change and the most capable we’ve built to date.” Training is complete, and the model is already undergoing real-world testing with a small group of early-access customers.

For context, Claude’s evolution has been rapid. Claude 3 Opus (2024) set early benchmarks, followed by Claude 3.5 Sonnet, Claude 4 variants, and Opus 4.5/4.6 in 2025. Mythos appears to be the logical successor—potentially what the community has speculated as “Opus 5”—pushing frontier AI into new territory while raising serious safety questions.

How Was Claude Mythos Leaked?

The leak occurred on or around March 27, 2026, due to a straightforward but embarrassing human-error misconfiguration in Anthropic’s content management system (CMS). Nearly 3,000 unpublished assets—including draft blog posts, images, PDFs, audio files, and even internal documents—were left in a publicly searchable data store (sometimes called a “data lake”).

Assets were set to “public” by default, with guessable URLs. Security researchers Roy Paz (LayerX Security) and Alexandre Pauwels (University of Cambridge) discovered the cache and alerted media outlets.

Leaked materials included:

Two near-identical draft blog posts (one titled for “Claude Mythos,” the other “Claude Capybara”).
Structured web-page data with headings and a planned publication date.
Unused marketing assets from past launches.
An internal PDF about an invite-only CEO retreat hosted by Anthropic CEO Dario Amodei.

Anthropic quickly confirmed the incident as “human error” in CMS configuration and removed public access. No evidence suggests malicious intent or a breach of model weights—only marketing and planning documents were exposed.

This event highlights a growing vulnerability in the AI industry: rapid iteration and internal documentation often outpace secure publishing workflows. Similar leaks have occurred at other labs, but this one provided unusually detailed insight into an unreleased flagship model.

Leaked Benchmark Scores and Performance Claims

Exact numerical scores were not disclosed in the leaked drafts—Anthropic has not published official benchmarks yet. However, the language is unambiguous and consistent across both draft versions:

“Compared to our previous best model, Claude Opus 4.6, Capybara gets dramatically higher scores on tests of software coding, academic reasoning, and cybersecurity, among others.”

The model is further described as “currently far ahead of any other AI model in cyber capabilities” and one that “presages an upcoming wave of models that can exploit vulnerabilities in ways that far outpace the efforts of defenders.”

What do these benchmark categories actually measure?

Software Coding (e.g., SWE-Bench Verified, HumanEval, LiveCodeBench): Real-world software engineering tasks, including bug fixing, feature implementation, and repository-level understanding. Opus 4.6 already led in many coding leaderboards; a “dramatic” jump here would mean Mythos could autonomously handle complex, multi-file codebases that currently require senior engineers.
Academic Reasoning (e.g., GPQA, MMLU-Pro, MATH, FrontierMath): Graduate-level science, math, and multi-step logical problems. Improvements here signal stronger chain-of-thought reasoning and knowledge synthesis.
Cybersecurity: Vulnerability discovery, exploit generation, red-teaming simulations, and defensive hardening. This is the most emphasized area—and the most concerning.

While prior Claude models (Opus 4.5/4.6) achieved strong results—e.g., Opus 4.5 scored ~80.9% on SWE-Bench Verified—the leaked claims position Mythos in a qualitatively different league.

Model Characteristics and Technical Profile

Beyond benchmarks, the drafts reveal several defining traits:

Scale and Cost: “Very expensive for us to serve, and will be very expensive for our customers to use.” This implies a massive parameter count and high inference costs, limiting initial availability to enterprise and high-value use cases.
Reasoning Depth: Emphasis on “deep connective tissues” between knowledge domains suggests superior long-context understanding and cross-domain synthesis.
Agentic Capabilities: Early access appears targeted at organizations needing advanced coding agents and cybersecurity tools.
Safety-First Philosophy: Consistent with Anthropic’s constitutional AI approach, the company is prioritizing risk assessment—especially in cybersecurity—before broader release.

Cybersecurity Implications: The Biggest Red Flag

The most striking element of the leak is Anthropic’s own warning about the model’s dual-use potential. By being “far ahead” in cyber capabilities, Mythos could:

Autonomously discover zero-day vulnerabilities.
Generate sophisticated exploit code at scale.
Simulate advanced persistent threats (APTs) faster than human defenders can respond.

The draft explicitly states the company wants to “act with extra caution” and share findings with cyber defenders to prepare for “an impending wave of AI-driven exploits.”

Market reaction was immediate: cybersecurity stocks plunged on March 27-28, 2026, as investors priced in the risk that offensive AI capabilities could outpace defensive tools.

This aligns with broader industry trends. OpenAI has similarly flagged high cyber capabilities in models like GPT-5.3-Codex. Real-world incidents already show state actors (e.g., a Chinese group) using Claude variants for infiltration campaigns. Mythos would supercharge such threats.

Positive side: Early access to defensive organizations could accelerate secure coding practices, automated patching, and threat hunting—potentially making the internet safer in the long term.

Comparison Table: Claude Mythos vs. Previous Models

Aspect	Claude Opus 4.6 (Current Flagship)	Claude Mythos / Capybara (Leaked)	Key Takeaway
Tier	Opus	New “Capybara” tier (above Opus)	Major architecture leap
Coding Performance	Strong (e.g., ~80.9% SWE-Bench)	Dramatically higher	Potential to rival or exceed senior engineer productivity
Academic Reasoning	Excellent	Dramatically higher	Deeper multi-step logic and knowledge integration
Cybersecurity	Capable (vulnerability detection)	Far ahead of any current model	Qualitative leap; raises dual-use risks
Inference Cost	High (Opus pricing)	Very expensive (even higher)	Enterprise-only initially
Release Status	Generally available	Early-access testing only	Deliberate, safety-focused rollout
Overall Capability	State-of-the-art 2025	“Step change” / “Most powerful ever”	New frontier benchmark

Conclusion: A Leaked Glimpse into the Next AI Era

The Claude Mythos leak offers a rare, unfiltered look at Anthropic’s roadmap. It confirms the company has achieved a genuine “step change” in core capabilities while simultaneously acknowledging the profound risks—particularly in cybersecurity—that come with such power. Whether labeled Opus 5 or a new Capybara tier, Mythos signals that frontier AI is entering a phase where capabilities outpace safe deployment timelines.

