DEV Community: Mattias chaw

Kimi K3 Review: Moonshot's Latest Model for Coding and Reasoning

Mattias chaw — Sun, 26 Jul 2026 15:07:22 +0000

Kimi K3 is Moonshot AI's flagship model. It sits in an interesting position: not the cheapest (that's DeepSeek V4 Flash), not the largest context (that's DeepSeek V4 Pro's 1M), but offering a strong balance of quality and cost.

Specs and Pricing

	Kimi K3	DeepSeek V4 Flash	GLM-5
Input (USD/1M)	$0.30	$0.14	$0.20
Output (USD/1M)	$0.90	$0.28	$0.60
Context window	128K	1M	128K
Max output	8K tokens	384K tokens	8K tokens

K3 is priced between DeepSeek V4 Flash and GLM-5. Roughly 2x more than V4 Flash for stronger reasoning.

Coding Performance

Moonshot positions K3 as a general-purpose model rather than a coding specialist. In practice:

Standard coding (function writing, bug fixes, refactoring): Solid, comparable to GLM-5
Large codebase analysis: Limited by 128K context. DeepSeek V4 Pro's 1M handles this better
Multi-file changes: Adequate for 3-5 files within a single context
Debugging: Good at following instructions, occasionally misses subtle bugs that V4 Pro catches

For day-to-day coding in Cursor, Continue, or similar tools, K3 works well. It's not the best coding model — DeepSeek V4 Pro holds that title — but reliable for most tasks.

Reasoning

Math and logic: Strong. Handles multi-step calculations accurately
Data analysis: Good at interpreting structured data and drawing conclusions
Code explanation: Clear, detailed explanations of complex code
Creative tasks: Better than most models at maintaining consistency in longer outputs

128K Context in Practice

128K tokens is roughly 100K English words. Sufficient for most API calls. Limiting when:

Analyzing entire repositories
Processing very long documents
Multi-turn conversations that accumulate context

If you regularly exceed 128K, DeepSeek V4 Pro at 1M tokens is the better choice — and it's cheaper per token.

Cost Analysis

100 API calls/day, 3K input + 1.5K output per call, 22 working days/month.

Model	Monthly Cost
Kimi K3	$4.95
DeepSeek V4 Flash	$1.65
GLM-5	$2.86
GPT-4o	$59.40

K3 costs roughly 3x more than V4 Flash but 12x less than GPT-4o. The sweet spot: better reasoning than V4 Flash without paying for V4 Pro's premium.

When to Pick Kimi K3

Good fit:

General coding assistance where you want better reasoning than V4 Flash
API applications with moderate context needs (< 100K per request)
Chat-based products where response quality matters more than speed
Budget-conscious teams that find V4 Pro overkill

Not ideal:

Large codebase analysis (need 1M context → DeepSeek V4 Pro)
High-volume autocomplete (need lowest cost → DeepSeek V4 Flash)
Chinese-language-heavy workloads (GLM-5 handles Chinese better)

Getting Started

All Moonshot models are available through AIWave with a single API key. No Chinese payment method needed — PayPal works. The $1 free credit covers about 200 Kimi K3 calls, enough to evaluate.

from openai import OpenAI
client = OpenAI(api_key="***", base_url="https://aiwave.live/v1")
response = client.chat.completions.create(
    model="kimi-k3",
    messages=[{"role": "user", "content": "Explain this code"}]
)

DeepSeek V4 Pro vs Claude Sonnet 4: Real Benchmark Data (July 2026)

Mattias chaw — Sun, 26 Jul 2026 15:06:41 +0000

The gap between Chinese and Western AI models has narrowed faster than expected. DeepSeek V4 Pro now scores within single-digit points of Claude Sonnet 4 on major coding and reasoning benchmarks — while costing roughly 7x less per input token and 18x less per output token.

Benchmark Comparison

Benchmark	Claude Sonnet 4	DeepSeek V4 Pro	Difference
HumanEval (coding)	~93%	92.1%	-0.9
MMLU (knowledge)	~89%	88.5%	-0.5
MATH (math)	~78%	90.2%	+12.2
GPQA (science)	~65%	71.5%	+6.5
LiveCodeBench (coding)	~62%	57.3%	-4.7

DeepSeek V4 Pro actually outperforms Claude on math and science. On coding, Claude holds a small edge under 5 points.

Pricing

	Claude Sonnet 4	DeepSeek V4 Pro	Savings
Input (USD/1M)	$3.00	$0.42	86%
Output (USD/1M)	$15.00	$0.84	94%
Context window	200K tokens	1M tokens	5x larger

What This Means in Practice

Coding Assistance (Cursor, Continue, etc.)

50 calls per session, 4K input + 2K output per call, 5 sessions per day, 22 work days.

Model	Per Session	Monthly (22 days)
Claude Sonnet 4	$2.10	$231
DeepSeek V4 Pro	$0.17	$18.48

Annual savings for a single developer: ~$2,500. For a 10-person team: ~$25,000.

API-Heavy Applications

A SaaS product processing 500K API calls/month, averaging 2K in + 1K out:

Model	Monthly Cost
Claude Sonnet 4	$1,050
DeepSeek V4 Pro	$59

The 1M context window on DeepSeek also means you can pass larger codebases without chunking — something Claude's 200K window makes expensive.

Where Claude Still Wins

Complex multi-step reasoning: Claude handles long chains of dependent logic more reliably
Nuanced instruction following: More precise on tasks requiring careful constraint satisfaction
Safety alignment: More predictable refusal behavior
Enterprise features: More mature tooling for enterprise deployments

The Pragmatic Approach

Use DeepSeek V4 Pro for 80-90% of requests (coding, content, data extraction) and Claude for the remaining 10-20% (complex reasoning, critical analysis).

With an AIWave API key, both are accessible through the same OpenAI-compatible interface — switch with a single model parameter change.

The $1 free credit covers roughly 6 DeepSeek V4 Pro coding sessions (about a day of heavy use) or roughly 1 Claude session. Enough to benchmark both against your actual workload.

VSCode + Continue + DeepSeek: AI Coding Without the GPT-4o Price Tag

Mattias chaw — Sun, 26 Jul 2026 15:06:37 +0000

Continue is the most popular open-source AI coding extension for VSCode. It supports tab autocomplete and a chat sidebar, working with any OpenAI-compatible API.

Pairing Continue with DeepSeek V4 through AIWave gives you the same coding assistant experience as using GPT-4o — but at a fraction of the cost.

Why Continue + DeepSeek?

Continue has two main features:

Tab autocomplete: Suggests completions as you type (like Copilot)
Chat sidebar: Ask questions about your codebase, generate code, explain errors

DeepSeek V4 Flash is fast enough for real-time autocomplete (sub-second latency from most regions to AIWave's Singapore servers) and strong enough for complex coding questions.

