Papers › DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code...
DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence
Daya Guo, Qihao Zhu, Dejian Yang, Zhenda Xie, Kai Dong, Wentao Zhang, Guanting Chen, Xiao Bi, Y. Wu, Y. K. Li, Fuli Luo, Yingfei Xiong, Wenfeng Liang
The rapid development of large language models has revolutionized code intelligence in software development. However, the predominance of closed-source models has restricted extensive research and development. To address this, we introduce the DeepSeek-Coder series, a range of open-source code models with sizes from 1.3B to 33B, trained from scratch on 2 trillion tokens. These models are pre-trained on a high-quality project-level code corpus and employ a fill-in-the-blank task with a 16K window to enhance code generation and infilling. Our extensive evaluations demonstrate that DeepSeek-Coder not only achieves state-of-the-art performance among open-source code models across multiple benchmarks but also surpasses existing closed-source models like Codex and GPT-3.5. Furthermore, DeepSeek-Coder models are under a permissive license that allows for both research and unrestricted commercial use.
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Code
Syntology Ran 9 of 10 code samples harvested from 1 repository linked to this paper; 1 has no recorded run. Of those that ran: 9 ran with no contract checked.
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Code Syntology ran Syntology
10 samples harvested; 9 ran; 0 honoured the contract we drafted; 1 has no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
Licence: 0 of the 10 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Code Generation | APPS | deepseek-ai/deepseek-coder-6.7b-instruct | Competition Pass@1 | 11.09 | #5 of 18 | Archive leaderboard | report |
| Code Generation | APPS | deepseek-ai/deepseek-coder-6.7b-instruct | Interview Pass@1 | 19.70 | #5 of 18 | Archive leaderboard | report |
| Code Generation | APPS | deepseek-ai/deepseek-coder-6.7b-instruct | Introductory Pass@1 | 33.80 | #5 of 18 | Archive leaderboard | report |
| Code Generation | MBPP | GPT-4 (few-shot) | Accuracy | 80 | #24 of 99 | Archive leaderboard | report |
| Code Generation | MBPP | GPT-3.5 Turbo (few-shot) | Accuracy | 70.8 | #30 of 99 | Archive leaderboard | report |
| Code Generation | MBPP | DeepSeek-Coder-Instruct 33B (few-shot) | Accuracy | 70 | #31 of 99 | Archive leaderboard | report |
| Code Generation | MBPP | DeepSeek-Coder-Base 33B (few-shot) | Accuracy | 66 | #39 of 99 | Archive leaderboard | report |
| Code Generation | MBPP | DeepSeek-Coder-Instruct 6.7B (few-shot) | Accuracy | 65.4 | #41 of 99 | Archive leaderboard | report |
| Code Generation | MBPP | DeepSeek-Coder-Base 6.7B (few-shot) | Accuracy | 60.6 | #49 of 99 | Archive leaderboard | report |
| Code Generation | MBPP | DeepSeek-Coder-Instruct 1.3B (few-shot) | Accuracy | 49.4 | #62 of 99 | Archive leaderboard | report |
| Code Generation | MBPP | DeepSeek-Coder-Base 1.3B (few-shot) | Accuracy | 46.2 | #74 of 99 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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