How to Get Grok Imagine for Free: Access, Pricing, and Alternatives

CometAPI03 — Thu, 26 Mar 2026 15:59:58 +0000

Grok Imagine Video is not free on official xAI/Grok platforms as of March 2026 (free tier removed due to high demand and misuse concerns), but you can access it affordably — or with free starter credits — via third-party aggregators like CometAPI. CometAPI offers the model at just $0.04 per second (480p), with new users often receiving $1–$5 in free credits upon signup.

This guide shows you exactly how to generate high-quality text-to-video or image-to-video clips (up to 15 seconds with native audio) for pennies or even free initially, plus full API tutorials and comparisons to Sora 2.

Grok Imagine Video, launched by xAI on January 28, 2026, has quickly become one of the most talked-about AI video tools. It delivers photorealistic 720p videos with synchronized native audio, strong prompt adherence, and creative controls that rival or surpass OpenAI’s Sora 2 in speed and style flexibility.

What Is Grok Imagine Video?

Grok Imagine Video is xAI’s flagship text-to-video and image-to-video generation model (model ID: grok-imagine-video), powered by the proprietary Aurora engine. It creates short cinematic clips (1–15 seconds) directly from natural language prompts, uploaded images, or existing video references. Key capabilities include:

Native audio generation: Synchronized sound effects, ambient music, character speech, and lip-sync — no post-production needed.
Advanced editing: Animate still images, extend clips, remove/replace objects, restyle scenes, or apply “Spicy,” “Fun,” or “Normal” modes.
Output specs: Up to 720p resolution, customizable aspect ratios (16:9, 9:16, 1:1), durations 1–15 seconds.
Best-in-class features: Exceptional motion consistency, prompt following (including iterative refinements), and photorealistic or stylized outputs (realistic, sci-fi, fantasy).

Following the January 28 API launch, xAI rolled out video extension (continue any frame), multi-image animation (up to 7 references), and improved audio in February–March updates. However, free access on grok.com/imagine and the X app was heavily restricted or eliminated for non-subscribers around mid-March due to deepfake concerns and server load. Official Grok users now report “paywall” prompts even for single generations on free accounts.

Real-world performance data (from independent benchmarks and xAI announcements):

Generation speed: 10–17 seconds for 10-second clips (2–4× faster than many competitors).
Quality rankings: Often tops charts for motion stability and audio sync versus Veo 3.1 or Kling 2.5.
Use cases: Short social media ads, cinematic storyboards, product demos, educational animations, and creative experiments.

Is Grok Imagine Video free? Latest 2026 Access Reality

Whether it is free depends on the platform you use. If you are using xAI's official channels, it is no longer fully free. However, if you look to third-party integration platforms—such as CometAPI—free usage quotas are still available.

Early 2025–early 2026: Limited free generations (3–10 images/videos per day or rolling 2-hour windows) were available to all X users and grok.com visitors.
March 2026 update: Free tier effectively removed for video (and often image) generation. Users now see immediate upgrade prompts. Free/logged-in accounts get 0–very limited daily attempts; full access requires X Premium (~$8–$16/mo), Premium+ (~$40/mo), or SuperGrok (~$30/mo).

Good news: You can still access near-free or low-cost usage through API aggregators like CometAPI, which proxy the official model at discounted rates (up to 20% off) and often include signup bonuses (up to 5$).

How Much Does Grok Imagine Video Cost Officially?

Official xAI Grok Imagine API (via x.ai/api/imagine or console.x.ai):

Priced at $4.20 per minute of generated video (including audio) — roughly $0.07 per second.
Additional costs for high-res or batch processing.
Requires xAI API key and billing setup; no generous free credits for video.

Subscription route (Grok app/X):

X Premium: Limited quotas (~20–50 videos/24h depending on tier).
SuperGrok: Higher limits but still rate-limited during peaks.

How to Get Grok Imagine Video for Free (or Almost Free) in 2026

The most reliable “free” path is CometAPI — a unified AI API platform that aggregates 500+ models (including official xAI endpoints) at 20–40% lower prices than direct vendors. New users frequently receive $1–$5 free credits after signup .

Why CometAPI wins for free/cheap access:

Category	Item	Price
Input Pricing	Text	N/A (Free)
	Image	$0.0016
	Video per second	$0.008
Output Pricing	480p	$0.04
(Per second by resolution)	720p	$0.056

Grok Imagine Video pricing: $0.04/second (480p) or $0.056/second (720p) — up to 43% cheaper than official.
Sora 2 alternative: Only $0.08/second.
OpenAI-compatible SDK → one API key for everything.
Async processing, usage analytics, and no vendor lock-in.
CometAPI is the most stable and developer-friendly

Alternative Solutions in CometAPI: Sora 2 and Other Video Models

CometAPI’s current video-generation alternatives to Grok Imagine Video include Sora 2, Sora 2 Pro, Veo 3 Fast, and Veo 3.1 Pro . CometAPI lists Grok Imagine Video at $0.04/sec, Sora 2 at $0.08/sec, Sora 2 Pro at $0.24/sec, and Veo 3.1 Pro at $2 per request. CometAPI lets you switch models instantly without new keys. Here’s how Grok Imagine Video stacks up:

Feature	Grok Imagine Video (xAI)	Sora 2 (OpenAI via CometAPI)	Veo 3.1 Pro (Google)	Kling 2.5 / Hailuo AI
Price per second	$0.04 (480p) / $0.056 (720p)	$0.08 / $0.24 (Pro)	~$2 per request	Varies (~$0.05–$0.10)
Max Duration	15 seconds	Up to 20+ seconds	8–10 seconds	4–10 seconds
Native Audio	Yes (lip-sync, effects, speech)	Yes	Limited	Yes
Image-to-Video	Excellent	Strong	Very good	Good
Editing Capabilities	Full (extend, restyle, object swap)	Moderate	Strong	Lip-sync focused
Speed	10–17 seconds	60–120+ seconds	Fast	Fast
Best For	Creative control, audio sync, speed	Cinematic realism	Photorealism	Effects & motion
Content Policy	“Spicy” mode available (moderated)	Strict	Strict	Varies

A simple rule of thumb is this: choose Grok Imagine Video when you want fast, lower-cost iteration and integrated editing; choose Sora 2/ veo 3.1 when you need stronger audio coupling and cinematic realism; choose Sora 2 Pro when quality is worth the premium.