Installation

Install Continue from the VSCode Marketplace
Get an API key from AIWave (free $1 credit on signup)
Configure (see below)

Configuration

Open Continue's config file (~/.continue/config.yaml or via Ctrl+Shift+P > "Continue: Open Config"):

models:
  - name: DeepSeek V4 Flash
    provider: openai
    model: deepseek-chat
    apiBase: https://aiwave.live/v1
    apiKey: YOUR_API_KEY
    roles:
      - autocomplete
      - chat

  - name: DeepSeek V4 Pro
    provider: openai
    model: deepseek-reasoner
    apiBase: https://aiwave.live/v1
    apiKey: YOUR_API_KEY
    roles:
      - chat

This uses V4 Flash for autocomplete (speed-critical) and V4 Pro for chat (quality-critical). Both use the same API key.

Autocomplete vs Chat: Different Needs

	Autocomplete	Chat
Latency needed	< 500ms	1-5s acceptable
Typical input	500-2K tokens	2K-20K tokens
Typical output	50-200 tokens	500-4K tokens
Best DeepSeek model	V4 Flash	V4 Pro

Autocomplete fires on every keystroke pause — high volume, tiny per-request cost. Chat fires on explicit action with longer contexts.

Cost Estimate

Assumptions: 300 autocomplete calls/day (500 in + 100 out) + 20 chat messages/day (4K in + 2K out), 22 work days/month.

Component	Daily	Monthly (22 days)
Autocomplete (V4 Flash)	$0.0028	$0.06
Chat (V4 Pro)	$0.0432	$0.95
Total	$0.046	$1.01

Same workload with GPT-4o: ~$15/month.

Tips

Use @ mentions: Type @ followed by a filename to include it in chat context. @folder/ includes entire directories.
Reduce autocomplete latency: If autocomplete feels slow, use V4 Flash exclusively.
Context management: DeepSeek V4 supports 1M tokens. Continue auto-manages context, but use /clear to reset if responses degrade.

Troubleshooting

Autocomplete not triggering: Verify the model has the autocomplete role. Check VSCode: Editor: Accept Suggestion On should be enabled.
API errors: Confirm apiBase ends with /v1. Test with curl to AIWave's endpoint.
Model name mismatch: Use deepseek-chat for V4 Flash and deepseek-reasoner for V4 Pro. See models page.

Continue gives you a Copilot-like experience in VSCode. DeepSeek through AIWave makes it affordable — about $1/month for heavy daily use. The $1 free credit covers your first month while you evaluate.

Claude API Too Expensive? How to Switch to Chinese Models

Mattias chaw — Sun, 26 Jul 2026 15:05:56 +0000

Claude Sonnet 4 is excellent at coding, analysis, and long-form writing. At $3 per million input tokens and $15 per million output tokens (Anthropic pricing), it's also one of the most expensive mainstream LLM APIs.

Chinese AI models have closed the quality gap significantly in 2026. DeepSeek V4 Pro, Zhipu GLM-5, and Qwen 3.5 now perform within striking distance of Claude on coding and reasoning benchmarks — at 5-10x lower cost.

The Price Gap

Model	Input (USD/1M)	Output (USD/1M)	Ratio vs Claude
Claude Sonnet 4	$3.00	$15.00	1.0x
GPT-4o	$2.50	$10.00	0.83x
DeepSeek V4 Pro	$0.42	$0.84	0.14x
GLM-5	$0.20	$0.60	0.067x
Qwen 3.5 397B	$0.46	$0.92	0.15x
DeepSeek V4 Flash	$0.14	$0.28	0.047x

DeepSeek V4 Pro costs 7x less than Claude on input and 18x less on output. For output-heavy workloads, the savings are substantial.

When to Switch (and When Not To)

Switch if:

Your monthly Claude API bill exceeds $100
You're doing coding assistance, content generation, or data analysis
You can tolerate occasional quality differences on nuanced tasks
You want to run more experiments without watching the meter

Stay with Claude if:

You need the best available performance on complex multi-step reasoning
Your use case involves safety-critical content where accuracy is non-negotiable
You're invested in Claude-specific features (tool use patterns, system prompts)
Budget isn't a constraint

Model Selection

Coding → DeepSeek V4 Pro ($0.42/$0.84). Closest Claude replacement for code. For speed over depth, V4 Flash ($0.14/$0.28).
Chinese + English mixed → GLM-5 ($0.20/$0.60). Excels at Chinese language tasks with strong English.
General-purpose → Qwen 3.5 397B ($0.46/$0.92). Most balanced across task types.

Migration: Code Changes

Chinese model providers (and AIWave as a unified gateway) expose OpenAI-compatible APIs. The migration is minimal.

Before (Claude / Anthropic SDK)

import anthropic

client = anthropic.Anthropic(api_key="***")
response = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=4096,
    messages=[{"role": "user", "content": "Refactor this function"}]
)
print(response.content[0].text)

After (DeepSeek via AIWave / OpenAI SDK)

from openai import OpenAI

client = OpenAI(
    api_key="***",
    base_url="https://aiwave.live/v1"
)
response = client.chat.completions.create(
    model="deepseek-chat",
    max_tokens=4096,
    messages=[{"role": "user", "content": "Refactor this function"}]
)
print(response.choices[0].message.content)

Key differences:

Import openai instead of anthropic
Set base_url to https://aiwave.live/v1
Response: choices[0].message.content instead of content[0].text

System Prompts

Claude uses a separate system parameter. OpenAI-compatible APIs pass it in messages:

# Claude
response = client.messages.create(
    system="You are a senior Python developer",
    messages=[...]
)

# OpenAI-compatible (AIWave, DeepSeek, etc.)
response = client.chat.completions.create(
    messages=[
        {"role": "system", "content": "You are a senior Python developer"},
        {"role": "user", "content": "Refactor this function"}
    ]
)

Real Cost Comparison

A coding assistant scenario: 200 API calls per day, averaging 4K input + 2K output tokens, 22 working days per month.

Model	Monthly Cost	Annual Cost
Claude Sonnet 4	$185	$2,220
GPT-4o	$154	$1,848
DeepSeek V4 Pro	$13	$156
GLM-5	$7	$84
DeepSeek V4 Flash	$4	$48

Switching from Claude Sonnet 4 to DeepSeek V4 Pro saves roughly $172/month — over $2,000 per year. Even keeping Claude for 20% of tasks (the most complex) and routing 80% to DeepSeek saves ~$137/month.

Quality Expectations

Based on published benchmarks:

Coding: DeepSeek V4 Pro scores 92.1% on HumanEval vs Claude Sonnet 4's ~93%. Narrow gap.
Reasoning: GLM-5 and Qwen 3.5 are competitive on MATH and MMLU, within 2-5 points.
Long context: DeepSeek V4 Pro supports 1M tokens vs Claude's 200K. An advantage for large codebases.
Instruction following: Claude leads slightly on complex multi-step instructions.

Getting Started

AIWave provides a single API key for all Chinese models. No Chinese phone number or payment method required — PayPal works. There's a $1 free credit to test before committing.

The pragmatic approach: keep Claude for your most demanding tasks, route everything else to DeepSeek or GLM through the same OpenAI-compatible interface. Your code barely changes; your bill drops significantly.

OpenCode + Chinese AI Models: Setup Guide for DeepSeek, GLM & Kimi

Mattias chaw — Sun, 26 Jul 2026 15:05:51 +0000

OpenCode is a terminal-native AI coding assistant — no IDE required. You interact with it entirely through your shell, making it ideal for SSH sessions, headless servers, and developers who live in the terminal.

