DEV Community: CometAPI03

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?

How to Use Sora 2 Pro Without Subscription (2026 Guide)

CometAPI03 — Tue, 17 Mar 2026 17:09:59 +0000

You cannot legally “unlock” Sora 2 Pro inside OpenAI’s web UI without the official route (ChatGPT Pro or OpenAI API access). However, there are legal alternatives to get Sora-level Pro results without buying ChatGPT Pro: (1) call the Sora 2 Pro model directly via OpenAI’s Video API and pay per-use; (2) use commercial API-aggregation platforms (for example, CometAPI) or SaaS platforms that resell or route Sora 2/2 Pro calls; or (3) use authorized third-party API aggregators (they require their own accounts/fees).

CometAPI is integrated with Sora 2(and pro), allowing developers to generate videos using the API or directly in the playground. No CAPTCHA is required. Furthermore, Global GPT has fewer content restrictions, and the generated videos are watermark-free.

What is Sora 2 Pro and why it matters

Sora 2 Pro is OpenAI’s highest-fidelity video + synced audio generation variant of the Sora 2 family. It’s optimized for production-grade outputs (better physics, longer coherence, synchronized speech & sound), supports text and image inputs, and outputs video + audio in common containers (MP4, MOV). In OpenAI’s model docs, Sora 2 Pro is listed as the most advanced synced-audio video model with higher quality and slower speed versus the standard Sora 2. Pricing is explicitly tiered by resolution and billed per second (e.g., $0.30/second at 720×1280).

Key advantages

Higher fidelity & resolution: Production-grade visual fidelity and motion/physics realism (improved temporal coherence).
Synchronized audio & dialogue: Built-in voice and environmental sound generation with alignment to lip/scene movement.
Longer clips and fewer artefacts: Pro is tuned for tougher shots — longer durations, fewer discontinuities, and priority generation in busy periods.
Programmatic access: Available both via interactive UI (Sora app / ChatGPT) and the OpenAI Video API (as a model name like sora-2-pro), enabling automation and integration.

Sora 2 Pro subscription cost (2026)

Main option (most accurate)

ChatGPT Pro subscription: about $200 per month
This plan includes access to Sora 2 Pro features (higher quality, longer videos, priority generation)

👉 So effectively:
Sora 2 Pro = ~$200/month (via ChatGPT Pro)

What you get for $200/month

With the Pro plan, Sora 2 Pro typically includes:

🎥 Up to 1080p video generation
⏱️ Longer clips (≈20–25 seconds)
⚡ Priority rendering speed
🚫 No watermark (for commercial use)
🎯 Higher-quality physics, motion, and audio sync

These capabilities are consistently described as the key differences vs lower tier.

Who this is for: Creators who prefer an interactive GUI and built-in conversational prompts (no coding). Good for rapid prototyping, editing by instructions, and teams that already use ChatGPT.

Cheaper alternatives

1) ChatGPT Plus (budget option)

$20/month
Includes limited Sora access (NOT Pro)
Lower quality (e.g., 720p, shorter clips, watermark)

2) CometAPI: Pay-as-you-go API (no subscription)

No monthly fee required
API price 20% off

Who this is for: Developers and teams who want integration, automation, or to embed Sora outputs in pipelines. Cost scales with generated seconds.

Model Name	Tags	Orientation	Resolution	Price
sora-2-pro	videos	Portrait	720x1280	$0.24 / sec
sora-2-pro	videos	Landscape	1280x720	$0.24 / sec
sora-2-pro	videos	Portrait (High Res)	1024x1792	$0.40 / sec
sora-2-pro	videos	Landscape (High Res)	1792x1024	$0.40 / sec
sora-2-pro-all	-	Universal / All	-	$0.80000

Quick comparison

Option	Cost	Includes Sora 2 Pro?	Best for
ChatGPT Plus	$20/month	❌ No (limited Sora)	Casual users
ChatGPT Pro	$200/month	✅ Yes	Professionals / creators
API (pay-as-you-go)	~$0.30–$0.70/sec	✅ Yes	Developers / occasional use

Use Sora 2 Pro via ChatGPT Pro (step by step)

What you get with ChatGPT Pro: OpenAI announced ChatGPT Pro as a $200/month tier that includes prioritized access to high-compute features, early product previews, and experimental access to models — OpenAI notes Pro users can use Sora 2 Pro via the Sora site/experience as part of Pro benefits.

Typical workflow (web UI / app)

Subscribe to Pro at OpenAI / ChatGPT billing → log into the Sora web app or Sora integrated UI.
Choose Sora 2 Pro model (if available in your region/account). Configure resolution, aspect ratio, length.
Provide prompt text and optional reference image / cameo asset. Set style parameters and audio voice settings.
Generate; download or use storyboard to stitch scenes (Pro users can produce longer sequences).

Cost comparison (practical):

ChatGPT Pro: $200 / month (flat subscription; includes various Pro benefits and experimental Sora Pro access).
OpenAI API Sora 2 Pro: $0.30–$0.70 per second depending on resolution (720×1280 → $0.30/s; 1920×1080 → $0.70/s). That means a 25-second 720p clip costs 25 × $0.30 = $7.50 in direct API generation cost.

When Pro subscription makes sense

You need unlimited experimentation, priority compute during peak times, early features (storyboard), or you value the subscription convenience and bundled tools (file management, storyboard). If your monthly video spend is low (e.g., < $200/month), paying per video via API / SaaS may be cheaper; if you generate large volumes, Pro may be better value.

Use Sora 2 Pro via the API (get Sora 2 Pro without ChatGPT Pro subscription)

What CometAPI does: it’s an API aggregation layer that normalizes multiple provider endpoints into one OpenAI-style REST interface. You can select a model string (e.g., sora-2-pro) and CometAPI routes the request to the underlying provider, centralizing billing & keys. This is a paid, legitimate commercial service.

1) Account + API key

2) Build your prompt and parameters

Decide:

model: "sora-2-pro"
seconds / duration: target length (e.g., 15, 20, 25)
size: resolution (e.g., 1280x720 landscape or 1080p if supported — Pro commonly supports 1080p at higher cost)
prompt: natural language scene description; include camera/lighting/action cues and any dialogue script.

Prompt example (concise):

A sunlit street market at golden hour. Medium telephoto shot — slow push in over a stall with colorful fruits; a middle-aged vendor smiles and speaks one sentence: "Fresh figs, directly from the farm." Gentle ambient crowd noise, a distant street musician. Realistic textures; cinematic color grade.

3) API workflow (high level)

Submit text + assets to POST /videos (or the model endpoint for sora-2-pro). The job is queued and returns a job id. Poll GET /videos/{id} or configure a webhook. When ready, download via GET /videos/{id}/content. This pattern is described in community docs and API references.

Example curl

curl -X POST "https://api.cometapi.com/v1/videos" \  -H "Authorization: Bearer sk-XXXX" \  -H "Content-Type: application/json" \  -d '{    "model": "sora-2-pro",    "prompt": "A cinematic 8-second clip of a red bicycle rolling down a wet cobblestone street at dusk, realistic lighting, cinematic depth of field, soft piano soundtrack, short spoken line: \"We go on.\"",    "duration_seconds": 8,    "resolution": "1280x720",    "style": "cinematic",    "audio_voice": {"language":"en","voice":"studio_female_1"}  }'

Why use an aggregator?