How to Use Grok Imagine Video API Free on CometAPI (Step-by-Step Tutorial)

Step 1: Sign up & claim free credits

Go to cometapi.com, start by creating a CometAPI account and requesting the trial credit described in its documentation. New users currently receive $1 in trial credits after registration and a request to product@cometapi.com— enough for 20–30 seconds of 480p video.

Step 2: Choose your endpoint

Base URL: https://api.cometapi.com/v1 (or specific Grok routes). Use OpenAI-compatible client or raw HTTP.

Step 3: Generate your first video (Python example)

import requests
import time

API_KEY = "your_cometapi_key"
BASE_URL = "https://api.cometapi.com/grok/v1"  # or unified endpoint

headers = {
    "Authorization": f"Bearer {API_KEY}",
    "Content-Type": "application/json"
}

payload = {
    "model": "grok-imagine-video",
    "prompt": "A futuristic cyberpunk city at night with flying cars and neon rain, cinematic lighting",
    "duration": 10,
    "resolution": "720p",
    "aspect_ratio": "16:9"
}

# Create generation task
response = requests.post(f"{BASE_URL}/videos/generations", headers=headers, json=payload)
task_id = response.json().get("request_id")

# Poll for result
while True:
    status = requests.get(f"{BASE_URL}/videos/{task_id}", headers=headers).json()
    if status.get("data", {}).get("status") == "SUCCESS":
        video_url = status["data"]["data"]["video"]["url"]
        print("✅ Video ready:", video_url)
        break
    time.sleep(10)

Download the ephemeral MP4 immediately. Cost for this 10s 720p clip: ~$0.56.

Image-to-Video example: Add "image": "https://your-image-url.jpg" or base64.

Step 4: Monitor usage & scale

CometAPI dashboard shows real-time costs, success rates, and analytics. Set budgets to avoid surprises.

Advanced parameters: Add style: "cinematic", custom modes, or editing endpoints for refinements.

Pro tip: Start with 480p for testing to maximize free credits. Once credits are used, top-up is cheap and instant.

Option: PlayGround

After registering and logging in, simply enter "prompt" and a reference image in PlayGround to output the video.

Advanced Use Cases, Best Practices & Limitations

Use cases with data:

Marketing: 80% faster content creation vs traditional editing (user reports).
Education: Animate historical events or scientific processes.
Filmmaking: Storyboard prototypes before full production.

Best practices:

Use specific, layered prompts (subject + action + style + lighting + camera movement).
Leverage image references for consistency across clips.
Test “Spicy” mode responsibly (age-verified, moderated).

Limitations (March 2026 data):

Max 15s per clip (extend via API for longer sequences).
Ephemeral output URLs (download fast).
Content moderation blocks illegal/harmful prompts.
Rate limits during peak hours on aggregator platforms.

Ethical note: Always respect copyright, consent, and platform policies. xAI and CometAPI enforce strict guidelines.

Comparison Table: Official vs CometAPI vs Other Platforms

Platform	Grok Imagine Video Cost	Free Credits?	Ease of Use	Best For
Official xAI API	$0.07/sec	Limited	API only	Heavy enterprise
CometAPI	$0.04–$0.056/sec	Yes ($1+)	Excellent	Developers & cost savings
Grok App/X (paid)	Subscription-based	No (post-Mar)	UI only	Casual users

Conclusion: Start Generating Grok Imagine Videos Today

Grok Imagine Video represents a massive leap in accessible AI creativity, but official free access has ended. CometAPI solves this perfectly: lower prices, unified access, Sora 2 alternatives, and free starter credits make professional-grade video generation realistic for everyone — from hobbyists to agencies.

Action steps:

Visit CometAPI → sign up → claim credits.
Run the Python example/ playground above.
Experiment and scale.

With 2026’s rapid AI evolution, tools like this democratize filmmaking. Bookmark this guide, share your creations, and stay updated — CometAPI continue shipping improvements daily.

How to Use MiMo V2 API for Free in 2026: Complete Guide

CometAPI03 — Thu, 26 Mar 2026 15:55:23 +0000

To use MiMo V2 API for free, get free quota via CometAPI or self-host the open-source weights on Hugging Face. For Pro and Omni, leverage OpenRouter routing, CometAPI aggregation, or Puter.js user-pays proxies. All models use a standard OpenAI-compatible endpoint. Official Xiaomi pricing starts at $1/$3 per million tokens for Pro (cheaper than Claude Opus 4.6), but free tiers and aggregators make high-performance agentic AI accessible without upfront costs.

Xiaomi stunned the AI world in mid-March 2026 with the launch of its MiMo-V2 series—three powerful large language models engineered for the “agentic era.” Released around March 18–21, 2026, the lineup includes the flagship MiMo-V2-Pro, the multimodal MiMo-V2-Omni, and the efficient open-source MiMo-V2-Flash. These models have quickly climbed global leaderboards, with MiMo-V2-Pro ranking 8th worldwide (and 2nd among Chinese models) on the Artificial Analysis Intelligence Index while delivering performance that rivals or approaches Claude Opus 4.6 and GPT-5.2 at a fraction of the cost.

The MIMO V2 series, including MImo-v2 pro, mimo-V2-omni, and mimo-v2-flash, are now accessible via CometAPI.

What Exactly Is MiMo V2 and Why Is It Generating Buzz in 2026?

MiMo V2 is Xiaomi’s new AI family built around agentic workloads rather than simple chat. The lineup now includes MiMo-V2-Flash, MiMo-V2-Pro, MiMo-V2-Omni, and MiMo-V2-TTS. Released March 18–19, 2026, it includes three specialized models that work together as a complete platform: a reasoning “brain” (MiMo-V2-Pro), multimodal “senses” (MiMo-V2-Omni), and speech synthesis (MiMo-V2-TTS, not covered in depth here).

