Kimi-K2.7-Code

1. Model Introduction Kimi K2.7 Code is a coding-focused agentic model built upon Kimi K2.6. With substantial improvements on real-world long-horizon coding tasks, it 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. 2. Model Summary | | | |:---:|:---:| | **Architecture** | Mixture-of-Experts (MoE) | | **Total Parameters** | 1T | | **Activated Parameters** | 32B | | **Number of Layers** (Dense layer included) | 61 | | **Number of Dense Layers** | 1 | | **Attention Hidden Dimension** | 7168 | | **MoE Hidden Dimension** (per Expert) | 2048 | | **Number of Attention Heads** | 64 | | **Number of Experts** | 384 | | **Selected Experts per Token** | 8 | | **Number of Shared Experts** | 1 | | **Vocabulary Size** | 160K | | **Context Length** | 256K | | **Attention Mechanism** | MLA | | **Activation Function** | SwiGLU | | **Vision Encoder** | MoonViT | | **Parameters of Vision Encoder** | 400M | 3. Evaluation Results Benchmark Kimi K2.6 Kimi K2.7 Code GPT-5.5 Claude Opus 4.8 Coding Kimi Code Bench v2 50.9 62.0 69.0 67.4 Program Bench 48.3 53.6 69.1 63.8 MLS Bench Lite 26.7 35.1 35.5 42.8 Agentic Kimi Claw 24/7 Bench 42.9 46.9 52.8 50.4 MCP Atlas 69.4 76.0 79.4 81.3 MCP Mark Verified 72.8 81.1 92.9 76.4 Footnotes 1. **General Testing Details** Unless stated otherwise, Kimi K2.7 Code and K2.6 were tested with thinking mode enabled via Kimi Code CLI at temperature = 1.0, top-p = 0.95, and a 262,144-token context length; GPT-5.5 ran in Codex with xhigh mode, and Opus 4.8 in Claude Code with xhigh mode. Aside from these differences, all benchmarks were evaluated under the same conditions. 2. **Coding Benchmarks** Kimi Code Bench V2 is our in-house benchmark designed to evaluate coding agents on realistic tasks. It has diversed software engineering tasks across 10+ mainstream programming languages and a full production tech stack coveri