DeepSeek-Coder-V2: GPT-4 Turbo coding quality with open weights

In one sentence DeepSeek releases Coder-V2 in 16B and 236B MoE variants, trained on 6T tokens across 338 languages. The first open-weight model to surpass GPT-4 Turbo on coding benchmarks and top SWE-bench.

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Imagine having a free, downloadable tool that writes code better than one of the most powerful paid tools in the world. That is what DeepSeek made possible with Coder-V2.

DeepSeek-Coder-V2 uses a particular architecture called "Mixture of Experts" (MoE): instead of activating all the billions of model parameters for every response, it activates only the subsets of "experts" most relevant to the specific problem. This makes the model much more efficient: the large version has 236 billion total parameters but only uses a fraction of them for each individual operation.

The practical result: the larger model surpasses GPT-4 Turbo on major code benchmarks, including SWE-bench which simulates solving real bugs in real GitHub repositories. It was the first open-weight model to achieve this. It covers 338 programming languages and has a 128k token context window. For companies wanting AI coding capabilities without depending on cloud APIs with variable costs, this represented a significant shift.