Unlike traditional chat models, MiMo V2 prioritizes agentic workflows—long-horizon planning, tool use, multi-step reasoning, and real-world interaction (e.g., browser control, code execution, robotics perception).

The buzz stems from performance-to-price leadership. Xiaomi claims MiMo-V2-Pro matches or exceeds Claude Opus 4.6 in agentic benchmarks while costing 60–80 % less. Early adoption data from OpenRouter shows Hunter Alpha (an internal test build of Pro) topping daily call volumes and surpassing 1 trillion tokens processed within days of its quiet debut.

MiMo-V2-Pro is being paired with major agent frameworks to offer one week of free API access for developers worldwide. In other words, this is not a closed, invite-only launch; Xiaomi is clearly trying to seed an ecosystem around MiMo V2 fast.

What Are the Standout Features and Advantages of MiMo V2?

MiMo-V2-Pro is a ~1-trillion-parameter model (42 billion active parameters via Mixture-of-Experts routing), making it roughly three times larger than MiMo-V2-Flash in effective scale. It employs a Hybrid Attention mechanism (7:1 sliding-window-to-global ratio) and a lightweight Multi-Token Prediction (MTP) layer that triples generation speed through self-speculative decoding. The result: a 1-million-token context window capable of ingesting entire codebases, long documents, or hours of video transcripts in one pass.

MiMo-V2-Omni extends this with native omni-modal fusion—image, video, and audio encoders share a single backbone, enabling simultaneous perception and anticipatory reasoning (predicting future events from current inputs). MiMo-V2-Flash, the lightweight sibling, uses a 5:1 hybrid attention design, 309 billion total / 15 billion active parameters, and supports 256K context while remaining fully open-source under the MIT license.

Key Features (Shared and Variant-Specific)

Massive Context: 1M tokens (Pro) or 256K (Flash/Omni) with near-perfect Needle-in-a-Haystack retrieval (99.9 % at 64K for Flash).
Hybrid Thinking & Tool Use: Toggleable reasoning mode returns reasoning_content and tool_calls; native structured output for agents.
Agentic Optimization: Fine-tuned via Multi-Teacher On-Policy Distillation and large-scale RL on 100,000+ code and tool-use tasks.
Efficiency: FP8 inference, MTP speculative decoding, and aggressive KV-cache compression reduce costs and latency.
Multimodal (Omni only): Unified processing of 1080p video, >10-hour audio, and cross-modal resonance without separate adapters.
Open Ecosystem: MIT license for Flash weights on Hugging Face; seamless integration with OpenClaw, KiloCode, Blackbox, Cline, and OpenCode frameworks.

Proven Advantages (Backed by Data)

Performance: MiMo-V2-Pro scores 61.5 on ClawEval (#3 globally), 81.0 on PinchBench, and 71.7 on SWE-Bench Verified—competitive with Claude Opus 4.6 yet cheaper. Flash leads all open-source models on SWE-Bench Multilingual (71.7) and AIME 2025 math (94.1 %). Omni excels in MMAU-Pro audio (76.8) and OmniGAIA multimodal agent tasks (54.8).
Cost Efficiency: Pro input/output pricing is ~70 % lower than Claude equivalents; Flash is effectively free on OpenRouter.
Stability & Reliability: 100 % uptime reported on OpenRouter routing to Xiaomi’s CN infrastructure; improved tool-call accuracy after post-launch iterations.
Developer Velocity: One-query frontend generation, end-to-end agent flows, and self-hosting options accelerate prototyping from days to hours.
Accessibility: Public API launch with one-week free credits via partner frameworks and free Flash tier democratize frontier AI.

These advantages position MiMo V2 as the go-to for cost-sensitive, high-stakes agent development in 2026.

How to Access MiMo V2 API (Free & Paid Options)

All models use OpenAI-compatible endpoints, so you can swap base URLs and model names with minimal code changes.

1. Hugging Face (Best for Free Self-Hosting of Flash)

MiMo-V2-Flash weights: XiaomiMiMo/MiMo-V2-Flash.
Steps for Free Local Use:
1. Install transformers + vllm or llama.cpp for quantization.
2. Download weights (309B MoE quantizes well to 4-bit).
3. Run inference server: vllm serve --model XiaomiMiMo/MiMo-V2-Flash --tensor-parallel-size 4 (needs ~80–128GB VRAM for full; lower with quant).
Free Tier on HF Inference Endpoints: Pay-per-use GPU hours (~$0.50/GPU-hour), but Flash is the only open weights model.
Limitations: Hardware cost; Pro/Omni unavailable (closed).

Pro Tip: Use for offline agents or cost-free prototyping.

2. OpenRouter (Easiest Free/Paid Routing)

OpenRouter provides normalized OpenAI-compatible endpoints with intelligent routing and fallbacks.

MiMo-V2-Flash:free – Completely free (rate-limited but generous for development).
MiMo-V2-Pro & Omni – Paid but among the cheapest frontier options; 100 % uptime, sub-6-second latency.

Step-by-step:

Sign up at openrouter.ai (free $1 credit).
Generate API key.
Use model IDs: xiaomi/mimo-v2-flash:free, xiaomi/mimo-v2-pro, or xiaomi/mimo-v2-omni. Example Python code (using OpenAI SDK):

from openai import OpenAI
client = OpenAI(base_url="https://openrouter.ai/api/v1", api_key="your_key")
response = client.chat.completions.create(
    model="xiaomi/mimo-v2-flash:free",
    messages=[{"role": "user", "content": "Explain hybrid attention in MiMo-V2"}]
)

Enable reasoning with reasoning={"enabled": True} for step-by-step traces.

Limitation：However, a hidden problem has been widely reported: OpenRouter's MIMO v2 generation is unstable and frequently fails, yet developers are still forced to pay the bills. In addition, OpenRouter's model pricing is 25% higher than CometAPI.

3. CometAPI (Robust Aggregator for Unified Access)

CometAPI is a commercial OpenAI-style aggregator supporting hundreds of models, including Xiaomi’s MiMo V2 lineup via unified endpoints.

