DeepSeek-Coder-V2 is an open-source code language model that achieves performance comparable to GPT4-Turbo in code-specific tasks. It is pre-trained on an additional 6 trillion tokens from an intermediate checkpoint of DeepSeek-V2, significantly enhancing its coding and mathematical reasoning capabilities while maintaining comparable performance in general language tasks. Compared to DeepSeek-Coder-33B, DeepSeek-Coder-V2 demonstrates significant advancements in code-related tasks, reasoning, and general capabilities. It supports 338 programming languages and extends the context length from 16K to 128K tokens. In benchmark evaluations, DeepSeek-Coder-V2 outperforms closed-source models like GPT4-Turbo, Claude 3 Opus, and Gemini 1.5 Pro in coding and math benchmarks. The model is trained using a combination of Next-Token-Prediction and Fill-In-Middle (FIM) objectives. It is built on the DeepSeek-V2 framework and has a 16B and 236B parameter version. DeepSeek-Coder-V2 is released under a permissive license, allowing both research and commercial use. It supports a wide range of programming languages and has a long context length, enabling it to handle complex coding tasks. The model is evaluated on various benchmarks, including code generation, code completion, code fixing, code understanding, and mathematical reasoning. It achieves high scores on benchmarks like HumanEval, MBPP, LiveCodeBench, and SWE-bench, demonstrating strong performance in code-related tasks. In mathematical reasoning, it performs well on benchmarks like GSM8K, MATH, AIME, and Math Odyssey. In general natural language tasks, it shows comparable performance to DeepSeek-V2, achieving high scores on benchmarks like MMLU, ARC, and TriviaQA. The model is also effective in code completion and reasoning tasks, with strong performance on benchmarks like RepoBench and CRUXEval. Overall, DeepSeek-Coder-V2 is a leading open-source code model that outperforms closed-source models in various tasks.DeepSeek-Coder-V2 is an open-source code language model that achieves performance comparable to GPT4-Turbo in code-specific tasks. It is pre-trained on an additional 6 trillion tokens from an intermediate checkpoint of DeepSeek-V2, significantly enhancing its coding and mathematical reasoning capabilities while maintaining comparable performance in general language tasks. Compared to DeepSeek-Coder-33B, DeepSeek-Coder-V2 demonstrates significant advancements in code-related tasks, reasoning, and general capabilities. It supports 338 programming languages and extends the context length from 16K to 128K tokens. In benchmark evaluations, DeepSeek-Coder-V2 outperforms closed-source models like GPT4-Turbo, Claude 3 Opus, and Gemini 1.5 Pro in coding and math benchmarks. The model is trained using a combination of Next-Token-Prediction and Fill-In-Middle (FIM) objectives. It is built on the DeepSeek-V2 framework and has a 16B and 236B parameter version. DeepSeek-Coder-V2 is released under a permissive license, allowing both research and commercial use. It supports a wide range of programming languages and has a long context length, enabling it to handle complex coding tasks. The model is evaluated on various benchmarks, including code generation, code completion, code fixing, code understanding, and mathematical reasoning. It achieves high scores on benchmarks like HumanEval, MBPP, LiveCodeBench, and SWE-bench, demonstrating strong performance in code-related tasks. In mathematical reasoning, it performs well on benchmarks like GSM8K, MATH, AIME, and Math Odyssey. In general natural language tasks, it shows comparable performance to DeepSeek-V2, achieving high scores on benchmarks like MMLU, ARC, and TriviaQA. The model is also effective in code completion and reasoning tasks, with strong performance on benchmarks like RepoBench and CRUXEval. Overall, DeepSeek-Coder-V2 is a leading open-source code model that outperforms closed-source models in various tasks.