Kimi K2.7 Code: Official Hugging Face Model Specifications and Full Benchmark Data

One-sentence takeaway

Kimi K2.7 Code is a long-horizon coding and agent-specialized model built on an MoE architecture (1T total parameters / 32B active) , natively integrating the MoonViT multimodal vision encoder and out-of-the-box INT4 quantization, achieving a massive leap in coding performance while cutting thinking token consumption by roughly 30% compared to K2.6.

Test environment, inputs/configuration

• Base architecture: MoE architecture with 61 layers in total (including 1 dense layer) , 384 experts, activating 8 routed experts + 1 shared expert per token.

• Attention and activation: Multi-Head Latent Attention (MLA) mechanism, SwiGLU activation function, 160K vocabulary size, and 256K context window.

• Vision module: MoonViT vision encoder (400M parameters) , supporting native image and video inputs.

• Quantization and deployment: Native INT4 quantization support (~595GB model weights) , compatible with vLLM, SGLang, and KTransformers inference backends.

• Evaluation parameters: Thinking mode strictly enabled (preserve_thinking=True) , temperature=1.0, top_p=0.95.

Results data

Official benchmark comparison matrix published in the Model Card (percentage scores) :

Conclusion

1. Significant leaps in code generation and multilingual capabilities: On the multilingual software engineering benchmark MLS Bench Lite, the score surged from 26.7 to 35.1 (approaching GPT-5.5's 35.5) , while Kimi Code Bench v2 saw an 11.1 percentage point gain.

2. Exceptional performance across MCP and Agent protocols: Reaching 81.1% on MCP Mark Verified, it surpasses Claude Opus 4.8 (76.4%) , demonstrating high-fidelity structured tool-following capabilities.

3. Inference efficiency optimization: Official figures indicate that K2.7 reduces unproductive overthinking (overthinking) tokens by an average of 30% compared to K2.6 in long-horizon tasks, accelerating end-to-end response latency and cutting API serving costs.

Limitations

• Several benchmarks in the official comparison table are internally developed evaluation suites by Moonshot (such as Kimi Code Bench v2 and Kimi Claw 24/7) , which require cross-validation against open-source third-party benchmarks.

• The mandatory requirement to preserve reasoning_content introduces compatibility barriers for third-party clients and API gateways that do not support reasoning field round-tripping.

Reproduction steps

1. Deploy with vLLM or SGLang: python3 -m sglang.launch_server --model-path "moonshotai/Kimi-K2.7-Code" --port 30000.

2. Maintain temperature=1.0, top_p=0.95, and thinking={"type": "enabled"}.

3. In multi-turn conversations, preserve and return the previous assistant message's reasoning_content verbatim.

4. Run the MCP Mark Verified and MLS Bench test suites respectively, logging success rates and token consumption.

Original evidence and data

The official Hugging Face README explicitly outlines the full model architectural hyperparameter table, benchmark score comparison matrix, INT4 quantization specifications, and multimodal video inference sample code.

Source excerpt or observation (short quote for compliance only)

• Official statement: "Kimi K2.7 Code strengthens end-to-end task completion across complex software engineering workflows while improving token efficiency, reducing thinking-token usage by approximately 30% compared with Kimi K2.6."