DeepSeek ships the final V4-Pro: open weights, 1M context, agentic scores up sharply

DeepSeek published DeepSeek-V4-Pro-0813 on Hugging Face on 13 August 2026, describing it as the official release of V4-Pro and the successor to the preview weights that had carried the name since April. The repository went up under the MIT licence, so the full model — 1.65 trillion parameters in a sparse mixture-of-experts layout, 61 layers, 384 routed experts with six active per token, shipped in FP8 — can be downloaded and self-hosted. The context window stays at 1,048,576 tokens. The architecture is unchanged from the preview; what is new is a DSpark speculative-decoding module attached to the model and a reworked reasoning control. The `reasoning_effort` parameter now takes three levels — low, high and max — letting callers trade deliberation time against cost. DeepSeek also dropped the Jinja chat template in favour of a documented Python encoder, a change that will require work from anyone running the weights locally. The gains DeepSeek reports are concentrated in agentic and coding work, and they are large. On the company's own table the new build reaches 62.7% on DeepSWE against 12.8% for the preview, 61.5% on NL2Repo against 38.5%, 83.3% on Cybergym against 52.7% and 87.9 on Terminal Bench 2.1 against 72.1. On Humanity's Last Exam it scores 42.7% without tools and 60.0% with them, up from 37.7%. Against rivals in the same table the picture is closer. Kimi K3 edges it on Terminal Bench (88.3) and DeepSWE (67.5), and Anthropic's Fable 5 leads on Humanity's Last Exam (53.3 without tools). What DeepSeek keeps is the position it has held all year: comparable results at frontier level, with the weights published rather than rented. Benchmark figures here are the vendor's own and have not yet been reproduced independently.