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Model Library
Arcee AI
Moonshot AI Kimi K3Moonshot AI Kimi K2.7Moonshot AI Kimi K2.6Moonshot AI Kimi K2.5
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  5. Kimi K2.7 Code

Kimi K2.7 Code

Kimi K2.7 Code is an advanced Mixture-of-Experts coding model built for agentic software engineering, long-horizon programming, and efficient repository-scale code generation. It features 1T total parameters with 32B activated, utilising a 61-layer architecture with 7,168 hidden size and 64 attention heads. The model routes 8 experts per token across 384 routed experts and a shared expert, leveraging Multi-head Latent Attention for efficient long-context processing. It supports a 256K token context window, incorporates native INT4 quantization, and optimizes complex workflows with improved token efficiency and end-to-end task completion.

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About this model

BlogHF Model Card

Kimi K2.7 Code is an advanced Mixture-of-Experts coding model built for agentic software engineering, long-horizon programming, and efficient repository-scale code generation. It features 1T total parameters with 32B activated, utilising a 61-layer architecture with 7,168 hidden size and 64 attention heads. The model routes 8 experts per token across 384 routed experts and a shared expert, leveraging Multi-head Latent Attention for efficient long-context processing. It supports a 256K token context window, incorporates native INT4 quantization, and optimizes complex workflows with improved token efficiency and end-to-end task completion.

Model Type

Vision-Language Model

Released

June 12, 2026

License: Modified MIT

Parameters

1T

Model ID
moonshotai/Kimi-K2.7-Code
Capabilities
Text GenerationInstruction FollowingReasoningMathematical ReasoningCode Generation+5

Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (moonshotai/Kimi-K2.7-Code). Released under Modified MIT. Further reading: Blog

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Hardware

B200

1,440 GB

Engine

vLLM
SGLang

Deployment code

INFERENCE COMMAND
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Performance under load

23k18k14k9.2k4.6k0163264128256512Total Tokens / secConcurrent requests
Tokens/s (total)

Benchmarks measured by Vultr on B200 · vLLM. Throughput and latency vary with concurrency, input length and engine configuration, so treat these as a comparison baseline, not a service guarantee.

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Shivam Sharma

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