moonshotai/Kimi-Dev-72B

Hugging Face
TEXT GENERATIONConcurrency Cost:4Model Size:72.7BQuant:FP8Ctx Length:32kPublished:Jun 16, 2025License:mitArchitecture:Transformer0.4K Open Weights Warm

Kimi-Dev-72B is a 72.7 billion parameter open-source coding Large Language Model developed by Moonshot AI, specifically optimized for software engineering tasks. It achieves a new state-of-the-art performance of 60.4% on SWE-bench Verified among open-source models. The model is trained using large-scale reinforcement learning, autonomously patching real repositories and passing entire test suites to ensure robust solutions. It is primarily designed for issue resolution and code generation in real-world development scenarios.

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Kimi-Dev-72B: An Open-Source Coding LLM for Issue Resolution

Kimi-Dev-72B, developed by Moonshot AI, is a 72.7 billion parameter open-source coding Large Language Model engineered for software engineering tasks. It distinguishes itself by achieving a new state-of-the-art performance on the challenging SWE-bench Verified benchmark among open-source models.

Key Capabilities & Features

  • State-of-the-Art Performance: Achieves 60.4% on SWE-bench Verified, surpassing other open-source models in its category.
  • Reinforcement Learning Optimization: The model is optimized through large-scale reinforcement learning, where it autonomously patches real repositories within Docker environments.
  • Robust Solution Generation: Rewards are gained only when the entire test suite passes, ensuring the generation of correct and robust solutions that align with real-world development standards.

Ideal Use Cases

  • Automated Issue Resolution: Excellent for tasks requiring the model to identify and fix bugs or implement features in existing codebases.
  • Code Generation & Refactoring: Suitable for generating new code or refactoring existing code with a focus on correctness and passing tests.
  • Software Engineering Research: Provides a strong baseline for researchers exploring advanced coding LLM capabilities and reinforcement learning applications in software development.

Popular Sampler Settings

Top 3 parameter combinations used by Featherless users for this model. Click a tab to see each config.

temperature
top_p
top_k
frequency_penalty
presence_penalty
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