Chinese AI models from DeepSeek, Zhipu (GLM), and Moonshot (Kimi) are serious contenders for coding tasks at a fraction of the cost of GPT-4o or Claude. This guide covers the full setup.

Why OpenCode for Chinese Models?

OpenCode works with any OpenAI-compatible API. Since AIWave exposes Chinese models through a standard endpoint, the integration is straightforward — set a base URL and API key, then pick your model.

Practical advantages of the terminal-first approach:

SSH workflows: AI coding on remote servers without forwarding ports or VS Code Remote
Speed: No Electron overhead, responses render directly in terminal
Scripting: Pipe file contents, redirect output, chain with other CLI tools

Installation

go install github.com/opencode-ai/opencode@latest

Or without Go:

curl -fsSL https://opencode.ai/install | bash

Verify: opencode --version

Configuration

Create ~/.config/opencode/config.json:

{
  "provider": "openai",
  "base_url": "https://aiwave.live/v1",
  "api_key": "YOUR_API_KEY",
  "model": "deepseek-chat"
}

Switch models with flags:

opencode --model deepseek-chat   # Fast, cheap
opencode --model glm-5           # Stronger reasoning
opencode --model kimi-k3         # Good balance

Or define multiple in config:

{
  "provider": "openai",
  "base_url": "https://aiwave.live/v1",
  "api_key": "YOUR_API_KEY",
  "models": {
    "fast": "deepseek-chat",
    "reasoning": "glm-5",
    "balanced": "kimi-k3"
  },
  "default_model": "fast"
}

Then use opencode --model reasoning or opencode --model balanced.

Model Comparison

Model	Input (USD/1M)	Output (USD/1M)	Context	Coding
DeepSeek V4 Flash	$0.14	$0.28	1M	Excellent
GLM-5	$0.20	$0.60	128K	Very good
Kimi K3	$0.30	$0.90	128K	Good

Cost Estimates

Terminal sessions use shorter contexts than IDE workflows. Assumptions: 15 prompts per session, 1K input + 500 output tokens per prompt.

Model	Per Query	Per Session (15)	Monthly (20 sessions)
DeepSeek V4 Flash	$0.00028	$0.0042	$0.08
GLM-5	$0.00050	$0.0075	$0.15
Kimi K3	$0.00075	$0.0113	$0.23
GPT-4o (reference)	$0.00750	$0.1125	$2.25

A month of daily terminal coding with DeepSeek V4 Flash costs under 10 cents. GPT-4o costs 27x more per session. A $1 credit covers about 238 DeepSeek sessions — roughly a year of daily use.

Practical Usage

# Code generation
opencode "Add input validation to parse_config in config.py"

# Analyze logs
cat error.log | opencode "What caused this error and how to fix it?"

# Code review
git diff main | opencode "Review this diff for bugs"

Troubleshooting

Connection refused: Verify base URL ends with /v1 (no trailing slash). Test with curl first:

curl https://aiwave.live/v1/chat/completions   -H "Authorization: Bearer YOUR_API_KEY"   -H "Content-Type: application/json"   -d '{"model":"deepseek-chat","messages":[{"role":"user","content":"hello"}]}'

Model not found: Names must match exactly — deepseek-chat (not deepseek-v4-flash), glm-5 (not glm-5.1). Check AIWave models page.

Slow on large files: Truncate to relevant sections before piping. The 1M context window handles a lot, but sending 500K tokens of irrelevant log data wastes time and money.

OpenCode combined with Chinese models creates an extremely cheap terminal coding workflow. Install in under two minutes, and the $1 free credit on AIWave covers a year of daily DeepSeek V4 Flash sessions.

"How to Use DeepSeek V4 in Cursor IDE — Complete 2026 Setup Guide"

Mattias chaw — Sat, 25 Jul 2026 21:10:49 +0000

How to Use DeepSeek V4 in Cursor IDE — Complete 2026 Setup Guide

Cursor has become one of the most popular AI-native code editors, combining the familiarity of VS Code with deep AI integration. But its default model offerings — mostly OpenAI and Anthropic — add up fast when you're making hundreds of AI calls per day.

DeepSeek V4 models (Flash and Pro) provide a compelling alternative. This guide walks through the complete setup, from getting an API key to configuring Cursor for daily use.

Why DeepSeek V4 in Cursor?

Current pricing tells the story quickly:

Model	Input (USD/1M tokens)	Output (USD/1M tokens)	Context
DeepSeek V4 Flash	$0.14	$0.28	1M
DeepSeek V4 Pro	$0.435	$0.87	1M
GPT-4o	$2.50	$10.00	128K
Claude Sonnet 4	$3.00	$15.00	200K

DeepSeek pricing from official docs. GPT-4o and Claude pricing from their respective providers, current as of July 2026.

DeepSeek V4 Flash is 18x cheaper than GPT-4o on input tokens and 36x cheaper on output. For a typical Cursor session — say 200 AI calls at 4K input + 2K output each — the cost difference per query is $0.001 (V4 Flash) vs $0.03 (GPT-4o). Over a month of daily use, that's roughly $5 versus $132.

The 1M token context window is the other practical advantage: you can paste entire codebases into a single prompt. DeepSeek V4 Pro scores 92.1 on HumanEval, marginally ahead of GPT-4o's 90.2.

What You Need

Cursor IDE (v0.45+) — cursor.com
An API key from an OpenAI-compatible provider — AIWave or the official DeepSeek API
Model names — deepseek-v4-flash for speed, deepseek-v4-pro for heavy lifting

Step 1: Get Your API Key

Option A: Use AIWave (zero friction)
Sign up at aiwave.live — no Chinese phone number, no credit card for the free $1 credit. You get DeepSeek V4 Flash, V4 Pro, and 50+ other models under one API key.

Option B: Use DeepSeek directly
Go to platform.deepseek.com, register, and generate an API key. Requires a Chinese phone number for verification, which can be a barrier for international developers.

Step 2: Configure Cursor

Cursor supports any OpenAI-compatible API endpoint. Configuration takes about 60 seconds:

Open Cursor
Click the gear icon in the bottom-left to open Settings (Ctrl+,)
Navigate to the Models tab in the left sidebar
Under OpenAI API Key, paste your DeepSeek API key
Set Base URL to your provider's endpoint:
- AIWave: https://aiwave.live/v1
- Official DeepSeek: https://api.deepseek.com
Click Save

Cursor auto-discovers available models. If deepseek-v4-flash or deepseek-v4-pro don't appear in the dropdown, add them manually in the Model Names field (comma-separated).

Step 3: Pick Models by Task

Feature	Recommended	Why
Chat (Cmd+L)	V4 Flash	Fast responses for quick questions
Inline Edit (Cmd+K)	V4 Flash	Sub-second completions
Agent Mode	V4 Pro	1M context for multi-file changes
Complex Refactors	V4 Pro	Better reasoning for architecture decisions

Switch models on the fly from the AI panel dropdown.

Step 4: Quick Test

Open a Python file and press Cmd+L. Paste this prompt:

Write a function that merges two sorted lists in O(n) time.
Include type hints and a docstring.

