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Moonshot AI's 1T-parameter open-weights MoE coding and agentic model with mandatory extended thinking and a 262,144-token context window.
Overview. Kimi K2.7 Code is Moonshot AI's coding- and agentic-focused model, built on the Kimi K2.6 architecture and released as open weights under a Modified-MIT licence. It's a 1-trillion-parameter mixture-of-experts model with 384 experts and 32 billion active parameters per token, paired with a 262,144-token context window. Developers working on multi-step coding and agentic tool-use tasks that need a lot of context to reason over would reach for this model.
Key strengths. Its coding capability is the standout signal: Moonshot AI positions it explicitly as a coding-focused agentic model, and its measured coding benchmark score is markedly stronger than its general-quality score. It also carries a tool-calling loop suited to agentic workflows, and accepts image input alongside text. Extended thinking runs on every response, giving the model a mandatory reasoning pass before it answers — useful for harder coding and multi-step problems, though it comes at the cost of always being on.
When to use it. Use Kimi K2.7 Code for agentic coding tasks that benefit from a large context window — working across many files, long conversation histories, or extended tool-call chains. Its always-on extended thinking and large MoE footprint make it better suited to asynchronous or batch-style workloads than to latency-sensitive, real-time chat. It's a reasonable fit when the task is coding- or reasoning-heavy rather than general conversation, writing, or domain-specific work like legal or medical analysis, where it has no particular strength.
Limitations. Extended thinking cannot be turned off, so every request carries a reasoning trace and the added latency that comes with it. Moonshot AI has not disclosed a training data cutoff for this model, and its general-quality benchmark is modest relative to its coding strength, so it isn't a strong pick for broad, non-coding tasks. No content-safety or moderation tuning is documented for this model.
Sovereignty note. Kimi K2.7 Code is built by Moonshot AI, based in China; the creator does not carry CLOUD Act exposure. No Stav endpoint currently exists for this model, so endpoint-level data residency has not yet been established.
| Inference Provider | Sovereignty | Runs in | In / out per 1M | Status | Pin |
|---|---|---|---|---|---|
| L1 | €1.15 / 4.05 | Online |
13 use cases classified by the curator. The note says why.
1T-parameter MoE coding model with 262K context and mandatory extended thinking suits large-repo agentic workflows, but draft status and a 43.0 quality index mean it trails validated production coding models.
Coding-focused MoE with extended thinking supports multi-file bug tracing across a 262K context, but draft release status means it's unvalidated for reliability-critical diagnosis.
Purpose-built coding architecture with 262K context handles long completion contexts, though draft status and low benchmark score warrant caution before production use.
Mandatory extended thinking and coding specialization support step-by-step code explanation, though not yet validated at active-release quality.
262K-token context enables whole-repository review passes; quality index of 43.0 and draft status limit confidence relative to established review models.
Vision support plus a 262K context window could handle long scanned-document OCR, though this is a coding-first model without OCR-specific benchmarks.
Vision capability exists but the model's design and description are coding-centric with no evidence of chart/data-visualization extraction performance.
No explicit tool-calling capability documented on this variant, and draft status plus 43.0 quality index make it a poor default for function-calling applications.
Capabilities list vision and prompt-cache but no confirmed tool-calling support, and draft status leaves agentic reliability unverified for orchestration workflows.
Sourced numbers, not our own evaluations. Percentiles are against the whole Stav catalogue.
Computed from aa-intelligence-index, gpqa-diamond, aa-engineering-index, aa-coding-index, livecodebench, terminal-bench-v4, ifbench, aa-lcr.
curl https://api.stav.ai/v1/chat/completions \
-H "Authorization: Bearer $STAV_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "moonshotai/kimi-k2-7-code",
"reasoning_effort": "high",
"messages": [{"role": "user", "content": "Hello!"}]
}'→ X-Stav-Request-Id · X-Stav-Model · X-Stav-Provider → X-Stav-Sovereignty-Level — the rung the request was served at