One API to integrate; fallback providers; combined billing; sometimes a free trial credit; simpler integration into no-code platforms (Zapier, n8n)
Who this is for: Developers and teams who want integration, automation, or to embed Sora outputs in pipelines. Cost scales with generated seconds.

API vs Interactive UI (Sora web / ChatGPT Pro)

Dimension	API (OpenAI sora-2-pro or aggregator)	Interactive Web UI (Sora app / ChatGPT Pro)
Control / Automation	✅ Full programmatic control, repeatable, seeds & snapshots, batch jobs	✅ Great for single experiments, storyboarding, WYSIWYG
Cost model	Pay-per-second (predictable per clip) — can be cheaper for occasional users	Subscription ($200/month) — better if you need heavy, unlimited experimentation
Scalability	High — integrate into pipelines, batch, server side	Low — manual creation, UI limits
Rate limits	Subject to API tier & rate tiers; increases with spend	Subject to UI quotas and Pro priority
Versioning / reproducibility	Snapshots & seeds for exact reproducibility	Less deterministic; manual re-generation may vary
Ease of use	Requires engineering setup or aggregator / SDK	Extremely easy — immediate UX, storyboard tools
Legal / TOS considerations	Clean: bill OpenAI / aggregator	Clean: included in subscription
Best for	Integrations, production pipelines, bulk rendering	Rapid prototyping, creators, storyboard assembly

Best practices of sora-2-pro

Prompt & creative tips

Be explicit about duration — Sora charges per second; shorter tests save money.
Use seeds & snapshots for reproducible output when using the API. (OpenAI exposes snapshots.)
Start low res for iteration (1280×720), then render a final at 1080p/4K if needed.
Split long narratives into scenes (use storyboard features or stitch clips in NLE). Storyboard tools are available in the Pro UX.

Cost & operation controls

Estimate cost before you generate: seconds × $/s × resolution factor + platform fee. Use small experiments.
Batching & async work: queue multiple jobs in one request when possible (saves latency but not per-second cost).
Rate tiers: raise your API tier if you need higher RPM/throughput (OpenAI rate tiers increase with spend).

Legal, safety & ethical rules

Avoid illicit or deceptive content (deepfakes of private persons without consent). OpenAI’s policies and platform TOS govern allowed content. Use consent & releases for likenesses.
Watermarking & provenance: If producing realistic AI people/voices, consider watermark or metadata to indicate AI origin for transparency. Many platforms now warn about misinformation risks.
Copyright: Don’t upload copyrighted audio or images you don’t own as references without rights.

Closing note

Sora 2 Pro represents a major step for text→video/ audio generation: synchronized sound, better physics, and storyboard workflows unlock a host of creative and production use cases. If you don’t want to buy or wait for a ChatGPT Pro invite, reputable third-party aggregators like CometAPI provide a viable, paid route to sora-2-pro today.

Best way to uninstall OpenClaw completly and check for malware 2026

CometAPI03 — Tue, 17 Mar 2026 17:02:27 +0000

OpenClaw, an open-source, native AI agent framework that quickly gained popularity in late 2025/early 2026, has now become a security risk: Governments and enterprises have begun warning users against unrestricted use of OpenClaw due to ongoing reports of security vulnerabilities, malicious third-party "skills," fake installers spreading malware, and high-risk vulnerabilities that could lead to remote code execution or token theft. In March 2026, the Chinese government instructed departments to avoid installing OpenClaw on work devices. Given these circumstances, users and administrators must be cautious about removing OpenClaw and verify that the removal is thorough.

Quick roadmap: What you’ll learn — what OpenClaw is, why removing it matters, how uninstalls can be incomplete, exact commands and checks for each OS, how to find and clean leftover secrets, and how to reinstall safely if you decide to try again.

What is OpenClaw?

OpenClaw is an open-source agent framework and CLI that lets users run autonomous/agentic AI workflows locally. It gained traction because it can orchestrate tasks — from email triage to scheduled automation to running local language models — with minimal configuration. Because it often requires broad file and network access (local files, system services, cloud APIs), it’s powerful — and therefore potentially risky when misconfigured or exploited.

Key technical points you should know:

OpenClaw commonly runs as a background service (“gateway” or “agent”) and exposes a local server (HTTP/WebSocket) for its UI and integrations.
Install methods vary: npm/pnpm/bun global packages, downloadable installers (macOS .dmg/.app, Windows .exe), container images, and repackaged third-party binaries.
It stores persistent state and credentials (workspaces, configuration files, tokens, logs) under user profile directories by default (e.g., ~/.openclaw or %LOCALAPPDATA%\OpenClaw).
Because it can keep long-lived credentials and accept remote requests on localhost, a vulnerable or malicious OpenClaw instance can expose secrets or be turned into persistence for attackers.

Why is there concern that OpenClaw might not be completely removed?

Uninstalling a CLI or app does not necessarily eliminate: running services/daemons, scheduled tasks, registry keys, leftover files (with saved tokens), browser extensions, machine-level persistent agents, or third-party malware that piggybacked on the OpenClaw name.

Uninstalling modern agent platforms is two-track work: removing local binaries/services and cutting off remote access. Common failure modes include:

Leftover state directories and secrets. The official uninstall command (when available) focuses on removing the runtime, but local state directories (e.g., user config, profiles, token caches) often remain. If a user uninstalls via npm uninstall -g or removes the binary manually, those directories persist and store API keys, tokens, or session cookies. Security researchers have shown that the CLI uninstall can leave ~/.clawdbot or ~/clawdbot/ behind if alternate removal paths are used.
Background services that survive. On macOS, user LaunchAgents (e.g., ai.openclaw.gateway) may still be registered; on Linux, systemd user services may persist; on Windows, scheduled tasks or Startup entries in the user profile may keep components alive. If these aren’t cleaned, the gateway can restart or at least block reinstall attempts.
Remote tokens and integrations. Even with a pristine local removal, OpenClaw may have issued long-lived tokens or OAuth sessions to third-party services. Those tokens remain valid until explicitly revoked or rotated. Removing the local client does nothing to revoke them.
Docker / WSL / VM artifacts. Many users run OpenClaw inside Docker containers, WSL2 instances, or VPSes. Uninstalling the host binary does not remove containers, volumes, or images that hold data. Similarly, cloud snapshots or automated backups may keep sensitive data.

Because of these layers, I advise a careful, reproducible process: uninstall via the official method if available, enumerate and delete residual files and background services, and then rotate/revoke every credential OpenClaw touched.

How to uninstall OpenClaw completely — step-by-step

Important preface: If you suspect a compromise (malware installed, unknown network connections, leaked tokens), isolate the system (disconnect from network) before performing live uninstall steps to avoid data exfiltration during removal. Consider forensic capture if this is a managed/enterprise device. The steps below are comprehensive; pick the ones that apply to how OpenClaw was installed on your machine. Use administrator/root privileges where required.