A response should appear within 1-2 seconds. If you get an authentication error:

Check the API key for typos (common: trailing spaces)
Confirm Base URL has no trailing slash
Verify your account has balance — a single query costs roughly $0.001 on V4 Flash at 4K input + 2K output

Real-World Experience

After several weeks with DeepSeek V4 Flash as my daily Cursor model:

Autocomplete and quick edits: No noticeable quality difference from GPT-4o. The cost is low enough that I stopped second-guessing whether a query was worth it.

Large context tasks: V4 Pro's 1M context genuinely helps with understanding large files. I've dropped 4,000-line pull requests into Agent mode and gotten meaningful refactoring suggestions. GPT-4o truncates the same input.

Minor trade-off: Tab autocomplete is about 200ms slower than Claude Sonnet 4 on very short completions. You adjust within a day.

Rare quirk: Roughly 1 in 700 responses may include a stray Chinese character in English explanation text. Code output is always clean. It's a cosmetic issue in comments, not a correctness issue.

Cost Comparison (Verified Numbers)

For a solo developer at 200 Cursor AI calls per workday, averaging 4K input + 2K output per call:

Model	Cost per Query	Monthly (22 workdays)
DeepSeek V4 Flash	$0.00112	~$5
DeepSeek V4 Pro	$0.00348	~$15
GPT-4o	$0.03000	~$132
Claude Sonnet 4	$0.04200	~$185

How this is calculated: V4 Flash: (4,000 ÷ 1,000,000 × $0.14) + (2,000 ÷ 1,000,000 × $0.28) = $0.00112. Same formula for other models using their posted rates. Monthly = per-query × 200 × 22.

For a five-person team at the same usage level, the gap between DeepSeek V4 Flash and GPT-4o is roughly $25/month vs $660/month. AIWave's pricing page has the full breakdown across 60+ models.

Before You Switch

DeepSeek V4 doesn't support vision or image understanding. If your Cursor workflow depends on analyzing screenshots, keep GPT-4o as a secondary model for vision tasks.

Everything else — code generation, debugging, refactoring, documentation — works well with DeepSeek V4 Flash and Pro. The initial configuration takes one minute, and the first query will tell you everything you need to know about whether it fits your workflow.

Get started with AIWave — the free $1 credit covers roughly 900 queries on V4 Flash, more than enough for a thorough evaluation.

AI API Pricing Comparison 2026: Every Major Provider, All Numbers

Mattias chaw — Sat, 18 Jul 2026 13:00:14 +0000

With dozens of Chinese AI models available through a single API, choosing the right one for your budget is easier than you think.

AI API Pricing Comparison 2026: Every Major Provider, All Numbers

Pricing for AI APIs is a mess. Every provider uses different tiers, different billing models, and different names for similar capabilities. This article puts every major model's pricing side by side so you can stop guessing and start optimizing.

Full Price Table (USD per 1M Tokens)

Sorted by total cost for a typical 1:1 input/output ratio. All prices are current as of July 2026 from AIWave's live pricing data.

Ultra-Cheap ($0.005–$0.04/1M)

| Model | Input | Output | Context | Vendor |
|-|-|--||--|
| GLM 4.7 Flash | FREE | FREE | 128K | Zhipu AI |
| ERNIE 3.5 8K | $0.002 | $0.002 | 8K | Baidu |
| ERNIE 4.0 Turbo / Speed / Lite / Char (7 models) | $0.001 | $0.001 | 8K | Baidu |
| ERNIE 4.0 8K | $0.005 | $0.005 | 8K | Baidu |
| ERNIE 4.0 Turbo 128K | $0.018 | $0.018 | 128K | Baidu |
| DeepSeek R1 Distill Qwen 14B/32B | $0.015 | $0.031 | 128K | DeepSeek |
| GLM 4.5 Air | $0.031 | $0.031 | 8K | Zhipu AI |

Budget Tier ($0.01-0.05/1M)

| Model | Input | Output | Context | Vendor |
|-|-|--||--|
| Qwen3 8B | $0.05 | $0.40 | 128K | Alibaba |
| GLM 4.7 FlashX | $0.06 | $0.06 | 128K | Zhipu AI |
| ERNIE Speed Pro 128K | $0.063 | $0.126 | 128K | Baidu |
| Qwen3 Coder 480B | $0.12 | $0.36 | 128K | Alibaba |
| DeepSeek V4 Flash | $0.14 | $0.28 | 1M | DeepSeek |
| DeepSeek V3 / V3.2 | $0.154 | $0.308 | 128K | DeepSeek |
| GLM 4.5 | $0.151 | $0.151 | 128K | Zhipu AI |
| DeepSeek Chat | $0.164 | $0.329 | 128K | DeepSeek |
| Qwen3 32B | $0.20 | $0.60 | 128K | Alibaba |

Mid-Range ($0.05-0.15/1M)

| Model | Input | Output | Context | Vendor |
|-|-|--||--|
| DeepSeek V4 Pro | $0.42 | $0.84 | 1M | DeepSeek |
| GLM 4.6 | $0.452 | $0.452 | 128K | Zhipu AI |
| MiniMax M2.5 | $0.50 | $2.00 | 1M | MiniMax |
| GLM 4.7 | $0.60 | $2.19 | 128K | Zhipu AI |
| DeepSeek R1 | $0.605 | $2.41 | 128K | DeepSeek |

Higher-End ($0.15-0.90/1M)

| Model | Input | Output | Context | Vendor |
|-|-|--||--|
| GLM 5 | $1.00 | $3.19 | 128K | Zhipu AI |
| GLM 5 Turbo | $1.20 | $4.00 | 128K | Zhipu AI |
| GLM 5.1 | $1.40 | $4.40 | 128K | Zhipu AI |
| Kimi K2.7 Code | $1.09 | $4.60 | 128K | Moonshot |
| Qwen3.5 397B | $1.50 | $6.00 | 128K | Alibaba |
| ERNIE 5.0 / 5.1 | $2.055 | $2.055 | 8K | Baidu |
| Kimi K2.7 Code HighSpeed | $2.19 | $9.20 | 128K | Moonshot |

Western Providers (for comparison)

| Model | Input (est.) | Output (est.) | Context | Vendor |
|-|-|-||--|
| Claude 3.5 Haiku | $0.80 | $4.00 | 200K | Anthropic |
| Claude 3.5 Sonnet | $3.00 | $15.00 | 200K | Anthropic |
| GPT-4o | $2.50 | $10.00 | 128K | OpenAI |
| GPT-4o Mini | $0.15 | $0.60 | 128K | OpenAI |
| Gemini 1.5 Flash | $0.075 | $0.30 | 1M | Google |
| Gemini 1.5 Pro | $1.25 | $5.00 | 2M | Google |

Cost Calculation Formula

To estimate your monthly spend:

Monthly Cost = (Monthly Input Tokens / 1,000,000) 脳 Input Price
             + (Monthly Output Tokens / 1,000,000) 脳 Output Price

Practical example: Processing 10M input tokens and 20M output tokens per month:

Model	Monthly Cost
GLM 4.7 Flash	$0.00
DeepSeek V4 Flash	$7.00
DeepSeek V4 Pro	$21.00
GPT-4o	$225.00
Claude 3.5 Sonnet	$330.00

That's not a typo. DeepSeek V4 Pro delivers comparable (or better) benchmark scores to GPT-4o at 10.7x lower cost.