Steps:
1. Sign up at api.cometapi.com → Generate key.
2. Base URL: https://api.cometapi.com/v1
3. Model names: xiaomi/mimo-v2-pro, xiaomi/mimo-v2-omni, xiaomi/mimo-v2-flash.
Free/Paid: No dedicated free tier for Pro/Omni, but competitive pay-as-you-go (often 10–20% below direct via volume discounts). Flash mirrors OpenRouter free routing.

Why Choose CometAPI? Excellent developer tools, multimodal support, and reliability for production. Automatic provider routing, cache support, usage analytics. Pro/Omni often cheaper via aggregated providers.

Bonus Free Method:

Puter.js SDK routes MiMo V2 (including Pro/Omni) with a user-pays model—your app stays free while users cover tokens.

Official Xiaomi Platform (platform.xiaomimimo.com): Direct access with first-week free beta (now expired for most) and tiered pricing. Ideal for high-volume or cache-heavy use.

Comparison of MiMo V2 Solutions: CometAPI vs Hugging Face vs OpenRouter

Criteria	CometAPI	Hugging Face	OpenRouter
Pricing (Flash/Pro/Omni)	Competitive pay-as-you-go (~10–20% discounts)	Free (self-host Flash) / GPU-hour paid	Flash:free; Pro ~$0.23/$2.32 effective; Omni $0.40/$2
Stability / Uptime	High (enterprise-grade routing)	Hardware-dependent	Excellent (provider fallbacks, 89–100% cache hit)
Ease of Use	Unified dashboard, OpenAI compat	Requires infra setup	One-line swap, analytics
Free Access	free quoto but all api price lower(25%)	Full Flash weights free	:free Flash + beta credits
Multimodal Support	Full (images/audio via Omni)	Flash only (text)	Full (routes Omni natively)
Best For	Production apps needing reliability	Local/offline experimentation	Quick prototyping & cost optimization
Rate Limits	Generous volume tiers	None (self-host)	20 RPM free; scalable paid
Data Support	Strong logging & monitoring	Full control	Leaderboards & real-time pricing

Verdict (2026 Data): OpenRouter wins for most developers (free Flash + cheap Pro). CometAPI for enterprise stability. Hugging Face for zero ongoing token cost on Flash.

My practical verdict

If you want the lowest-friction free trial, start with Xiaomi’s one-week partner access or CometAPI’s trial credits. If you want the most reliable hosted API experience, use CometAPI. If you want the most control and the lowest long-term marginal cost, download the Hugging Face weights and self-host. For most developers, the smartest path is to prototype on CometAPI, then migrate the highest-volume workload to Hugging Face or a dedicated deployment once the usage pattern is clear.

What are the best practices for using MiMo V2 well?

Match the model to the job

Use Flash for coding, reasoning, and fast agent loops. Use Pro for long-horizon orchestration, large context, and task completion. Use Omni for screen understanding, audio, video, and any workflow where perception is part of the task. Xiaomi’s own positioning makes that split very explicit, and it is the easiest way to avoid paying Pro prices for a Flash-sized job, or using Flash where multimodal perception is really needed.

Keep prompts structured and tool-oriented

MiMo V2 is built for agents, so it tends to work best with highly structured instructions, clear tool definitions, and explicit success criteria. That is especially true for Omni and Pro, which are both described as supporting structured tool calling and function execution. In practice, you get better outcomes when you tell the model what to do, what to avoid, what the output format should be, and what counts as a completed task.

Control cost before it controls you

Long context is powerful, but it is easy to burn through tokens quickly if you stream too much conversation history into every call. MiMo-V2-Pro’s 1M-token window is impressive, but the useful question is not “can it fit?” It is “should it fit?” For most apps, trimming the prompt, using retrieval wisely, and reserving Pro for the hardest steps will save more money than any small provider price difference. The published rates make this especially relevant: Flash is dramatically cheaper

Final Takeaway

IXiaomi’s MiMo V2 delivers frontier agentic performance at disruptive prices—often free via Flash or aggregators. Whether you self-host on Hugging Face, route via CometAPI, you now have a complete playbook to build production agents without breaking the bank.If you later need a more stable production setup, Hugging Face’s dedicated endpoints and CometAPI’s provider failover are the two public stories that make the strongest case.

MiMo V2 is not just another open model release. It is a three-part stack for agentic AI: Flash for efficient reasoning, Pro for heavyweight orchestration, and Omni for multimodal perception and action.

Start Today: Grab a free CometAPI key and test mimo-v2-pro. Upgrade to Pro for mission-critical work. The agent era is here—and Xiaomi made it affordable.

What is Seedance 2.0? A Comprehensive Analysis

CometAPI03 — Tue, 24 Mar 2026 15:42:18 +0000

Seedance 2.0 is ByteDance’s next-generation AI video generation model, officially launched in March, 2026. It supports text, image, audio, and video inputs, can use up to 9 images, 3 video clips, and 3 audio clips as references, and is designed for director-level control, motion stability, and audio-video joint generation. In Artificial Analysis’ current blind-vote leaderboards, Seedance 2.0 leads both text-to-video and image-to-video categories without audio, with Elo scores of 1269 and 1351 respectively.

What Is Seedance 2.0?

Seedance 2.0 is ByteDance Seed’s new-generation video creation model. Officially, it is built on a unified multimodal audio-video joint generation architecture that accepts text, image, audio, and video inputs, and it is positioned as a creator tool with unusually broad reference and editing capabilities. Seedance 2.0 was designed for industrial-grade content workflows, with stronger physical accuracy, realism, controllability, and stability in complex motion scenes than the prior 1.5 release. Unlike earlier models that focused primarily on text-to-video, Seedance 2.0 introduces a fully unified multimodal generation pipeline, enabling:

Text-to-video generation
Image-to-video animation
Video-to-video editing
Audio-synchronized output

This makes it one of the most comprehensive AI video creation platforms available in 2026.

Why does that matter?

Most video generators are still optimized for a relatively narrow workflow: prompt in, clip out. Seedance 2.0 goes further by treating video generation more like a director’s workspace. According to ByteDance, it can use multiple reference types at once, preserve subject consistency, follow detailed instructions more faithfully, and even plan camera language in a more “directorial” way. That combination matters because the hardest problems in video generation are not just aesthetics, but continuity, motion coherence, and control over what happens across time.

