Kimi K2.7 Code is Moonshot AI’s newest open-weight coding model — 1 trillion total parameters, 32B active parameters in MoE architecture, 256K context window, and native support in OpenClaw v2026.6.7-beta.1 onward. If you’re running agentic coding workflows and want to try a capable open-weight model with long-context reliability, here’s how to get started.

Accuracy note: This guide uses confirmed information from the official Kimi API Platform documentation (platform.kimi.ai/docs) and the Kimi K2.7 Code quickstart guide. OpenClaw configuration commands are not included because specific config key names are not publicly documented — refer to the OpenClaw documentation and the in-app config system for provider setup instructions.

What You’re Getting

Before diving in, understand what K2.7 Code is designed for:

  • Long-horizon coding tasks: Reliable generalization across Rust, Go, Python, and other languages — frontend, DevOps, performance optimization
  • 256K context window: Shared with K2.6 and K2.5; important for large codebases
  • Thinking-only mode: K2.7 Code does not support non-thinking mode — it always reasons before responding. This is by design for coding accuracy, but it means you should expect higher latency than non-thinking models.
  • ~30% fewer thinking tokens than K2.6: The reasoning budget is more efficient, even though you can’t turn it off.

Benchmark caveat: Moonshot’s published improvements (+21.8% on Kimi Code Bench v2) are from their own internal benchmarks. Independent third-party SWE-Bench Verified results were not widely available at launch time. Treat those numbers as directional, not definitive.

Step 1: Get a Kimi API Key

Kimi K2.7 Code is available via Moonshot’s OpenAI-compatible API. To access it:

  1. Go to platform.kimi.ai and create an account
  2. Navigate to the API Keys section in your console
  3. Create a new API key and copy it — you’ll need this to configure OpenClaw

The API is available at: https://api.moonshot.ai/v1 (OpenAI-compatible endpoint).

The model ID is: kimi-k2.7-code

Step 2: Verify the SDK Works (Optional Sanity Check)

If you want to test the API directly before configuring OpenClaw, the Kimi API uses OpenAI SDK format:

# Install OpenAI SDK if you haven't already
# pip install --upgrade 'openai>=1.0'

import openai

client = openai.OpenAI(
    api_key="YOUR_KIMI_API_KEY",
    base_url="https://api.moonshot.ai/v1"
)

response = client.chat.completions.create(
    model="kimi-k2.7-code",
    messages=[
        {"role": "user", "content": "Write a Python function that finds all prime numbers up to n using the Sieve of Eratosthenes."}
    ]
)

print(response.choices[0].message.content)

This uses the confirmed OpenAI SDK integration documented in the official Kimi K2.7 Code quickstart guide.

Step 3: Configure OpenClaw to Use Kimi K2.7 Code

OpenClaw v2026.6.7-beta.1 adds native Kimi K2.7 Code support, including fixes for tool-call IDs and reasoning_content replay that matter in long agentic loops.

To configure the Kimi provider in OpenClaw:

  1. Upgrade to v2026.6.7-beta.1 or later. Earlier versions don’t have Kimi K2.7 Code in the provider catalog.
  2. Use the OpenClaw config UI or config system to add your Kimi API key. The exact config key paths are not publicly documented — refer to the OpenClaw documentation or the in-app setup flow for the Moonshot/Kimi provider.
  3. Select the model as kimi-k2.7-code when creating or configuring your agent.

For the specific config steps in your version of OpenClaw, check the in-app help or the OpenClaw GitHub releases page for the v2026.6.7 changelog — it includes integration notes for the Kimi provider.

Step 4: Design Your Agentic Coding Workflow

K2.7 Code performs best when the task structure plays to its strengths:

Good fits:

  • Code review and refactoring tasks where full-file context matters
  • Multi-file changes that require understanding dependencies
  • Long-running debugging sessions where reasoning about the state space helps
  • Tasks involving languages where the model shows strong generalization (Rust, Go, Python, frontend)

Worth knowing:

  • Because K2.7 Code is thinking-only, every response includes a reasoning phase. Budget for this in latency-sensitive workflows.
  • The 256K context window is large but not infinite — for very large monorepos, you’ll still need to be thoughtful about what context you include.

Where to Run It

Beyond the Kimi API, K2.7 Code is available through several inference providers:

  • Kimi API Platform: platform.kimi.ai — direct access, confirmed OpenAI-compatible
  • Hugging Face: Open weights available for self-hosting
  • Cloudflare Workers AI: Available in the Cloudflare Workers AI catalog
  • Fireworks AI: Listed in the Fireworks model catalog
  • Ollama: Available for local inference (resource requirements significant for a 32B-active/1T-total MoE)

For most OpenClaw users, the Kimi API is the lowest-friction path. For teams with strong data sovereignty requirements, the HuggingFace open weights + self-hosted inference route is viable but requires meaningful hardware.

Pricing (as of June 2026)

From the Kimi API Platform:

  • Cache Hit: $0.19 / MTok
  • Input: $0.95 / MTok
  • Output: $4.00 / MTok

These are the published rates at launch; check platform.kimi.ai for current pricing.

Sources

  1. Kimi K2.7 Code Quickstart — Kimi API Platform
  2. Kimi API Platform — Model Overview
  3. OpenClaw v2026.6.7-beta.1 Release Notes
  4. Kimi K2.7 Code on Cloudflare Workers AI

Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260613-0800

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