Summary of the complete removal process (quick checklist)

Pause & isolate: disconnect the host from networks (or block gateway port) if you suspect compromise.
Official uninstall: openclaw uninstall (CLI) + remove global package.
Stop/remove services: systemd/launchd/schtasks/services.
Delete state & workspace: ~/.openclaw, ~/.clawdbot, /var/lib/openclaw, /Applications/OpenClaw.app, etc.
Revoke & rotate credentials: API keys, OAuth tokens, webhook secrets used by OpenClaw.
Hunt for persistence & malware: run AV/malware scans, inspect cron, scheduled tasks, autorun registry, and system PATH.
Verify: confirm no open ports, no running processes, no files, and no credentials remain. (See verification commands below).
Optional: reinstall safely in a sandboxed environment (cloud VM / container) only after confirming cleanup and hardening.

Global commands & principles (applies to all platforms)

Run the official uninstall command first (if available):

# Official CLI uninstall (recommended)openclaw uninstall

If openclaw uninstall is available, it will remove the gateway service and prompt to remove state/config. Always read prompts; if you want non-interactive:

openclaw uninstall --all --yes --non-interactive

(Official docs: the install/uninstall flow uses npm/pnpm/bun global packages).

Remove global CLI package (how you installed it):

# npmnpm rm -g openclaw# pnpmpnpm remove -g openclaw# bunbun remove -g openclaw

(If you installed from source, remove the checkout and any symlinks you created.)

Delete state/config/workspace directories (common paths; adjust if you customized):

rm -rf "${OPENCLAW_STATE_DIR:-$HOME/.openclaw}"rm -rf "$HOME/.clawdbot"rm -rf "$HOME/.openclaw/workspace"# macOS apprm -rf /Applications/OpenClaw.app

(Official guidance and community checklists recommend removing the state dir and workspace to eliminate models, logs, and stored credentials).

Revoke and rotate API keys & OAuth tokens that the agent used: OpenAI/Anthropic keys, Slack bots, Telegram bots, Gmail/Google OAuth, Zapier, etc. If in doubt, rotate keys for sensitive services and inspect logs for suspicious activity.

Hunting for malicious leftovers (for compromised installs)

If a fake installer or malicious skill installed additional malware, removing the OpenClaw runtime is necessary but not sufficient. Hunt for:

Unexpected user accounts, cron jobs, scheduled tasks, or SSH keys.
New systemd units or launchd plists that were not removed by uninstall.
Unusual open network connections (ss, netstat, lsof), especially to unknown IPs.
Processes with unusual parent/child relationships.
File system anomalies (recently modified files in /tmp, /var/tmp, %APPDATA%).
Known indicator files from reported campaigns (check vendor IoCs — e.g., Huntress, vendor blogs).

If you find other malware, stop and treat as a security incident: preserve logs, capture memory if possible, and follow organization incident response procedures.

Uninstall differences: macOS vs Windows vs Linux (short comparison)

macOS — uses launchd/LaunchAgents and macOS app bundles. Apps installed as .app can leave plists and cron entries. Permissions and user-level launch agents are common persistence points. (Commands: launchctl, rm -rf /Applications/*, ps/lsof).
Windows — uses services, scheduled tasks, and registry Run keys. Malicious Windows installers commonly add services or scheduled tasks that run after removal if left. (Commands: Get-Service, Get-ScheduledTask, registry inspection).
Linux — often run as systemd service or in Docker. Default installs on servers may bind to an interface and be publicly reachable; check systemctl, docker, ss. Servers are most likely to have large-scale exposure issues.

Removing secrets and revoking access (critical)

Even after files are deleted, tokens or service accounts stored in other cloud providers or third-party dashboards remain valid. Treat them as compromised until rotated.

Actions:

Identify connected providers and tokens. Inspect ~/.openclaw/config, ~/.openclaw/credentials, workspace files, or environment variable files that OpenClaw used. Grep for likely keywords:

# Unix example: search for lines that look like API keys
grep -RiE "(api(_)?key|token|authorization|bearer)" ~/.openclaw || true

Revoke and rotate API keys in each provider dashboard. Log in to providers (OpenAI, Anthropic, cloud vendors) and revoke keys used by OpenClaw; create new keys if needed and remove them from any config files.
Reset passwords and rotate service credentials where the same credential may have been used elsewhere.
Check secrets in your password managers (1Password, Bitwarden, etc.) for stale OpenClaw entries and delete/rotate them.

The security analysis that looked at uninstall traces found that tokens and leftover credentials are the primary residual risk — revocation and rotation are mandatory parts of a “complete” uninstall.

How to Uninstall OpenClaw on Windows

Stop any gateway or app process

# find processesps aux | grep -i openclaw# if you see PID 1234kill 1234

Uninstall launch agents / launchd service

# list possible launch agentslaunchctl list | grep -i openclaw# unload example (adjust label)sudo launchctl bootout system /Library/LaunchDaemons/com.openclaw.gateway.plist

Remove app & CLI

# If installed as macOS apprm -rf /Applications/OpenClaw.app# remove state and CLIrm -rf ~/.openclawnpm rm -g openclaw

Check for malicious installers / other persistence

Inspect ~/Library/LaunchAgents, /Library/LaunchDaemons, and /etc/paths.d.
Check crontab -l for scheduled jobs.
Use lsof -i :<gateway_port> to see if any process is listening on the OpenClaw port (default gateway port can vary).

Verify

# No listening gateway port (example port 3000)lsof -iTCP -sTCP:LISTEN -P | grep 3000 || echo "gateway not listening"# No processesps aux | grep -i openclaw || echo "no openclaw process"

How to Uninstall OpenClaw on Linux (systemd / Debian / RPM / container)

High-level steps: stop systemd unit, remove systemd unit file, uninstall package/npx, delete state, remove crontab entries, remove container images if used.

Stop and disable service

sudo systemctl stop openclaw-gateway.service
sudo systemctl disable openclaw-gateway.service

If the service name differs, locate it:

systemctl list-units --type=service | grep -i openclaw

Remove systemd service file (if installed)

sudo rm -f /etc/systemd/system/openclaw-gateway.service
sudo systemctl daemon-reload

Remove package / npm global package

# if installed via npm/pnpm/bun:
npm uninstall -g openclaw
pnpm remove -g openclaw
bun remove -g openclaw

# if installed as a system package, use apt/dnf
sudo apt remove openclaw   # hypothetical; confirm package name

Delete state/config/workspace

rm -rf "${OPENCLAW_STATE_DIR:-$HOME/.openclaw}"
rm -rf /var/lib/openclaw  # if system-wide state
rm -rf /etc/openclaw      # if config stored here

Check for running sockets / listening ports

ss -ltnp | grep -i openclaw || true
ps aux | grep -i openclaw || true

Containers:
If you ran via Docker/Podman:

docker ps -a | grep openclaw
docker rm -f <container-id>
docker images | grep openclaw
docker rmi <image-id>

How to Uninstall OpenClaw on Windows (PowerShell / Services / Task Scheduler)

High-level steps: stop Windows Service or process, remove scheduled tasks, uninstall MSI/exe, uninstall npm package, delete %APPDATA% state, clean registry keys if present, and scan for malware.