What is new and Key Features in Seedance 2.0?

Unified multimodal generation

The most important feature is the model’s ability to jointly reason over several modalities. Seedance 2.0 supports up to 9 images, 3 videos, and 3 audio clips as references, along with natural-language instructions, and can generate videos up to 15 seconds long. In practical terms, that means you can guide not only the subject and scene, but also motion style, camera movement, special effects, and audio cues in one generation pass.

Director-level control

Seedance 2.0 is also built around what ByteDance describes as director-level control. Creators can shape performance, lighting, shadow, and camera movement using reference images, audio, and video. The model can preserve stable subject identity, reproduce complex scripts accurately, and choose camera language in a way that reflects a kind of built-in “editing logic.” For creators, that is a major step beyond basic text-to-video.

Editing and extension, not just generation

Another notable upgrade is that Seedance 2.0 does not stop at generation. Seedance 2.0 adds video editing and video extension capabilities, allowing targeted changes to specific scenes, characters, actions, or plot points, and enabling continuous follow-on shots. The developer article also explains that the model can be used to “continue shooting” by extending a clip rather than starting over. That matters for workflow efficiency, because it reduces the need to regenerate an entire scene just to fix one segment.

Better handling of complex motion

Seedance 2.0 is significantly stronger in scenes with multiple subjects, interactions, and complicated motion. Generation quality has improved substantially from version 1.5, with better physical accuracy, realism, and controllability. Seedance 2.0’s usable rate in difficult motion scenes reaches an industry SOTA level in its internal evaluation framing, while also acknowledging that further improvement is still needed in fine detail stability, realism, and vividness.

Performance Benchmark

The strongest third-party signal in the sources reviewed is the Artificial Analysis Video Arena. On the current leaderboard pages, Dreamina Seedance 2.0 720p leads the Image-to-Video Arena without audio with Elo 1351, and the Text-to-Video Arena without audio with Elo 1269. The leaderboard pages also state that rankings come from blind user votes, which is important because it measures human preference at scale rather than only model-internal metrics.

That matters because it means Seedance 2.0 is not only being marketed as capable; it is currently being preferred by users in head-to-head comparison tests on two major arenas. In text-to-video without audio, it leads Kling 3.0 1080p (Pro), SkyReels V4, PixVerse V6, and Kling 3.0 Omni 1080p (Pro). In image-to-video without audio, it narrowly edges PixVerse V6 and grok-imagine-video.

Seedance 2.0 Performance Snapshot

Metric	Seedance 2.0
Image-to-Video Rank	Top 15 globally
ELO Score	~1258
Text-to-Video Rank	Top 25
Cost	~$1.56/min
Strength	Cost-performance balance

👉 Interpretation:

Not always #1 in raw quality
But exceptional value-to-performance ratio

How good is Seedance 2.0, really?

Its biggest strengths

Seedance 2.0’s biggest strengths are clear: it handles complex motion better than many video models, it supports multiple reference modalities, it offers editing and extension, and it currently leads the most visible public arena rankings in text-to-video and image-to-video without audio. Improvements in physical accuracy, realism, and controllability, which are exactly the attributes that matter when a model moves from toy demos into professional workflows.

Its current limitations

Seedance is not presented by ByteDance as perfect.There is still room to improve detail stability, realism, and motion vividness, and it notes remaining challenges in multi-subject consistency, text rendering precision, and complex editing effects.

My assessment

Based on the sources reviewed, Seedance 2.0 looks less like a marginal update and more like a serious step toward a production-ready video system. Its strongest case is not a single flashy demo, but the combination of a broader multimodal input stack, direct editing controls, clip extension, and credible public leaderboard leadership. That makes it one of the most important video models currently on the market, especially for teams that care about controllability as much as raw cinematic quality.

Seedance 2.0 vs Sora 2 vs Veo 3.1

Comparison Table (2026 AI Video Leaders)

Feature	Seedance 2.0	Sora 2	Veo 3.1
Developer	ByteDance	OpenAI	Google
Input Types	Text, image, audio, video	Text	Text + image
Audio Generation	✅ Native	❌ Limited	✅
Max Video Length	15–20 sec	~25 sec	~8 sec (extendable)
Editing Capability	⭐ Advanced (reference-based)	Moderate	Moderate
ELO Ranking	Top 15–25	High	High
Cost Efficiency	⭐ High	Medium	Medium
Commercial Use	Yes	Limited (watermark)	Yes
Unique Strength	Multimodal editing	Long storytelling	Visual fidelity

Key Takeaways

Seedance 2.0 = best editing + multimodal flexibility
Sora 2 = best narrative length
Veo 3.1 = best image-to-video fidelity

On current Artificial Analysis text-to-video rankings, Seedance 2.0 720p is ahead of both Veo 3.1 and Sora 2 Pro in the no-audio category. That does not settle every quality debate, because the models differ in workflow, safety constraints, and product packaging, but it does show that Seedance 2.0 has moved into the same top tier as the most visible Western offerings.

Seedance 2.0’s most obvious advantage is input breadth. ByteDance says it can jointly process text, image, audio, and video, and can use as many as 9 images, 3 videos, and 3 audio clips at once. OpenAI’s Sora 2 documentation, by contrast, lists text and image as inputs and video plus audio as outputs, with access via the Sora app and sora.com; Sora 2 Pro is also available to ChatGPT Pro users on the web. Google’s Veo 3.1 sits somewhere in between: it is built around image-guided creation and audio-rich video generation, with up to 3 reference images, scene extension, and first-and-last-frame control.

How to access and where to compare

If you want to access Sora 2, Veo 3.1, and xx simultaneously on one platform, I recommend CometAPI. CometAPI's Playgoud provides direct video generation using only a simple command or some reference images. If you want to configure your own video generation API programmatically, then CometAPI is even more worth considering. It provides APIs for Sora 2, Veo 3.1, etc., and is currently priced at 20% off.