The Chinese Model Advantage

Chinese AI providers consistently undercut Western pricing by 5-50x while maintaining competitive quality:

DeepSeek V4 Pro ($0.42 input) vs GPT-4o ($2.50 input) 鈥?same ballpark benchmark performance, but DeepSeek is 6x cheaper on input tokens alone.
GLM 4.7 Flash is completely free with 128K context 鈥?no Western provider offers anything close.
ERNIE models starting at $0.001/1M tokens are cheaper than any Western option by orders of magnitude.

This isn't about compromising quality. DeepSeek V4 Pro scores 92.1 on HumanEval (vs GPT-4o's 90.2) and 90.2 on MATH (vs 76.6). You're paying less for equal or better performance.

Smart Model Selection Strategy

Always start cheap. Use GLM 4.7 Flash or ERNIE affordable models for prototyping. Minimal cost, immediate feedback.
Match model to task complexity. Don't use a $0.42/1M model for simple text classification. Use ernie-4.0-turbo-128k at $0.018/1M instead.
Use output pricing to guide model choice. If your workload is output-heavy (code generation, long responses), models with low output ratios like GLM 4.5 ($0.151/$0.151) save significantly.
Budget for context size. DeepSeek V4 Flash and V4 Pro offer 1M context windows 鈥?a feature that costs $1.25/1M input on Gemini 1.5 Pro.

Token Cost Quick Reference

For a 1K token prompt producing 500 tokens of output:

Model	Cost per Call
GLM 4.7 Flash	$0.00
DeepSeek V4 Flash	$0.00028
DeepSeek V4 Pro	$0.00084
GPT-4o	$0.00750

At 10,000 calls/day, that's $0.00 vs $2.80 vs $8.40 vs $75.00. Scale makes the difference obvious.

See the live pricing page for the most current rates across all 60+ models. Sign up to start testing with your $5 free credit. Join Discord to discuss cost optimization strategies with other developers.

Ready to put this to the test? Sign up for AIWave and get $5 free credit to try it yourself. No credit card needed.

We're a small team behind AIWave. No VC money, no big marketing budget — just a few people who believe Chinese AI models should be accessible to everyone in the world. Your API calls keep this project alive. If you find value in what we're building, stick around. It means more than you know.

AI API Pricing Comparison 2026: Every Major Provider, All Numbers

Mattias chaw — Fri, 17 Jul 2026 13:00:22 +0000

AI API Pricing Comparison 2026: Every Major Provider, All Numbers

Full Price Table (USD per 1M Tokens)

Sorted by total cost for a typical 1:1 input/output ratio. All prices are current as of July 2026 from AIWave's live pricing data.

Ultra-Cheap ($0.005–$0.04/1M)

Budget Tier ($0.01-0.05/1M)

Mid-Range ($0.05-0.15/1M)

Higher-End ($0.15-0.90/1M)

Western Providers (for comparison)

Cost Calculation Formula

To estimate your monthly spend:

Monthly Cost = (Monthly Input Tokens / 1,000,000) 脳 Input Price
             + (Monthly Output Tokens / 1,000,000) 脳 Output Price

Practical example: Processing 10M input tokens and 20M output tokens per month:

Model	Monthly Cost
GLM 4.7 Flash	$0.00
DeepSeek V4 Flash	$7.00
DeepSeek V4 Pro	$21.00
GPT-4o	$225.00
Claude 3.5 Sonnet	$330.00

That's not a typo. DeepSeek V4 Pro delivers comparable (or better) benchmark scores to GPT-4o at 10.7x lower cost.

The Chinese Model Advantage

Chinese AI providers consistently undercut Western pricing by 5-50x while maintaining competitive quality:

DeepSeek V4 Pro ($0.42 input) vs GPT-4o ($2.50 input) 鈥?same ballpark benchmark performance, but DeepSeek is 6x cheaper on input tokens alone.
GLM 4.7 Flash is completely free with 128K context 鈥?no Western provider offers anything close.
ERNIE models starting at $0.001/1M tokens are cheaper than any Western option by orders of magnitude.

This isn't about compromising quality. DeepSeek V4 Pro scores 92.1 on HumanEval (vs GPT-4o's 90.2) and 90.2 on MATH (vs 76.6). You're paying less for equal or better performance.

Smart Model Selection Strategy

Always start cheap. Use GLM 4.7 Flash or ERNIE affordable models for prototyping. Minimal cost, immediate feedback.
Match model to task complexity. Don't use a $0.42/1M model for simple text classification. Use ernie-4.0-turbo-128k at $0.018/1M instead.
Use output pricing to guide model choice. If your workload is output-heavy (code generation, long responses), models with low output ratios like GLM 4.5 ($0.151/$0.151) save significantly.
Budget for context size. DeepSeek V4 Flash and V4 Pro offer 1M context windows 鈥?a feature that costs $1.25/1M input on Gemini 1.5 Pro.

Token Cost Quick Reference

For a 1K token prompt producing 500 tokens of output:

Model	Cost per Call
GLM 4.7 Flash	$0.00
DeepSeek V4 Flash	$0.00028
DeepSeek V4 Pro	$0.00084
GPT-4o	$0.00750

At 10,000 calls/day, that's $0.00 vs $2.80 vs $8.40 vs $75.00. Scale makes the difference obvious.

VSCode + ZooCode + Chinese AI Models: Complete Setup Guide

Mattias chaw — Fri, 17 Jul 2026 05:09:45 +0000

VSCode + ZooCode + Chinese AI Models: Complete Setup Guide

Published: 2026-07-07 | Category: Developer Tools | Reading Time: 8 min

If you're a developer looking to integrate Chinese AI models into your VSCode workflow, ZooCode is the bridge you need. It's a VSCode extension that turns any OpenAI-compatible API endpoint into a full-featured coding assistant — chat, inline completions, and code generation — right inside your editor.

This guide walks you through connecting ZooCode to AIWave, an OpenAI-compatible API gateway that provides access to 60+ Chinese AI models through a single endpoint. No multiple accounts, no separate SDK installations.

Why ZooCode?

You might wonder why not just use GitHub Copilot or Cursor's built-in AI. Fair question. ZooCode's advantage is model flexibility. Copilot locks you into OpenAI's models. Cursor uses Claude by default. ZooCode lets you pick any model from any vendor — and switch between them per-task.

This matters because:

No single model is best at everything
Model quality changes monthly — the leader today may not be the leader next quarter
Cost varies dramatically between models — DeepSeek V4 Flash is free to ultra-affordable, while premium models cost more
You might want Chinese models for Chinese codebases, Western models for English documentation

ZooCode decouples your editor from your model vendor. You get the best tool for each job.

Why Chinese AI Models for Coding?

Before diving into setup, let's address why you'd use Chinese models over the usual suspects:

Cost efficiency. DeepSeek V4 Flash costs $0.14/$0.28 per million tokens (input/output) — roughly 10× cheaper than GPT-4o while scoring 89.2% on HumanEval versus GPT-4o's 90.2%. The gap is negligible; the savings are not.
Massive context windows. DeepSeek V4 Flash supports 1M token context. DeepSeek V4 Pro also supports 1M. You can paste entire codebases into context.
Strong coding benchmarks. Qwen3 Coder (480B MoE) hits 88.4% on HumanEval. GLM-5.1 from Zhipu scores 87.0%.

