Best Kimi Models for OpenClaw — Moonshot AI's Long-Context Models

The best Kimi model for OpenClaw is Kimi K2.5, Moonshot AI's trillion-parameter MoE model released on January 27, 2026. It delivers a 256K context window, native multimodal support, and API pricing at $0.60 per million input tokens, making it one of the most cost-effective frontier models available for agent workflows.

Part of The Complete Guide to OpenClaw — the full reference covering setup, security, memory, and operations.

Why Kimi for OpenClaw?

Kimi is Moonshot AI's flagship model family, built from the start around ultra-long context processing. While most frontier models top out at 128K-200K context windows, Kimi K2.5 provides 256K tokens natively, and the underlying architecture was designed to scale context without the accuracy degradation that plagues many competitors at high token counts.

For OpenClaw operators, this matters because agent workflows accumulate context fast. Tool calls, system prompts, conversation history, document references, and multi-step reasoning all consume tokens. A model that handles 256K tokens reliably means fewer session resets, fewer lost instructions, and more coherent multi-step execution.

Moonshot AI is based in Beijing and the Kimi models support both Chinese and English natively. According to the Kimi K2.5 quickstart documentation, the model is optimized for dialogue, coding, reasoning, and autonomous agent tasks. The API follows the OpenAI format, which means OpenClaw integration requires minimal configuration.

Kimi Model Specs Comparison

Moonshot AI has released several Kimi models over the past year. The table below compares the models most relevant to OpenClaw operators, based on Kimi's official pricing page and public benchmark data.

Kimi K2.5 is the clear winner for new OpenClaw deployments. It supersedes both K2 and K1.5 across context length, benchmark scores, and multimodal capability. Moonshot AI officially deprecated the older kimi-latest alias on January 28, 2026, directing all users to K2.5.

Kimi API Setup for OpenClaw

The Kimi API uses the OpenAI SDK format. You can get an API key from the Kimi Open Platform and connect it to OpenClaw with three environment variables.

export OPENAI_API_KEY="your-kimi-api-key"
export OPENAI_BASE_URL="https://api.moonshot.ai/v1"
export OPENAI_MODEL="kimi-k2.5"

That is all you need. OpenClaw will route all requests through the Kimi API using the standard OpenAI-compatible chat completions endpoint.

Connecting via OpenRouter

If you prefer a single API key for multiple providers, Kimi K2.5 is also available on OpenRouter. Use the model ID moonshotai/kimi-k2.5:

export OPENAI_API_KEY="your-openrouter-api-key"
export OPENAI_BASE_URL="https://openrouter.ai/api/v1"
export OPENAI_MODEL="moonshotai/kimi-k2.5"

For a full walkthrough of OpenRouter configuration, see the OpenClaw OpenRouter setup guide.

Long-Context Advantages for OpenClaw

Kimi K2.5's 256K context window is one of the largest available in a frontier API model at this price point. For OpenClaw, long context translates directly into better agent performance in several ways.

Fewer session resets. OpenClaw agents that run multi-step workflows, call multiple tools, and maintain conversation history can easily exceed 64K tokens in a single session. With 256K available, the agent can maintain coherence across longer workflows without hitting context limits and losing earlier instructions.

Better document ingestion. If your OpenClaw workflow involves processing long documents, contracts, codebases, or research papers, Kimi K2.5 can ingest significantly more content in a single pass than models limited to 128K.

Automatic context caching. The Kimi platform includes built-in context caching that reduces input costs by up to 75% on repeated prompt prefixes. For OpenClaw agents that use consistent system prompts and skill definitions, this means the effective cost per session drops significantly after the first request.

The benchmark evidence supports real-world performance at long context lengths. Kimi K2.5 scored 96.1% on AIME 2025 and 76.8% on SWE-Bench Verified according to Artificial Analysis, showing that the model maintains strong reasoning even when processing large amounts of context.

Cost Analysis

Kimi K2.5 is significantly cheaper than most frontier alternatives while delivering competitive benchmark scores. The table below puts Kimi pricing in context against models commonly used with OpenClaw.

At $0.60 per million input tokens, Kimi K2.5 costs roughly 4x less than GPT-4o and 5x less than Claude Sonnet on input, with a larger context window than either. The automatic caching further reduces effective input costs for recurring OpenClaw sessions.

The main cost competitor at the budget tier is Grok 3 Mini ($0.30/$0.50), which is cheaper on raw token pricing but has a smaller context window and no built-in caching. For operators who need long context, Kimi K2.5 is the better value. For more on OpenClaw cost optimization across all providers, see How Much Does OpenClaw Cost.

Limitations and Tradeoffs

Kimi K2.5 is a strong choice, but it is not the right model for every OpenClaw operator.

• No local deployment: Unlike open-weight models such as Llama or Qwen, Kimi K2.5 is only available through cloud APIs. If you need fully local or air-gapped operation, look at Ollama-hosted models instead.
• China-based infrastructure: The Kimi API is served from Moonshot AI's infrastructure in China. Some operators may face latency depending on their geographic location, and some organizations have policies restricting data routing through Chinese servers. OpenRouter can mitigate this for operators who prefer a US-based proxy.
• Smaller ecosystem: Compared to OpenAI or Anthropic, Moonshot AI has a smaller developer community and fewer third-party integrations. Documentation is available in both English and Chinese, but the English documentation is less comprehensive in some areas.
• Output pricing is moderate: At $2.50 per million output tokens, Kimi K2.5 is not the cheapest option for output-heavy workflows. Models like Grok 3 Mini ($0.50 output) or DeepSeek V3 ($1.10 output) are cheaper for tasks that generate large volumes of text.

Related Guides

• Kimi K2.5 OpenClaw Guide
• OpenClaw OpenRouter Setup
• Best Ollama Models for OpenClaw
• How to Set Up OpenClaw AI Agent

FAQ

What is the best Kimi model for OpenClaw in 2026?

Kimi K2.5 is the best Kimi model for OpenClaw as of April 2026. It offers a 256K context window, native multimodal support, and pricing at $0.60 per million input tokens. Moonshot AI deprecated all older Kimi model aliases in January 2026 in favor of K2.5.

How do I connect Kimi to OpenClaw?

Set OPENAI_API_KEY to your Kimi API key from platform.moonshot.ai, set OPENAI_BASE_URL to https://api.moonshot.ai/v1, and set OPENAI_MODEL to kimi-k2.5. The API is OpenAI-compatible, so no custom integration is needed.

Does Kimi K2.5 support both English and Chinese?

Yes. Kimi K2.5 is natively bilingual, trained on both English and Chinese data. It handles both languages fluently for dialogue, reasoning, coding, and document analysis tasks.

How does Kimi K2.5 compare to GPT-4o for OpenClaw?

Kimi K2.5 costs roughly 4x less than GPT-4o on input tokens ($0.60 vs $2.50 per million) and offers a larger context window (256K vs 128K). On benchmarks, K2.5 scores 96.1% on AIME 2025 versus GPT-4o's lower scores, and 76.8% on SWE-Bench Verified. The main tradeoff is ecosystem maturity and geographic infrastructure.

Is Kimi K2.5 available on OpenRouter?

Yes. Kimi K2.5 is available on OpenRouter under the model ID moonshotai/kimi-k2.5. This lets you use Kimi alongside other models through a single API key and US-based routing.