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Kimi-Dev-72B

Code moonshotai Released 2025-06-16
-- 72.0B params 131.1K context Open Source

About this model



We introduce Kimi-Dev-72B, our new open-source coding LLM for software engineering tasks. Kimi-Dev-72B achieves a new state-of-the-art on SWE-bench Verified among open-source models.

  • Kimi-Dev-72B achieves 60.4% performance on SWE-bench Verified. It surpasses the runner-up, setting a new state-of-the-art result among open-source models.

  • Kimi-Dev-72B is optimized via large-scale reinforcement learning. It autonomously patches real repositories in Docker and gains rewards only when the entire test suite passes. This ensures correct and robust solutions, aligning with real-world development standards.

  • Kimi-Dev-72B is available for download and deployment on Hugging Face and GitHub. We welcome developers and researchers to explore its capabilities and contribute to development.

Kimi Logo

Performance of Open-source Models on SWE-bench Verified.

Quick Start

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "moonshotai/Kimi-Dev-72B"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "Give me a short introduction to large language model."
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=512
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]

Citation

@misc{kimi_dev_72b_2025,
  title        = {Introducing Kimi-Dev: A Strong and Open-source Coding LLM for Issue Resolution},
  author       = {{Kimi-Dev Team}},
  year         = {2025},
  month        = {June},
  url          = {\url{https://www.moonshot.cn/Kimi-Dev}}
}

Benchmark Scores

MBPP
79.6
HumanEval
93.3
LiveCodeBench
46.1
SWE-Bench-Verified
60.4

Technical Specs

  • Parameters: 72.0B
  • Architecture: Transformer
  • Context Window: 131,072 tokens
  • Input Modalities: text

Hardware Requirements

  • VRAM: 144.0 GB
  • Compute: 2-3x NVIDIA A100 80GB (BF16) or 8-GPU tensor parallel recommended for full 131K context