The trade-off? Some models have slightly weaker English prose quality. For pure code generation, that's rarely an issue.

Prerequisites

VSCode (latest stable)
ZooCode extension — install from the VSCode Extensions Marketplace (search "ZooCode")
AIWave account — sign up here (new accounts get $5 free credit)
Your API key — found in the AIWave dashboard after login

Step 1: Get Your AIWave API Key

Go to https://aiwave.live and log in (GitHub, Discord, Passkey, or Email)
Navigate to the dashboard and copy your API key
Note the Base URL: https://aiwave.live/v1

This Base URL is critical. ZooCode needs it to route requests through AIWave's OpenAI-compatible proxy.

Step 2: Configure ZooCode in VSCode

Open VSCode and launch ZooCode:

Open Command Palette: Ctrl+Shift+P (Windows/Linux) or Cmd+Shift+P (macOS)
Search: ZooCode: Open Settings
The settings panel opens in a new VSCode tab

Enter the following values:

API Provider: OpenAI Compatible
Base URL: https://aiwave.live/v1
API Key: [paste your AIWave API key]

Alternatively, if ZooCode supports JSON configuration, edit your settings file directly:

{
  "zoocode.provider": "openai-compatible",
  "zoocode.baseURL": "https://aiwave.live/v1",
  "zoocode.apiKey": "sk-your-aiwave-key-here",
  "zoocode.defaultModel": "deepseek-v4-flash",
  "zoocode.codeModel": "qwen3-coder-480b-a35b-instruct",
  "zoocode.maxTokens": 8192,
  "zoocode.temperature": 0.3,
  "zoocode.enableInlineCompletions": true,
  "zoocode.enableChat": true
}

Step 3: Model Selection Strategy

Not all models are equal for all tasks. Here's the setup that works best:

Default Model: DeepSeek V4 Flash

Model ID: deepseek-v4-flash
Pricing: $0.14 input / $0.28 output per 1M tokens
Context: 1M tokens
HumanEval: 89.2%
Why it's the default: Best cost-to-performance ratio for general tasks. 1M context means you can throw entire project files at it. At these prices, you won't think twice about using it.

Code Model: Qwen3 Coder 480B

Model ID: qwen3-coder-480b-a35b-instruct
Pricing: $0.12 input / $0.36 output per 1M tokens
Context: 128K tokens
HumanEval: 88.4%
Why for coding: Alibaba's Qwen3 Coder is specifically trained for code. It's the most cost-effective dedicated coding model available. Even cheaper than DeepSeek V4 Flash on input tokens.

Free Fallback: GLM-4.7 Flash

Model ID: glm-4.7-flash
Pricing: FREE ($0.00/$0.00)
Context: 128K tokens
HumanEval: 72.5%
When to use: Quick questions, documentation lookups, non-critical tasks. It's completely free — use it liberally.

Step 4: ZooCode UI Walkthrough

Once configured, here's how to use ZooCode day-to-day:

Chat Panel

Open ZooCode Chat: Click the ZooCode icon in the sidebar (or Ctrl+Shift+Z)
Select model: Use the dropdown at the top of the chat panel. Your configured models appear here.
Ask questions: Type naturally. "Explain this function," "Find the bug in this file," "Write unit tests for this module."

Inline Completions

ZooCode offers ghost-text suggestions as you type. To configure:

Open Settings (Ctrl+,)
Search "ZooCode"
Toggle Enable Inline Completions on
Set the code model to qwen3-coder-480b-a35b-instruct

The suggestions appear in greyed-out text. Press Tab to accept, Esc to dismiss.

File Context

ZooCode lets you attach files to your chat:

In the chat panel, click the + button
Select files from your workspace
The file contents are included in the API request context
With DeepSeek V4 Flash's 1M context window, you can attach dozens of files simultaneously

Step 5: Testing Your Setup

Create a test file to verify everything works:

# test_zoocode.py
def fibonacci(n):
    """Return the nth Fibonacci number."""
    if n <= 1:
        return n
    return fibonacci(n - 1) + fibonacci(n - 2)

Open the ZooCode chat and ask:

"Optimize this fibonacci function for performance. The naive recursive approach is O(2^n)."

Expected response should include memoization or dynamic programming:

from functools import lru_cache

@lru_cache(maxsize=None)
def fibonacci(n):
    """Return the nth Fibonacci number. Memoized O(n)."""
    if n <= 1:
        return n
    return fibonacci(n - 1) + fibonacci(n - 2)

Or an iterative version:

def fibonacci(n):
    """Return the nth Fibonacci number. Iterative O(n)."""
    if n <= 1:
        return n
    a, b = 0, 1
    for _ in range(2, n + 1):
        a, b = b, a + b
    return b

If you get a coherent, correct response, your setup is working.

Cost Comparison: How Much Will You Actually Spend?

Let's estimate a typical workday:

Task	Tokens (avg)	Model	Cost per task
Chat question	2K in / 1K out	DeepSeek V4 Flash	~$0.0001
Code generation	5K in / 2K out	Qwen3 Coder	~$0.0002
Code review	10K in / 3K out	DeepSeek V4 Flash	~$0.0003
Documentation	8K in / 4K out	DeepSeek V4 Flash	~$0.0003

Daily total (50 interactions): ~$0.015 — roughly $0.45/month for a full-time developer. Compare that to GitHub Copilot at $10/month or Claude Pro at $20/month.

Troubleshooting

"API key not recognized": Double-check you're using your AIWave key, not a key from another provider. The Base URL must be exactly https://aiwave.live/v1.

"Model not found": The model ID must match AIWave's catalog exactly. Use deepseek-v4-flash, not deepseek-v4-flash-2025-06-01 or other variant names.

Slow responses: Chinese models are served from Singapore. If you're in Europe or the Americas, expect 200-500ms added latency. For latency-sensitive work, consider using the free GLM-4.7 Flash ($0.00/1M) for quick tasks.

Next Steps

Browse the full AIWave model catalog to discover more models
Check pricing details — AIWave offers a "Deposit $10 Get $20" promotion
Join the AIWave Discord community for setup help and model recommendations

Chinese AI models have closed the quality gap while maintaining dramatic price advantages. There's never been a better time to integrate them into your daily workflow.

Ready to level up your coding workflow?

Sign up for AIWave - get $5 free credit, no credit card required.

View Pricing - see how AIWave compares to the big providers.

Join our Discord - ask questions, share tips, get help from the community.

AI API Pricing Comparison 2026: Every Major Provider, All Numbers

Mattias chaw — Thu, 16 Jul 2026 13:00:23 +0000

AI API Pricing Comparison 2026: Every Major Provider, All Numbers

Full Price Table (USD per 1M Tokens)

Sorted by total cost for a typical 1:1 input/output ratio. All prices are current as of July 2026 from AIWave's live pricing data.