Stop process and service

Open PowerShell as Administrator:

# find process
Get-Process -Name *openclaw* -ErrorAction SilentlyContinue

# if it's a service, stop it (replace service name if different)
Stop-Service -Name "OpenClawGateway" -Force -ErrorAction SilentlyContinue

Remove service via sc.exe (if necessary)

sc.exe queryex OpenClawGateway
sc.exe stop OpenClawGateway
sc.exe delete OpenClawGateway

Remove scheduled tasks

Get-ScheduledTask | Where-Object {$_.TaskName -like '*openclaw*'} | Format-Table TaskName, TaskPath
Unregister-ScheduledTask -TaskName "OpenClawTask" -Confirm:$false

Uninstall binaries

If installed via Windows installer: Settings → Apps → Apps & features → search “OpenClaw” → Uninstall.
If installed via npm:

npm uninstall -g openclaw
pnpm remove -g openclaw
bun remove -g openclaw

Delete state/config directories

Remove-Item -Recurse -Force "$env:LOCALAPPDATA\OpenClaw"
Remove-Item -Recurse -Force "$env:USERPROFILE\.openclaw"

Search for artifacts across disk

Get-ChildItem -Path C:\ -Include *openclaw* -File -Recurse -ErrorAction SilentlyContinue | Select-Object FullName -First 200

Check listening ports and net connections

# list listening ports and owning process IDs
netstat -ano | Select-String ':LISTEN' | Select-String 'openclaw' -Context 0,1

Registry cleanup (advanced)

If you find installers left registry keys for persistence, back up the registry first, then carefully remove keys under HKLM\Software\ or HKCU\Software\ that match OpenClaw. Only perform registry edits if comfortable — otherwise get IT or incident responders involved.

Why reinstall may fail and how to troubleshoot

If reinstall attempts fail (e.g., openclaw onboard errors, gateway install failing, or the GUI never starts), common reasons:

Leftover service entries block new installs. Old LaunchAgents, systemd units, or Scheduled Tasks can conflict with new installs. Remove them (see checks above) before reinstalling.
Ports already bound. The gateway binds WebSocket/listener ports; a stale process or container may keep those ports open. Use lsof -i / netstat -tulpn to find winners and stop them.
Broken node/pnpm environment. OpenClaw relies on Node/Bun/pnpm in places—ensure your package manager and runtimes are correct, and that PATH points to the expected version. Installing via the recommended method (inside WSL for Windows, or native macOS package flow) reduces friction.
Missing permissions/TCC on macOS. On macOS the app needs Accessibility / Screen Recording / Microphone permissions to expose certain node capabilities. If these are blocked or in a bad state, the app may fail to start. Use tccutil and System Settings to verify.
Leftover config profiles with mismatched profile names (OPENCLAW_PROFILE environment variable). Ensure no environment variables are forcing a named profile that no longer exists.

Troubleshooting commands

# find processes using likely ports (example 3000/8080)
sudo lsof -iTCP -sTCP:LISTEN -P -n | grep -E "3000|8080|openclaw" || true

# check journal logs (systemd)
journalctl --user -u ai.openclaw.gateway.service -b | tail -n 200

# on macOS, check Console or syslog for launchd errors:
log show --predicate 'process == "openclaw" OR process == "launchd"' --last 1h

If reinstall still fails, collect logs (openclaw doctor or openclaw status --all), and if you suspect a prior compromise, prefer a clean OS reinstall or forensic image and consult your security team.

Conclusion

OpenClaw is a powerful example of how useful local agent tooling can be — but that same power makes cleanup and security remediation subtle. A “complete” uninstall is more than deleting an app; it’s stopping services, removing all state, revoking credentials, and verifying the system is clean. Use the official uninstall helper when possible, but follow the manual checklist above to catch the hard edge cases — especially if you installed from third-party sources.

CometAPI now integrates with openclaw. If you are looking for APIs that support Claude, Gemini, and GPT-5 Series, CometAPI is the best choice for using openclaw, and its API price is continuously discounted.). OpenClaw recently updated its compatibility with GPT-5.4 and optimized its workflow. Now you can also configure OpenClaw via CometAPI's GPT-5.4.

Ready to Go?

GPT-5.4 vs Claude Sonnet 4.6 (2026) The Ultimate AI Model Comparison

CometAPI03 — Wed, 11 Mar 2026 14:45:40 +0000

OpenAI’s GPT-5.4 (released March 5, 2026) and Anthropic’s Claude Sonnet 4.6 (released Feb 17, 2026) represent two competing approaches to the same market: large-context, agent-capable models optimized for knowledge work, coding, and long, multi-step workflows. Both support million-token context windows (in beta), but they make different tradeoffs in price, token efficiency, and where they concentrate engineering effort.

GPT-5.4 is positioned as OpenAI’s frontier model for professional work: it unifies reasoning, coding (Codex lineage), and native computer-use/agent abilities, and OpenAI reports an 87.3% mean score on a spreadsheet-modeling benchmark for junior investment banking tasks. It also exposes a “Thinking” mode that surfaces in-flight plans during multi-step reasoning.
Claude Sonnet 4.6 is Anthropic’s mid-tier model that has received a large upgrade in capability — deliberately targeting Opus-level task performance at Sonnet-class prices. Sonnet 4.6 is reported to hit ~79.6% on SWE-bench (coding), strong tool/agent scores (OSWorld, Terminal variants), and is now the default Claude model for many Anthropic products.

Using GPT-5.4 and Claude 4.6 models simultaneously requires switching between different providers and incurring expensive costs for each. However, CometAPI solves this problem. With just one API key, you can switch between both models simultaneously, paying only for the tokens used, without subscription.

What is GPT-5.4?

GPT-5.4 is OpenAI’s incremental frontier reasoning release aimed at professional knowledge work, rolled out in ChatGPT (as “GPT-5.4 Thinking”), the API, and Codex. OpenAI positions it as the first mainline reasoning model to inherit frontier coding capabilities from their GPT-5.3-Codex lineage, with improved computer-use, tool search, reduced hallucinations, and experimental 1M-token support in Codex. It is available as gpt-5.4 (and gpt-5.4-pro for higher performance) in the API.

Key product features (what changed vs GPT-5.2 / 5.3)

Upfront plan-of-thinking: GPT-5.4 can provide and present an upfront plan of its reasoning so users can steer mid-response — a workflow improvement for long tasks and multi-step deliverables.
Tool search & improved tool integration: better discovery of connectors and smoother tool use for agents across tools/files.
Token efficiency & speed: OpenAI claims GPT-5.4 is more token-efficient and faster per reasoning effort than GPT-5.2, i.e., fewer tokens to reach the same answer (translating into cost and latency benefits in many workflows).
Context window experimentation: Codex includes experimental support for a 1M token context window (API flag / experimental config). In ChatGPT, context windows remain at the standard (non-1M) settings at launch; Codex/Dev paths allow broader contexts for now.

