Kimi K2 Thinking for OpenClaw: Pricing, Setup, and What It's Good At

Current as of March 2026. Kimi K2 Thinking is Moonshot AI’s reasoning-heavy model that positions itself as a budget-friendly alternative to O1-preview for long-context tasks. At $0.47 per million input tokens, it offers a 131K window that holds up well during complex agentic workflows in OpenClaw.

Specs

Provider	Moonshot AI
Input cost	$0.47 / M tokens
Output cost	$2.00 / M tokens
Context window	131K tokens
Max output	N/A tokens
Parameters	N/A
Features	function_calling, reasoning

What it’s good at

Deep Logic

The reasoning feature allows it to tackle multi-step problems that usually trip up standard LLMs at this price point.

Context Stability

It manages the 131K token window effectively, showing less ‘middle-of-the-prompt’ forgetfulness than many competitors in the same tier.

Where it falls short

Slow Inference

The thinking phase adds significant latency, making it unsuitable for applications requiring instant feedback.

Regional Latency

API calls to api.moonshot.cn can be inconsistent for users outside of mainland China or Southeast Asia.

Best use cases with OpenClaw

• Complex Debugging — Use this when your OpenClaw agent needs to analyze a large stack trace against a wide context of source code within its 131K window.
• Logic-Heavy Planning — It’s ideal for agents that must generate a detailed execution plan before calling external tools or functions.

Not ideal for

• Simple UI Text — The $2 per million output cost is a waste on basic copy generation or greeting users that doesn’t require reasoning.
• Latency-Sensitive Tools — Avoid this for any interactive tool where the user expects a response in under 500ms due to the model’s ‘thinking’ overhead.

OpenClaw setup

Configure a custom provider in OpenClaw using the base URL https://api.moonshot.cn/v1 and the model ID moonshotai/kimi-k2-thinking.

{
  "models": {
    "mode": "merge",
    "providers": {
      "moonshotai": {
        "baseUrl": "https://api.moonshot.cn/v1",
        "apiKey": "YOUR-MOONSHOT-AI-API-KEY",
        "api": "openai-completions",
        "models": [
          {
            "id": "kimi-k2-thinking",
            "name": "Kimi K2 Thinking",
            "cost": {
              "input": 0.47,
              "output": 2
            },
            "contextWindow": 131072,
            "maxTokens": null
          }
        ]
      }
    }
  }
}

How it compares

• vs DeepSeek-V3 — DeepSeek is often cheaper on output, but Kimi’s 131K context window feels more robust for long-form reasoning tasks.
• vs GPT-4o-mini — GPT-4o-mini is significantly faster for basic tasks, but it lacks the specialized reasoning architecture that Kimi K2 uses for hard problems.

Bottom line

If you need O1-style reasoning with a large context window but only have a fraction of the budget, Kimi K2 Thinking is the best option for your OpenClaw agents.

TRY KIMI K2 THINKING ON HAIMAKER

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For setup instructions, see our API key guide. For all available models, see the complete models guide.