How to Use Seedance 2.0 with CometAPI

Text-to-Video Generation

Type a description of your scene. The more specific, the better — include camera movement, lighting, mood, and style. Seedance 2.0’s strong prompt adherence means the output closely matches your intent, making it reliable for content production rather than trial-and-error.

Within CometAPI Playground, you can directly input prompts and generate videos using the Seedance 2.0 model. This is especially useful for social media content (Reels, TikTok, YouTube Shorts), brand videos, and short narrative clips.

How it works:

Open CometAPI
Select the Seedance 2.0 model
Enter your prompt
Adjust parameters (duration, resolution, aspect ratio)
Run the generation job and wait for the output

Image-to-Video with CometAPI

Upload a static image — such as a product photo, concept illustration, or design mockup — and use Seedance 2.0’s image-to-video capabilities through CometAPI to animate it.

The result is smooth, context-aware motion generated from your visual input. This is ideal for teams that already have design assets and want to convert them into video without a full production workflow.

How it works:

Use the input_reference (or equivalent file upload field in Playground)
Add a motion-focused prompt describing how the scene should move

Example prompt:

“Camera slowly pushes in toward the product, soft studio lighting, subtle reflections, premium commercial feel”

Audio-Visual Generation in One Pass

Instead of generating video first and then separately adding audio, CometAPI supports Seedance 2.0’s native audio-visual generation pipeline.

By describing both the visuals and sound in a single prompt, you can generate synchronized video and audio in one step. This produces more cohesive and intentional results, while also reducing editing time.

Example prompt:

“A peaceful beach at sunrise, gentle waves rolling, warm golden light, soft ambient music with ocean sounds”

Output includes:

Generated video
Synchronized background audio
Naturally aligned timing and mood

Why Use CometAPI for Seedance 2.0

Direct access via API or Playground
Easy parameter control (duration, resolution, format)
Supports both text-to-video and image-to-video workflows
Built-in job handling for asynchronous video generation

Conclusion

Seedance 2.0 looks like a genuine leap in AI video generation: a multimodal system that combines text, image, audio, and video inputs; a leaderboard leader in both text-to-video and image-to-video; and a model built for director-style control rather than casual toy use. If you only care about raw perceived quality, the current evidence says it is exceptional.

Start creating with Seedance 2.0 on CometAPI today.

Composer 2: What is new and Compares with Claude Opus 4.6 & GPT-5.4

CometAPI03 — Tue, 24 Mar 2026 15:38:59 +0000

Cursor’s Composer 2 is the company’s newest agentic coding model, announced on March 19, 2026. Cursor describes it as “frontier-level at coding,” built for low-latency software work, and available directly inside Cursor with a standalone usage pool for individual plans. The launch also introduced a faster variant with the same intelligence, plus a new pricing structure designed to make agentic coding more affordable than many general-purpose frontier models.

Composer 2 matters because it reflects a broader shift in AI software development: the value is no longer just raw model intelligence, but the combination of speed, long-horizon task handling, tool use, and cost efficiency. Cursor’s own framing is explicit: the model is optimized for agentic coding, can handle challenging tasks that require hundreds of actions, and was trained to preserve critical context across long-running workflows.

What is Composer 2?

A model built for agentic coding, not just text completion

Composer 2 is Cursor’s in-house coding model. Composer 2 is specialized for software engineering intelligence and speed, trained in the Cursor agent harness, and intended to work well on real coding tasks rather than generic chat. That matters because agentic coding is different from ordinary code generation: the model must search a codebase, edit files, reason over multiple steps, and recover from mistakes without losing the thread of the task. Cursor’s long-horizon training post makes this design goal very clear.

Dual Model Variants:

Variant	Purpose
Standard	Lowest cost
Fast	Higher speed (default)

Why Cursor built it

Cursor’s research posts suggest a simple thesis: better coding agents need both intelligence and efficient continuation over many steps. Its internal benchmark (CursorBench) observations show that stronger performance on hard real-world coding tasks correlates with more thinking and more codebase exploration. Composer 2 is therefore trained not only to solve tasks, but to keep solving them across long trajectories that exceed the model’s immediate context length.

How Composer 2 Work?

Continued pretraining is the big upgrade

Composer 2’s quality gains come from its “first continued pretraining run,” which it describes as providing a much stronger base for reinforcement learning. This is important because it suggests the model is not merely a tuned version of Composer 1.5; it is a better starting point for the kind of long-horizon coding behavior Cursor wants.

Reinforcement learning on long coding trajectories

After continued pretraining, Cursor trains Composer 2 on long-horizon coding tasks through reinforcement learning. The company claims Composer 2 can solve difficult problems requiring hundreds of actions. In practical terms, that means the model is being taught to persist through multi-step debugging, code navigation, and iterative repair loops rather than producing a single-shot answer and stopping there.

Self-summarization is a key research advance

Cursor trains Composer for longer horizons using “self-summarization.” In that setup, when the model reaches a context trigger, it pauses and summarizes its own working state, then continues from that compressed context. Cursor says this technique lets it train on trajectories much longer than the model’s max context window and reward the summaries themselves as part of the training signal.

Durability

The practical upside is durability. Long coding tasks often fail when an agent forgets an earlier decision or loses the important details in a sprawling workspace. Self-summarization reduces compaction error by 50% while using one-fifth of the tokens compared with a tuned prompt-based compaction baseline in its test environments. That is a substantial claim, because compaction is one of the weak points of current agent systems.

What’s New in Composer 2?

1. Continued Pretraining + RL Scaling

Composer 2 introduces Cursor’s first large-scale continued pretraining pipeline, creating a stronger base model for reinforcement learning.

Then, it applies:

Long-horizon RL training
Task chaining across multiple steps
Real-world coding workflows

👉 Result: Better handling of complex engineering tasks, not just code snippets.

2. Long-Horizon Task Execution

Unlike earlier models that fail after a few steps:

Composer 2 can complete multi-file refactors
Execute terminal workflows
Maintain state across hundreds of actions

This pushes it toward true AI coding agent behavior.

3. Code-Only Training Strategy

Composer 2 is trained only on programming-related data.