Measured strengths and OpenAI’s evidence

OpenAI released a suite of benchmark results for GPT-5.4 showing:

GDPval (professional tasks): GPT-5.4 achieves 83.0% (wins or ties vs professionally produced baselines) — positioned as a new SoTA in OpenAI’s GDPval evaluations.
Coding (SWE-Bench Pro): GPT-5.4 posts 57.7% on SWE-Bench Pro (OpenAI’s publicly reported coding benchmark variant). GPT-5.4 also shows substantial gains on internal spreadsheet modelling tasks (mean score 87.3% vs 68.4% for GPT-5.2).
Tool/Browse performance: OpenAI reports BrowseComp 82.7% for GPT-5.4, showing improved web research and tool-backed retrieval.
Factuality: OpenAI reports GPT-5.4’s individual claims are 33% less likely to be false and full responses 18% less likely to contain any error vs GPT-5.2 on a de-identified user prompt set. That’s a nontrivial improvement for production documentation and legal/finance workflows.

What is Claude Sonnet 4.6?

Anthropic‘s Claude Sonnet 4.6 is a generational upgrade to the Sonnet tier: Sonnet is the mid-tier “workhorse” model family that balances capability and cost. Sonnet 4.6 aims to deliver Opus-level intelligence on many tasks (Opus is Anthropic’s premium family), with 1M token context support (beta/availability caveats) and large improvements in agentic robustness, document comprehension, and coding. Anthropic made Sonnet 4.6 the default Sonnet model for claude.ai and Claude Cowork without increasing Sonnet pricing.

Key product/features

Hybrid reasoning + agentic reliability: Sonnet 4.6 improves instruction-following, tool reliability, and adaptive thinking modes used in agentic pipelines. This improves performance on multi-step workflows and orchestrated multi-agent approaches (context compaction + subagents).
1M token context (beta): Anthropic supports 1M context for several internal tasks and documents, and reports results both for <1M public API variants and internal >1M evaluations — with context compaction methods to extend effective capability beyond the raw context window.
Pricing continuity: Sonnet 4.6 kept Sonnet’s previous price points — $3 / 1M input tokens and $15 / 1M output tokens, keeping it attractive for high-volume production use

Measured strengths and Anthropic’s evidence

Anthropic released a comprehensive Sonnet 4.6 system card and blog post documenting internal and third-party evaluations:

SWE-bench Verified (coding): Sonnet 4.6 79.6% on Anthropic’s reported SWE-bench Verified results — significantly strong on actual developer tasks and GitHub issue resolution tests. (Note: Anthropic’s SWE variants and OpenAI’s SWE-Bench Pro are not necessarily identical in composition — caveat below.)
BrowseComp: Sonnet 4.6 achieves 74.01% in a single-agent BrowseComp test, and with multi-agent orchestration (via context compaction and subagents) 82.07% — demonstrating that Sonnet’s multi-agent setups can match or exceed single-agent BrowseComp results from competitors in practice. Anthropic reports test-time compute scaling benefits as well.

Quick Comparison: GPT-5.4 vs Claude Sonnet 4.6

The table below compares the core technical specifications of both models.

Feature	GPT-5.4	Claude Sonnet 4.6
Developer	OpenAI	Anthropic
Release	March 2026	February 2026
Context Window	~1.05M tokens	Up to ~1M tokens
Maximum Output	~128K tokens	~128K tokens
Modalities	Text, image, computer interaction	Text, image
Agent Capability	Native computer use	Tool-based automation
Architecture Focus	General AI agent	Safe reasoning AI
Best For	automation & agents	coding & reasoning
Reasoning style	chain-of-thought planning	adaptive reasoning

GPT-5.4 focuses on agentic autonomy, while Claude Sonnet 4.6 emphasizes structured reasoning and safe deployment.

Feature and technical comparison

1. Context window (how much the model can “see” at once)

GPT-5.4: OpenAI public notes and press reporting indicate support for very large context windows (OpenAI has touted up to 1M tokens in certain variants and integration notes), with product tiers that trade context for latency and cost. Early coverage suggests both a 400k context offering in common dev paths and higher beta windows for Pro/Enterprise.
Claude Sonnet 4.6: Anthropic explicitly advertised beta support for a 1-million-token context in its Sonnet/Opus 4.6 line, positioning long-horizon reasoning as a core design goal. The Sonnet family’s claim centers on sustained chain-of-thought over long documents and agent traces.

Practical effect: When your task is multi-file codebase reasoning, month-long legal contracts, or data lakes of unstructured text, context window size materially improves accuracy, reduces the amount of manual retrieval engineering, and permits conversational workflows that reference long histories. But larger windows come with engineering tradeoffs — longer latencies, higher inference cost, and auditing complexity.

2. Native computer use & agent capabilities

GPT-5.4: One headline capability is “built-in computer use” — the model can generate code that interacts with the host OS or applications (via Playwright and similar toolchains), issue UI commands from screenshots, and orchestrate multi-step automation flows. OpenAI frames this as enabling autonomous agents that can run software rather than just produce code.
Claude Sonnet 4.6: Sonnet 4.6 improves agent planning and persistence: longer task-horizon planning, better internal state management, and improved tool selection. Anthropic emphasizes agent reliability (sustaining multi-step workflows), not just raw automation.

Practical effect: For automation-heavy workflows (e.g., “scrape, analyze, write report, submit ticket”), GPT-5.4’s native computer-use orientation may enable faster prototype agents. Sonnet 4.6’s emphasis on deliberative planning may reduce failure modes in longer agentic chains — helpful where auditability and stepwise correctness are paramount.

GPT-5.4 handles screenshots, mouse and keyboard input, and multi-step workflows at a cutting-edge level. This is one of the most important differences discussed in this article for operations, testing, browser automation, and cross-application tasks.

3. Coding & software engineering

GPT-5.4: Upgrades to Codex and a “/fast mode” to accelerate token throughput and developer feedback loops; positioned as stronger at multi-step developmental tasks and integrating with platforms like GitHub Copilot and VS Code. Early integrations show Copilot enabling GPT-5.4 assistance across mainstream IDEs.
Claude Sonnet 4.6: Anthropic focuses on compressing multi-day projects into hours, improved debugging, code review, and self-correction. Anthropic also points to better handling of large codebases and fewer hallucinated APIs in unit tests.

Practical effect: Both models significantly accelerate developer workflows. Which to pick comes down to integration (your stack, Copilot vs Anthropic SDK), latency/cost at scale, and which model aligns with your correctness expectations under adversarial or safety-critical constraints.

4. Knowledge work, documents, and office productivity

GPT-5.4: OpenAI has geared GPT-5.4 for documents, spreadsheets, and presentations; the company rolled out ChatGPT integrations for Excel and Sheets that let the model execute complex financial modeling tasks. The pitch: enable analysts to automate three-statement models, extract structured tables, and generate slides directly from raw data.
Claude Sonnet 4.6: Anthropic emphasizes long-context summarization and planning for knowledge work — better at sustaining multi-part arguments across long documents and producing structured outputs for legal, research, and policy workflows.

Practical effect: If your firm needs spreadsheet automation and tight integrations with Microsoft/Google productivity suites, OpenAI’s announced add-ins accelerate adoption. If your need is forensic analysis across long legal or research texts, Sonnet’s long-context claims are compelling.