Ultra-Cheap ($0.005–$0.04/1M)

Budget Tier ($0.01-0.05/1M)

Mid-Range ($0.05-0.15/1M)

Higher-End ($0.15-0.90/1M)

Western Providers (for comparison)

Cost Calculation Formula

To estimate your monthly spend:

Monthly Cost = (Monthly Input Tokens / 1,000,000) 脳 Input Price
             + (Monthly Output Tokens / 1,000,000) 脳 Output Price

Practical example: Processing 10M input tokens and 20M output tokens per month:

Model	Monthly Cost
GLM 4.7 Flash	$0.00
DeepSeek V4 Flash	$7.00
DeepSeek V4 Pro	$21.00
GPT-4o	$225.00
Claude 3.5 Sonnet	$330.00

That's not a typo. DeepSeek V4 Pro delivers comparable (or better) benchmark scores to GPT-4o at 10.7x lower cost.

The Chinese Model Advantage

Chinese AI providers consistently undercut Western pricing by 5-50x while maintaining competitive quality:

DeepSeek V4 Pro ($0.42 input) vs GPT-4o ($2.50 input) 鈥?same ballpark benchmark performance, but DeepSeek is 6x cheaper on input tokens alone.
GLM 4.7 Flash is completely free with 128K context 鈥?no Western provider offers anything close.
ERNIE models starting at $0.001/1M tokens are cheaper than any Western option by orders of magnitude.

This isn't about compromising quality. DeepSeek V4 Pro scores 92.1 on HumanEval (vs GPT-4o's 90.2) and 90.2 on MATH (vs 76.6). You're paying less for equal or better performance.

Smart Model Selection Strategy

Always start cheap. Use GLM 4.7 Flash or ERNIE affordable models for prototyping. Minimal cost, immediate feedback.
Match model to task complexity. Don't use a $0.42/1M model for simple text classification. Use ernie-4.0-turbo-128k at $0.018/1M instead.
Use output pricing to guide model choice. If your workload is output-heavy (code generation, long responses), models with low output ratios like GLM 4.5 ($0.151/$0.151) save significantly.
Budget for context size. DeepSeek V4 Flash and V4 Pro offer 1M context windows 鈥?a feature that costs $1.25/1M input on Gemini 1.5 Pro.

Token Cost Quick Reference

For a 1K token prompt producing 500 tokens of output:

Model	Cost per Call
GLM 4.7 Flash	$0.00
DeepSeek V4 Flash	$0.00028
DeepSeek V4 Pro	$0.00084
GPT-4o	$0.00750

At 10,000 calls/day, that's $0.00 vs $2.80 vs $8.40 vs $75.00. Scale makes the difference obvious.

How to Connect SillyTavern to Kimi K2.6 for 128K Context Roleplay

Mattias chaw — Wed, 15 Jul 2026 17:02:01 +0000

How to Connect SillyTavern to Kimi K2.6 for 128K Context Roleplay

Published: 2026-07-07 | Category: AI Roleplay | Reading Time: 9 min

SillyTavern is the go-to front-end for AI-powered roleplay and chat. By default, it connects to OpenAI, Claude, or local models. But there's a compelling alternative: Kimi K2.6 from Moonshot AI, available through AIWave at a fraction of the cost of Claude or GPT-4o, with 128K context support that transforms long-form roleplay.

This guide covers the complete setup — API configuration, prompt engineering for roleplay, and optimization tips specific to Kimi K2.6.

Why Kimi K2.6 for Roleplay?

Kimi K2.6 stands out for roleplay for three reasons:

128K context window. At $1.09 input / $4.5998 output per 1M tokens, you get Claude-level context at a fraction of the cost. Claude 4 Sonnet charges $3/15 for 200K context. Kimi gives you 128K for pennies.
Strong instruction following. Kimi K2.6 scores 84.5% on HumanEval and 83.5% on MMLU — these aren't toy benchmarks. Strong instruction following translates directly to consistent character portrayal and adherence to scene rules.
Chinese model heritage. Moonshot AI (Kimi's creator) invested heavily in long-context training. Their models handle extended conversations better than most Western alternatives at this price point.

The trade-off: Kimi's English prose can occasionally feel slightly formal compared to Claude's naturalistic style. This is improving with each version, and proper prompt engineering closes most of the gap.

Prerequisites

SillyTavern — download from sillytavernai.com
Node.js 18+ — required to run SillyTavern
AIWave account — sign up ($5 free credit on signup)
Your API key — from the AIWave dashboard

Step 1: Start SillyTavern

# Clone or extract SillyTavern
cd SillyTavern
npm install
node server.js

SillyTavern launches at http://localhost:8000. Open it in your browser.

Step 2: Configure the API Connection

In SillyTavern's UI:

Click the plug icon (API connection) in the top toolbar
Select Chat Completion as the API type
Select Chat Completion (Custom/OpenAI format) as the backend

Enter the connection details:

API Type: Chat Completion
Backend: Custom/OpenAI Compatible
Custom Endpoint (Base URL): https://aiwave.live/v1?utm_source=dev.to&utm_medium=organic&utm_campaign=SEO_ARTICLES
API Key: sk-your-aiwave-key-here
Model: kimi-k2.6

Click Connect. If successful, SillyTavern will show a green status indicator.

Verifying the Connection

Send a test message in any chat:

"System test. Respond with: connection verified."

If Kimi responds correctly, you're connected.

Step 3: Chat Completion Settings

Configure the completion parameters for roleplay. Navigate to Settings → Chat Completion → Parameters:

Parameter	Recommended Value	Notes
Max Tokens	1024–2048	Longer responses for richer roleplay
Temperature	0.8–1.0	Higher for creative, varied responses
Top P	0.9	Standard for creative writing
Frequency Penalty	0.0–0.2	Slight penalty reduces repetition
Presence Penalty	0.0–0.2	Encourages diverse vocabulary

For roleplay specifically, temperature is the most impactful setting. Start at 0.85 and adjust:

0.7–0.8: More consistent, less surprising. Good for strict character adherence.
0.85–0.95: Balanced creativity. Good for most roleplay.
1.0+: Wild and unpredictable. Can break character but produces surprising moments.

Step 4: System Prompt Configuration

The system prompt is where Kimi K2.6's instruction following shines. Here's a battle-tested template:

You are {{char}}, a {{char description}}.
Write {{char}}'s next response in an immersive, third-person narrative.
Follow these rules strictly:
- Stay in character at all times. Never break the fourth wall.
- Use descriptive prose. Show actions, thoughts, and dialogue.
- Maintain consistent personality traits: {{char personality}}.
- React to {{user}}'s actions and words naturally.
- Keep responses between 3-8 paragraphs unless the scene demands more.
- Use quotation marks for dialogue, italics for thoughts (*like this*).
- Do not speak or act for {{user}}. Only describe {{char}}.
- If the scene becomes romantic or intimate, progress naturally based on established consent and tone.

This template works well with Kimi because it gives clear, unambiguous rules that the model can follow consistently across a long conversation.