Why this matters:

Factor	General Models	Composer 2
Model size	Large	Smaller
Scope	Broad	Narrow
Efficiency	Lower	Higher
Cost	High	Low

👉 This explains the massive price-performance advantage.

4. Hybrid Foundation (Kimi Base + RL)

Recent disclosures revealed that Composer 2 was initially built on top of Kimi K2.5 (Moonshot AI) with additional reinforcement training.

Only ~25% compute from base model
Majority from Cursor’s training stack

👉 This reflects a new trend: hybrid model engineering + proprietary optimization

Performance benchmarks

Model	CursorBench	Terminal-Bench 2.0	SWE-bench Multilingual
Composer 2	61.3	61.7	73.7
Composer 1.5	44.2	47.9	65.9
Composer 1	38.0	40.0	56.9

Relative to Composer 1.5, Composer 2 is about 38.7% higher on CursorBench, 28.8% higher on Terminal-Bench 2.0, and 11.8% higher on SWE-bench Multilingual. That does not prove universal superiority over every external model, but it does show a clear step up within Cursor’s own model line.

How Do You Access Composer 2?

Cursor positions Composer 2 as part of the product’s agent-first workflow. It is available in Cursor itself, and Cursor says that on individual plans, Composer usage comes from a standalone usage pool with generous included usage. Cursor also says users can try Composer 2 in the “early alpha” of its new interface. That means Composer 2 is not just a model API; it is meant to be used inside Cursor’s agent workflow, where the editor, agent, browser, and review tools work together.

Inside Cursor

Composer 2 is available in Cursor and also in the early alpha of its new interface. The practical access model is product-native rather than API-first: users interact with it inside the Cursor editor and its agent workflow. That is consistent with Cursor’s broader direction, where the company treats the editor as the primary surface for model interaction.

Usage pools and plan structure

Every individual plan includes two usage pools that reset each billing cycle: Auto + Composer, which gives significantly more included usage when Auto or Composer 2 is selected, and an API pool charged at the model’s API rate. Cursor also says individual plans include at least $20 of API usage each month, with the exact amount increasing on higher tiers. The practical takeaway is that Composer 2 is designed to be used frequently without immediately forcing every request into pure API billing.

API Price:

$0.50 input / $2.50 output per 1M tokens; fast variant $1.50 / $7.50

Plan context

Cursor Pro at $20 per month, Pro Plus at $60, and Ultra at $200, each with different included usage levels. For teams, Cursor also offers Teams and Enterprise with additional controls. That matters because Composer 2 is not just a model SKU; it is part of a broader product package that blends pricing, usage pools, and collaboration controls.

Composer 2 vs Claude Opus 4.6 vs GPT-5.4: Which one should I choose?

Terminal-Bench 2.0

Model	Score
Composer 2	61.7
Claude Opus 4.6	~58
GPT-5.4	~75

👉 Composer 2:

Trails GPT-5.4 in peak performance

Beats Opus 4.6 in some setups

Official Pricing

Model	Input ($/M tokens)	Output ($/M tokens)
Composer 2	0.50	2.50
Composer 2 Fast	1.50	7.50
Claude Opus 4.6	5.00	25.00
GPT-5.4	2.50–5.00	15.00–22.50

👉 Composer 2 is:

10× cheaper than Opus 4.6
~5–6× cheaper than GPT-5.4

Why are Claude Opus 4.6 and GPT-5.4 still worthwhile?

Composer 2 is a strong fit for developers who spend most of their time inside Cursor, especially on repetitive code-editing loops, refactors, multi-file changes, and agentic tasks that benefit from speed and cost efficiency, is optimized around code and long-horizon action execution, with pricing that is dramatically lower.

But Claude Opus 4.6 and GPT-5.4 each bring wider professional capabilities, large context windows, and richer enterprise features. If you need to produce a polished essay, a spreadsheet, and a browser-agent workflow in one go.

Comparison Table:

Feature	Composer 2	Claude Opus 4.6	GPT-5.4
Focus	Coding only	General AI	General AI
Cost	⭐ Lowest	Very high	Medium
Coding Accuracy	High	Very high	High
Reasoning	Medium	Very high	Very high
Speed	Fast variant available	Moderate	Moderate
Agent Capability	Strong	Strong	Improving
Multimodal	❌	✅	✅
Best Use Case	Dev workflows	Research-grade tasks	General + coding

Best-fit use cases and Access

If the task is broad reasoning, multimodal work, or general enterprise use, GPT-5.4 and Claude Opus 4.6 are both strong candidates based on their official positioning and capabilities. If the task is day-to-day coding inside Cursor, especially where cost and iteration speed matter, Composer 2 is the more specialized and cheaper fit. Cursor positions Composer 2 as a specialized agentic coding model for Cursor itself. , GPT-5.4 and Opus 4.6 are broad frontier models, while Composer 2 is purpose-built for the IDE-agent loop.

OpenAI positions GPT-5.4 as a frontier model for complex professional work, with tool support in the API and strong general reasoning. Anthropic positions Claude Opus 4.6 as its smartest model for coding, reasoning, and agentic work, now they all are with availability across CometAPI.

CometAPI's API is currently 20% off, and it can directly generate playgrounds. Compared to other solutions, CometAPI is a much better option; it's essentially a cursor that doesn't require a subscription.

Comclusion

Composer 2 is not just another incremental Cursor model. It is Cursor’s attempt to reset the price-performance curve for coding agents: stronger benchmark results than its predecessors, a design centered on long-horizon agent behavior, and pricing that is dramatically below the big frontier alternatives. Cursor’s own evidence shows clear gains over Composer 1 and 1.5, while its pricing undercuts Claude Opus 4.6 by 10x and GPT-5.4 by 5x on input tokens.

For teams already living in Cursor, Composer 2 is a compelling default for many coding tasks. For the hardest, highest-stakes, or widest-scope work, Claude Opus 4.6 and GPT-5.4 remain the premium benchmarks to compare against. The real story is that the frontier coding market is getting sharper, cheaper, and more specialized all at once.

If you're looking for an alternative to Cursors, or a cheaper, cutting-edge model API like Claude Opus 4.6 and GPT-5.4, then CometAPI is the best choice. Ready to go?