5. Multimodal support

GPT-5.4: marketed primarily as a text-first model with robust document and spreadsheet handling; image input support is noted in some GPT-5 series variants but GPT-5.4’s emphasis is on text + tool integrations (and developer-facing Codex features for programmatic tool use).
Claude Sonnet 4.6: Anthropic emphasize text, coding, and agent planning. Sonnet 4.6 is described as highly capable in “computer use” (simulated GUI interactions, automated tool invocation) and long-session planning; multimodal claims are less front-and-center than the model’s reasoning/agent strengths.

Practical takeaway: For workflows that require mixed media (images + text), buyers should validate modality support in the specific API tier they plan to use. For text-heavy, multi-file, and spreadsheet workflows both models prioritize encodings and compaction strategies that make long context tractable.

Side-by-side: capability and benchmark comparison

Below are concise, directly comparable datapoints drawn from the vendors’ published pages and system cards. I include the primary caveats inline.

Browse / web-research (BrowseComp)

GPT-5.4 (OpenAI) — 82.7% BrowseComp. (OpenAI: BrowseComp 82.7% in the GPT-5.4 release materials.)
Claude Sonnet 4.6 (Anthropic) — 74.01% single-agent BrowseComp; 82.07% multi-agent BrowseComp when run with an orchestrator + subagents / context compaction (Anthropic reports both values and explains the multi-agent advantage). Anthropic also reports test-time compute scaling (e.g., 64.69% @1M sampled tokens rising toward 74% at higher total sampled tokens).

Coding and developer work (SWE/Terminal)

SWE-style tests: Anthropic reports Sonnet 4.6 at 79.6% on SWE-Bench Verified (their verified, human-validated coding subset). OpenAI reports GPT-5.4 57.7% on SWE-Bench Pro (OpenAI’s public pro variant). These results show Sonnet very strong on Anthropic’s chosen SWE variant. Important caveat: the SWE datasets and evaluation protocols differ by vendor; direct numeric comparison should be treated cautiously.

Professional / knowledge work (GDPval / GDPval-AA / OfficeQA)

OpenAI (GPT-5.4) — GDPval 83.0% (OpenAI’s GDPval metric across 44 occupations; OpenAI frames this as matching or exceeding industry professionals in 83% of pairwise comparisons). OpenAI also reports very strong spreadsheet/presentation gains (e.g., internal investment banking task mean score 87.3% vs 68.4% for GPT-5.2).
Anthropic (Sonnet 4.6) — Anthropic reports strong performance on internal finance/OfficeQA and Real-World Finance tasks; Sonnet matches Opus 4.6 on OfficeQA and posts high task-completion rates in internal finance evaluations; Anthropic reports Sonnet 4.6 89.9% on GPQA Diamond and other high marks on domain tests. These are powerful signals that Sonnet is highly capable on enterprise document tasks.

Data-backed comparison table

Dimension	GPT-5.4 (OpenAI)	Claude Sonnet 4.6 (Anthropic)
BrowseComp (vendor reported)	82.7% (base) / 89.3% (Pro, some settings).	74.01% (single) → 82.07% (multi-agent).
Coding (vendor VAR)	SWE-Bench Pro ~57.7% (OpenAI reported).	SWE-bench Verified ~79.6% (Anthropic reported).
Pricing (input/output per 1M tokens)	~$2.50 / $15 (base list examples).	$3 / $15; strong caching & batch savings.
1M token context	Experimental via Codex/dev; ChatGPT rollout varies.	1M context beta + compaction strategies.
Safety posture	Factuality improvement (↓33% false claims vs GPT-5.2). Balanced refusal/completion.	Highly conservative refusals on many safety slices (system card numbers).

Pricing Comparison

Pricing is one of the most important factors for organizations deploying AI at scale.

API Pricing

Pricing	GPT-5.4	Claude Opus 4.6
Input tokens	$2.50 / 1M	$15 / 1M
Output tokens	$3/ 1M	$15 / 1M

GPT-5.4 is slightly cheaper on input tokens.

This difference becomes significant for high-volume workloads such as:

enterprise automation
data analysis pipelines
large-scale code generation

Subscription Pricing

Both platforms offer similar subscription tiers.

Plan	ChatGPT	Claude
Standard	$20/month	$20/month
Premium	$200/month	$200/month

At the subscription level, pricing parity means the real cost difference appears primarily in API usage.

Looking for cost-effectiveness: Access GPT-5.4 and Opus 4.6 via CometAPI.

If your workflow requires multiple GPT-5.4 and Claude 4.6 (each with its own characteristics), paying different vendors separately can be costly and cumbersome. This is where CometAPI's multi-modal aggregation platform comes in strategically.

CometAPI's philosophy is simple: instead of maintaining multiple official accounts to compare outputs, users can access leading models on a single platform, quickly switch between them, and evaluate workflows side-by-side. It also offers a 20% API discount and pay-as-you-go pricing without a subscription.

Strengths and Weaknesses

Where GPT-5.4 Wins

Advantages:

superior automation capabilities
better terminal-based coding
lower API cost
stronger performance in knowledge-work tasks
broader general intelligence

Best for:

startups
automation systems
developer tooling
research assistants

Where Claude Opus 4.6 Wins

Advantages:

stronger reasoning depth
best-in-class coding benchmark scores
better large-context retrieval
multi-agent collaboration tools

Best for:

enterprise software teams
infrastructure engineering
research environments

The Future: Multi-Model Workflows

An important industry trend is emerging.

Rather than choosing a single AI model, many teams now use multiple models simultaneously.

Example workflow:

GPT-5.4 → automation and data analysis
Claude Opus 4.6 → deep coding and architecture
other models → specialized tasks

This model-routing architecture allows teams to maximize strengths while minimizing weaknesses.

Final Verdict

Both GPT-5.4 and Claude Sonnet 4.6 are among the most powerful AI models available in 2026. GPT-5.4 excels in agentic automation and integrated workflows, while Claude Sonnet 4.6 offers efficient, scalable reasoning capabilities with competitive pricing.

GPT-5.4 in Openclaw Guide: Benfits, Configuration & Best Practice

CometAPI03 — Mon, 09 Mar 2026 16:11:14 +0000

OpenClaw’s recent release adds first-class, forward-compatible support for OpenAI’s GPT-5.4 and introduces a “memory hot-swappable” architecture that lets OpenClaw agents change which model and which memory store are active at runtime with minimal disruption. This unlocks large-context workflows (GPT-5.4’s expanded context windows), on-the-fly model specialization, and cost/latency optimizations for production agents. The upgrade is available in OpenClaw’s releases and accompanying docs; the examples below show practical configuration, code snippets, benchmark context, and recommended best practices.

What OpenClaw’s update actually shipped (quick summary)

On March 9, 2026, the open-source agent framework OpenAI-adjacent project OpenClaw shipped a major core release (2026.3.7) that adds first-class support for GPT-5.4 and a novel memory hot-swappable mechanism in its context engine. This release converts a widely used experimental agent framework into what the maintainers describe as an “Agent Operating System” — aiming to make production-grade agent workflows and model switching seamless for developers and teams.