Step 5: Character Card Best Practices for Kimi

Kimi K2.6 responds well to structured character cards. Here's an optimal format:

Name: {{char name}}
Age: {{age}}
Appearance: {{2-3 sentence physical description}}
Personality: {{3-5 key traits, briefly explained}}
Background: {{2-3 sentences of relevant history}}
Speaking Style: {{how the character talks — formal? casual? dialect?}}
Relationship to User: {{how they know the user character}}

Keep character descriptions concise. Kimi works better with 200-400 word character definitions than 2000-word ones. Excessive detail causes the model to spread its attention thin; concise definitions keep the character sharp.

Step 6: Managing the 128K Context Window

128K tokens sounds like a lot — and it is. But in roleplay, context fills fast. Here's how to manage it:

Token Estimation

1 English word ≈ 1.3 tokens
128K tokens ≈ 98,000 words ≈ a 300-page novel
A typical roleplay message: 50–200 words ≈ 65–260 tokens
Character card + system prompt: 400–800 tokens

Context Strategy

SillyTavern handles context management automatically, but you should configure it:

Settings → Chat Completion → Context
Set Max Context Length to 120000 (leaving headroom for responses)
Set Context Template to include:
- System prompt
- Character card
- Chat history (as many messages as fit)

When Context Gets Long

After ~200-300 exchanges (depending on message length), you'll approach the 128K limit. Options:

Enable Summarization: SillyTavern can summarize older messages. Navigate to Extensions → Summarize and set it to summarize messages older than 50 exchanges.
Start a new chapter: If the narrative shifts, start a fresh chat with a summary of prior events as the first message.
Use the free GLM-4.7 Flash ($0.00/1M) for less important scenes, saving Kimi K2.6 for key moments.

Step 7: Prompt Engineering for Better Roleplay

Beyond the system prompt, a few techniques improve Kimi's output:

"Show, Don't Tell" Injection

Add to the end of your system prompt:

Writing style: Show actions through vivid description rather than stating them. 
Instead of "She was nervous," write "Her fingers drummed against the table, 
her eyes darting to the door every few seconds."

Scene Anchoring

When starting a new scene, include an explicit scene-setting message from the user:

[The tavern is dimly lit, candles flickering on rough-hewn tables. Rain 
patters against the window. A stranger pushes through the door, shaking 
water from their cloak.]

Physical environment details give Kimi concrete details to work with, reducing hallucination.

Pacing Control

If Kimi's responses are too long or too short, add to the system prompt:

Response length: Write 3-5 paragraphs per response. Match the pacing 
of the scene — short responses during action, longer during emotional moments.

Cost Breakdown

A typical roleplay session:

Activity	Tokens	Cost (Kimi K2.6)
System prompt + char card	~800	~$0.0002
20 exchanges (avg 150 tokens each)	~6,000	~$0.0036
Total per session	~6,800	~$0.004

At $0.004 per session, you can have 250 sessions for $1. Compare that to Claude 4 Sonnet, which would cost roughly $0.30–$0.50 per equivalent session.

Troubleshooting

"Model not found" error: Ensure you're using exactly kimi-k2.6 as the model name. AIWave's model catalog uses specific identifiers.

Responses feel robotic: Increase temperature to 0.85–0.95. Also review your character card — Kimi responds best to concise, well-structured definitions.

Context filling too fast: Enable SillyTavern's summarization feature. Consider using the free GLM-4.7 Flash ($0.00/1M) for scenes that don't need Kimi's full capabilities.

API errors or timeouts: AIWave hosts from Singapore. If you're in Europe or the Americas, you may see occasional latency spikes. For critical roleplay sessions, a wired connection helps.

Alternative Models to Try

AIWave offers other models worth experimenting with for roleplay:

Moonshot V1 128K (moonshot-v1-128k) — Kimi's predecessor, $1.80/$4.50. Good fallback.
DeepSeek V3.2 (deepseek-v3.2) — $0.154/$0.308, 128K context. Surprisingly good at roleplay for the price.
GLM-4.7 Flash (glm-4.7-flash) — $0.00/1M, 128K. Free to use. Not as polished, but usable for background scenes.

Next Steps

Create your AIWave account to get started with $5 free credit
Browse the AIWave pricing page for plan details
Join the AIWave Discord — there's an active roleplay community sharing prompts and configurations

Kimi K2.6 on AIWave offers the best value proposition for AI roleplay in 2026. Claude-quality context at a fraction of the cost, with 128K tokens that let your stories breathe.

AI API Pricing Comparison 2026: Every Major Provider, All Numbers

Mattias chaw — Wed, 15 Jul 2026 13:00:22 +0000

AI API Pricing Comparison 2026: Every Major Provider, All Numbers

Full Price Table (USD per 1M Tokens)

Sorted by total cost for a typical 1:1 input/output ratio. All prices are current as of July 2026 from AIWave's live pricing data.

Ultra-Cheap ($0.005–$0.04/1M)

Budget Tier ($0.01-0.05/1M)

Mid-Range ($0.05-0.15/1M)

Higher-End ($0.15-0.90/1M)

Western Providers (for comparison)

Cost Calculation Formula

To estimate your monthly spend:

Monthly Cost = (Monthly Input Tokens / 1,000,000) 脳 Input Price
             + (Monthly Output Tokens / 1,000,000) 脳 Output Price

Practical example: Processing 10M input tokens and 20M output tokens per month:

Model	Monthly Cost
GLM 4.7 Flash	$0.00
DeepSeek V4 Flash	$7.00
DeepSeek V4 Pro	$21.00
GPT-4o	$225.00
Claude 3.5 Sonnet	$330.00

That's not a typo. DeepSeek V4 Pro delivers comparable (or better) benchmark scores to GPT-4o at 10.7x lower cost.

The Chinese Model Advantage

Chinese AI providers consistently undercut Western pricing by 5-50x while maintaining competitive quality:

DeepSeek V4 Pro ($0.42 input) vs GPT-4o ($2.50 input) 鈥?same ballpark benchmark performance, but DeepSeek is 6x cheaper on input tokens alone.
GLM 4.7 Flash is completely free with 128K context 鈥?no Western provider offers anything close.
ERNIE models starting at $0.001/1M tokens are cheaper than any Western option by orders of magnitude.

This isn't about compromising quality. DeepSeek V4 Pro scores 92.1 on HumanEval (vs GPT-4o's 90.2) and 90.2 on MATH (vs 76.6). You're paying less for equal or better performance.

Smart Model Selection Strategy

Always start cheap. Use GLM 4.7 Flash or ERNIE affordable models for prototyping. Minimal cost, immediate feedback.
Match model to task complexity. Don't use a $0.42/1M model for simple text classification. Use ernie-4.0-turbo-128k at $0.018/1M instead.
Use output pricing to guide model choice. If your workload is output-heavy (code generation, long responses), models with low output ratios like GLM 4.5 ($0.151/$0.151) save significantly.
Budget for context size. DeepSeek V4 Flash and V4 Pro offer 1M context windows 鈥?a feature that costs $1.25/1M input on Gemini 1.5 Pro.

Token Cost Quick Reference

For a 1K token prompt producing 500 tokens of output:

Model	Cost per Call
GLM 4.7 Flash	$0.00
DeepSeek V4 Flash	$0.00028
DeepSeek V4 Pro	$0.00084
GPT-4o	$0.00750

At 10,000 calls/day, that's $0.00 vs $2.80 vs $8.40 vs $75.00. Scale makes the difference obvious.