DeepSeek V4 Pro improves on its earlier preview without changing the underlying architecture. Its post-training process creates separate specialist checkpoints for mathematics, coding and agentic work, then distills the abilities of more than ten teachers into one student model.
DeepSeek V4 Pro also drafts several tokens ahead instead of predicting only one token at a time. The reported result is substantially faster generation, showing how a recently published research technique can move quickly into a working model release.
DeepSeek V4 Pro keeps MIT-licensed open weights, allowing independent hosts to compete on price even when the model is too large for most people to run at home. That availability gives users more control over hosting and model access than a closed service can provide.
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