3 practical items that matter for agent builders:

First-class GPT-5.4 support — model aliases and provider mappings that let agents select GPT-5.4 as the primary execution model (including channel overrides and per-agent model pins).
Context Engine & distributed channel binding — improvements in how OpenClaw assembles long context windows from memory, tool outputs, and channel history so high-capacity models get well-structured inputs.
Memory hot-swappable architecture — clearer memory plugin surfaces and workflows so you can replace memory backends or upgrade agents without losing “identity” or corrupting persisted state (the memory itself remains the single source of truth). OpenClaw’s memory design (plain Markdown files, indexed search, pluginized retrieval) is part of what enables safe hot-swapping.

GPT-5.4 — What GPT-5.4 is and benchmarks breakthrough

GPT-5.4 is the latest OpenAI frontier model release focused heavily on professional productivity (spreadsheets, document and presentation editing, multi-step reasoning and tool driving). According to OpenAI and independent press coverage, the release emphasizes:

Expanded context: GPT-5.4 introduces the next tier of context windows, with experimental 1M-token and improved long-context handling available via Codex/Codex-compatible endpoints—configuration knobs like model_context_window and model_auto_compact_token_limit are exposed for developers. This lets you keep far larger conversation state, documents, and code bases in active context.
Higher spreadsheet and reasoning accuracy — OpenAI reports a major improvement on spreadsheet modeling tasks (mean scores ~87% vs ~68% for GPT-5.2 on their banking/analyst spreadsheet benchmark).
Accuracy and factuality improvements: Early reviews and QA show ~33% reduction in hallucinations and lower error-prone outputs relative to GPT-5.2, with notable gains in document drafting and spreadsheet work. Reviewers also cited an ~18% decrease in error-prone responses on certain productivity tasks.
Integrated computer-use and Codex lineage improvements — GPT-5.4 includes capabilities inherited from the Codex lineage that improve code generation, interactive debugging, and operational tool driving (mouse/keyboard/screenshot automation in some demonstrations). This makes it better at the write-run-inspect-patch cycle typical of agent loops.

Benchmarks & comparative context (what numbers mean)

Spreadsheet modelling: OpenAI’s internal spreadsheet benchmark: ~87.3% mean score for GPT-5.4 vs ~68.4% for GPT-5.2. This is the headline the vendor uses to show task-specific gains.
Computer interaction (OSWorld / agent-style tests): independent testers and community runs show GPT-5.4 improves on agent interaction tasks that involve desktop or simulated UI manipulation, sometimes edging out recent Anthropic model variants in small margins on these task suites (differences are meaningful for agents, but not necessarily decisive in every workload).

Interpretation: GPT-5.4 is not a “magic bullet” that wins everything. It has clear strengths in integrated tool use, code execution patterns, and spreadsheet reasoning — which are exactly the workloads OpenClaw agents often run. For agent builders, the combination of improved executor reliability (Codex lineage) + planner competence + better long-context handling is highly relevant.

OpenClaw supports GPT-5.4: what changed and why it matters

OpenClaw’s release (see the project releases page) updates model resolvers and runtime to be forward-compatible with GPT-5.4’s expanded context and token limits and adds the “memory hot-swappable” capability so agents can switch memory backends or models at runtime. This is done in three concrete ways: 1) model metadata and resolver updates to accept the larger context and token limits; 2) agent runtime changes to orchestrate graceful model swaps and cache warm-up; 3) a memory API allowing multiple memory channels and hot switch triggers.

Version 2026.3.7’s support for GPT-5.4 plus a memory hot-swappable design provides two practical, complementary advantages:

Straightforward model upgrade path. OpenClaw can now present GPT-5.4 as a selectable "runtime" for agents, letting you switch from older GPT-5.x models or alternative vendors without reworking your agent logic. The OpenClaw update explicitly declares stable GPT-5.4 integration in the core.
Memory Hot-Swapping. Instead of persisting a single linear memory snapshot, OpenClaw’s Context Engine allows memory partitions to be detached, swapped or migrated at runtime — e.g., swap in a high-recall vector DB shard for debugging or swap to a GDPR-sanitized memory variant for external audits — without stopping the agent. That lowers disruption risk in production and enables use-case-specific memory configurations (debugging vs privacy vs performance).

practical performance breakthroughs & advantages

OpenClaw’s integration focuses on three practical areas where GPT-5.4 shines:

Tool orchestration fidelity. GPT-5.4’s improved internal tool-search and reasoning reduces tool-call churn (fewer redundant tool calls and fewer retries). That translates to lower API calls and faster completion for complex flows. Early reports indicate token and tool-call efficiency improvements compared to older GPT-5.x models.
Longer, richer context handling. OpenClaw agents can now keep much larger active contexts (including memory shards swapped in), allowing them to manage long conversations, multi-file projects, and iterative debugging without losing state.
Better deterministic code output. For workflows that auto-generate code (CI hooks, function stubs, infrastructure templates), GPT-5.4 tends to produce more consistent and runnable outputs, reducing human review overhead. Independent tests show notable improvements in code quality metrics vs prior GPT-5 models.
Memory continuity — “memory hot-swappable” lets you replace or augment memory stores (local cache, vector DB, LLM memory) without losing agent state or context, enabling A/B testing, rolling upgrades, and failover.

In the OOLONG benchmark test, the new version of OpenClaw, in conjunction with the lossless-claw plugin, achieved a high score of 74.8, leaving Claude Code (70.3 points) far behind. In particular, OpenClaw demonstrated stability and accuracy as the context length increased, prompting the engineers conducting the on-site testing to exclaim that "it's too conservative to say it runs well."

How to configure and use GPT-5.4 in OpenClaw (step-by-step)

A Simple OpenClaw Workflow Using GPT 5.4:

A typical configuration is shown below:
Users send messages via platforms such as Discord or Telegram.
OpenClaw receives the messages through its gateway server.
The gateway forwards the prompts to GPT 5.4 via the AI API provider.
GPT 5.4 generates a response or triggers a tool action.
OpenClaw sends the final result back to the user.

Below are pragmatic, copy-pasteable configuration examples and workflows to run GPT-5.4 in OpenClaw safely and reproducibly. These are intentionally conservative: enable the model first in a test agent and instrument everything for metrics and errors.

Prerequisites- OpenClaw upgraded to the release that includes GPT-5.4 mappings (the release notes referenced above).

Valid OpenAI API key with access to GPT-5.4 (I choose CometAPI endpoint with cheaper price).

1) Model selection & resolver configuration (Json/ YAML / CLI)

Put this in ~/.openclaw/openclaw.json (or merge into your existing config). Adjust provider name and token reference as required by your environment.

