Description
Kimi K2.7 Code is an advanced coding-focused agentic model developed by Moonshot AI, designed to enhance complex software engineering workflows. Building upon the foundation of Kimi K2.6, this model offers significant improvements in real-world long-horizon coding tasks, strengthening end-to-end task completion. It achieves approximately 30% reduction in thinking-token usage compared to its predecessor.
Read More
The model architecture is based on a Mixture-of-Experts (MoE) framework, featuring 1 trillion total parameters with 32 billion activated parameters. It includes 61 layers, with one dense layer, and utilizes an attention mechanism known as MLA. The model's vocabulary size is 160K, and it supports a context length of 256K tokens.
Kimi K2.7 Code has been evaluated across various benchmarks, demonstrating superior performance in coding tasks compared to Kimi K2.6 and competitive performance against other models like GPT-5.5 and Claude Opus 4.8. It excels in benchmarks such as Kimi Code Bench v2, Program Bench, MLS Bench Lite, and MCP Atlas.
The model supports native INT4 quantization and can be deployed on multiple inference engines, including vLLM, SGLang, and KTransformers. It is compatible with OpenAI/Anthropic APIs and requires a transformers version between 4.57.1 and 5.0.0.
Kimi K2.7 Code is released under the Modified MIT License, ensuring open access to both the code repository and model weights. The model is ideal for developers and researchers looking to enhance coding efficiency and performance in software engineering tasks.
- CategoryAI Coding Assistants
- Pricing ModelOpen source
- DeploymentCloud
- PlatformsWeb
Kimi K2.7 Code Highlights
Mixture-of-Experts Architecture
1 Trillion Total Parameters
256K Context Length
MLA Attention Mechanism
Native INT4 Quantization
Open Source
API Available
Vision Encoder MoonViT
Getting Started with Kimi K2.7 Code
Access model page: visit the official site
Authenticate: log in to your account
Configure: set up model parameters
Send prompts: initiate coding tasks
Fine-tune: adjust model settings for specific needs
Kimi K2.7 Code's Use Cases
- Software Engineering
- Token Efficiency
- Benchmark Performance
- Open Source Development
- API Integration
