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Kimi K2.7 Code

by Moonshot · Released 2026

Moonshot AI's frontier-scale open-source 1T-parameter model tuned for agentic coding. 262K context window, multi-turn tool calling, reasoning, vision inputs, and structured outputs for agentic workloads.

LLM Chat

Kimi K2.7 Code

Powered by Moonshot · Mixture-of-Experts (1T params, agentic coding)

Context Window

262K

Parameters

1T (MoE)

Max Output

16K

Category

LLM Chat

Overview

Kimi K2.7 Code is Moonshot AI's frontier-scale open-source model — a 1-trillion-parameter mixture-of-experts architecture purpose-built for agentic coding workloads. It pairs a 262,144-token context window with multi-turn tool calling, native reasoning, vision inputs, and structured outputs, making it well-suited for long-horizon software-engineering tasks that span large codebases and many tool invocations.

The model is designed for autonomous coding agents: it can plan a change across many files, call tools to read and edit code, run tests, and iterate — all while keeping the full project context in its very large window. Vision input lets it work from screenshots, diagrams, and design mockups, and structured output keeps tool-call payloads reliable.

Served through CallMissed, K2.7 Code is OpenAI-compatible (`/v1/chat/completions`) with streaming, full tool calling, and a `reasoning_effort` thinking toggle.

Pricing

MetricPrice
Input /1M tokens₹128.0000
Output /1M tokens₹540.0000

1 credit = ₹1 = $0.01 USD. Prices shown from provider; CallMissed passes through with ~35% markup.

Key Highlights

  • Frontier-scale 1T-parameter MoE tuned for agentic coding
  • 262K-token context for whole-codebase reasoning
  • Multi-turn tool calling + structured outputs
  • Vision inputs (screenshots, mockups, diagrams)

Benchmarks

BenchmarkScore
SWE-benchSOTA-class
LiveCodeBenchTop-tier

Technical Details

  • Frontier-scale 1T-parameter mixture-of-experts architecture
  • Context window: 262,144 tokens
  • Multi-turn function/tool calling
  • Native reasoning with reasoning_effort thinking toggle
  • Vision inputs supported
  • Structured outputs for agentic workflows
  • OpenAI-compatible on the CallMissed gateway

Strengths

  • Massive 262K context keeps whole codebases in view
  • Built for multi-turn tool-using coding agents
  • Vision + reasoning + structured outputs in one model

Limitations

  • Frontier-scale model — higher latency than small fast tiers
  • Newer release with limited production track record

Use Cases

Agentic codingWhole-repo refactoringTool-using dev agentsVision-assisted UI coding

API Example

curl https://api.callmissed.com/v1/chat/completions \
  -H "Authorization: Bearer cm_YOUR_KEY" \
  -d '{"model": "kimi-k2.7-code", "messages": [{"role": "user", "content": "Refactor this module and add tests"}]}'

Endpoint: POST /v1/chat/completions · Model ID: kimi-k2.7-code

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