</>JSON
{
  "agents": {
    "defaults": {
      "model": {
        "primary": "openai/gpt-5.4",
        "fallbacks": ["openai/gpt-5.3", "claude/opus-4.6"]
      },
      "workspace": "~/.openclaw/workspace"
    }
  },
  "models": {
    "providers": {
      "openai": {
        "api_key_env": "ComtAPI_API_KEY",
        "base_url": "https://api.cometapi.com/v1"
      }
    }
  },
  "plugins": {
    "slots": {
      "memory": "memory-core"
    }
  },
  "channels": {
    "modelByChannel": {
      "support-team": "gpt-5.4",
      "low-cost-batch": "gpt-5.3"
    }
  }
}

OpenClaw uses a model resolver to map logical model names (e.g., openai/gpt-5.4) to endpoints and runtime config. Add or update your resolver file (example models.yml):

</> YAML
# models.yml - OpenClaw model resolvers
models:
  openai/gpt-5.4:
    provider: openai
    model_id: gpt-5.4
    context_window: 1050000   # forward-compatible 1,050,000 tokens
    max_output_tokens: 128000
    api_base: "https://api.openai.com/v1"
    timeout_seconds: 120
    rate_limit_factor: 1.0

Or set it at runtime via CLI:

</> Bash
# Switch OpenClaw to use GPT-5.4 for the current agent session
openclaw model set openai/gpt-5.4

Note: The context_window and max

Notes

agents.defaults.model.primary picks the default model. Use channels.modelByChannel for per-channel overrides so you can route high-impact channels to GPT-5.4 and less demanding channels to cheaper models. See OpenClaw model selection docs for ordering semantics.
Please refer to the CometAPI model page for specific model names. If you wish to use OpenAI, replace the URL and API key with OpenAI's.
The context_window and max_output_tokens keys reflect the forward-compatibility changes in OpenClaw’s resolver so the agent will not attempt to use stale Codex limits.

2) How to enable and test “memory hot-swapping”

OpenClaw’s memory subsystem is file-based (Markdown files) plus indexers/search plugins so you can safely swap backend plugins (e.g., SQLite vector, Milvus, or external memory services) without losing the raw memory files.

A common pattern:

Standardize memory location: use a git-backed workspace: ~/.openclaw/workspace/ where MEMORY.md and memory/YYYY-MM-DD.md are authoritative.
Install & configure a memory plugin (example: sqlite-vec) and point plugins.slots.memory at it in the config.
Test migration: add a new plugin, run a shadow indexing job, compare retrieval results, then switch the plugins.slots.memory alias to the new plugin when satisifed.

Example plugin alias swap (bash pseudo-commands):

# install new plugin (example package)pip install openclaw-memory-sqlite-vec# update config safely (backup first)cp ~/.openclaw/openclaw.json ~/.openclaw/openclaw.json.bak# then edit JSON: plugins.slots.memory = "memory-sqlite-vec"# reload gateway (safe restart)systemctl restart openclaw || openclaw gateway restart# run a retrieval consistency check using the test harnessopenclaw test memory_consistency --samples 100

Why this is “hot-swappable”: the memory files stay the source of truth. Plugins implement indexing and retrieval layers; swapping them reindexes but does not change the underlying .md files. This allows model swaps without catastrophic identity drift — the agent still reads the same MEMORIES.

3) Example: pin an individual agent to GPT-5.4 (per-agent override)

You can override models per agent; add an agent entry like:

{
  "agents": {
    "my-analyst-agent": {
      "model": {
        "primary": "gpt-5.4"
      },
      "workspace": "~/.openclaw/workspace/analyst"
    }
  }
}

If the community release or your particular OpenClaw version requires CLI, you can also set a per-session model at runtime:

# Start a session and switch model for the live session
openclaw session start my-analyst-agent
openclaw session command /model gpt-5.4

Operational tip: pinning ensures deterministic behavior for that agent while you run A/B tests on others.

If you wish to use OpenAI, replace the URL and API key with OpenAI's.

4) Using Codex 1M context options (API knobs)

If your OpenClaw deployment accesses OpenAI Codex endpoints directly, pass context options:

{  "model": "openai-codex/gpt-5.4",  "input": "...",  "model_context_window": 1050000,  "model_auto_compact_token_limit": 200000}

Requests that exceed standard context windows may count at different usage rates (OpenAI docs note double accounting for requests beyond standard windows in Codex preview).

Best practices: maximizing GPT-5.4’s strengths in OpenClaw

Cost, latency & model mix

Hybrid model strategy: Use a smaller, cheaper model for short queries and stream processing; hot-swap to GPT-5.4 for heavyweight analysis, summarization, code generation requiring long context. This reduces overall token cost while preserving quality. (Implement via triggers in the memory config above.)
Token compaction & retrieval augmentation: Use retrieval-augmented pipelines to limit tokens sent to the model — store long documents in a vector DB, retrieve relevant segments, and include only the most relevant chunks plus a compact plan. GPT-5.4’s tool search helps here by locating helpful tools or documents automatically.
Warm-up & cold start: After a model swap, warm up the model with a short context priming run to avoid first-request latency spikes. Precompile any prompt templates and rehydrate critical memory channels. OpenClaw’s rolling strategy (see config) supports pre-warming.

Reliability & safety

Graceful fallback: Implement timeouts and fallback plans (e.g., degrade to a cached answer from a previous session) to handle API rate limits or quota errors.
Safety layers: Maintain policy filters and a verification step when outputs affect decisions. GPT-5.4 reduces hallucinations statistically, but verification is still important for high-stakes tasks.

Eval & monitoring

Reproduce your benchmarks: Run head-to-head tests for your workloads (code completion, multi-file refactor, spreadsheet analysis) using a standard rubric. Public reports indicate strengths in spreadsheet and productivity tasks — validate with your data.
Telemetry: Monitor token consumption, model latency, memory swap frequency, and answer quality (human ratings/automated tests). Use the telemetry to refine swap thresholds.

Example : Code review agent that hot-swaps

Goal: Run a routine lint + unit test summary on push (cheap model) and escalate to GPT-5.4 for multi-file refactor suggestions when tests fail or diffs exceed 10 files.

Flow (high level):

Pre-commit trigger runs local/fast-small-coder to generate lint summary.
If test_failures > 0 or diff_files > 10, trigger hot_swap to openai/gpt-5.4. Promote longterm_vector containing repo history.
Run GPT-5.4 prompt that has entire failing stack traces + relevant code files pulled into context. Generate refactor patch and unit test changes.
Human reviewer rates output; feedback updates memory.

Prompt skeleton (sent to GPT-5.4 after retrieval & compaction):

You are a senior reviewer. The repository has 12 changed files. Tests failed with stack traces below. Relevant files (retrieved): <file snippets>. Provide:1) concise summary of root cause (3 bullets),2) a minimal patch (diff) to fix,3) test changes needed,4) risk assessment and roll-back plan.

This use case highlights why large context + memory hot-swap is valuable: you can bring the full failing trace and multiple files into the model at once. Implement swap triggers conservatively to control costs.

Finally: who should adopt GPT-5.4 in OpenClaw (and when)

Adopt now if your agents perform multi-step code/tool tasks, heavy spreadsheet automation, or complex document editing where iterative write-run-inspect cycles dominate developer time. The productivity and reliability gains are most visible here.
Adopt carefully if you operate in cost-sensitive, high-volume chat channels where simpler reasoning suffices; use routing to preserve cost efficiency.
Don’t assume one-model dominance: benchmark on your data. GPT-5.4 is a strong contender for agent workloads, but model choice must be evidence-driven.