DeepSeek ships R1-0528: same 685B MoE weights, MIT license, context doubled to 128K
DeepSeek published R1-0528, a refresh of its January reasoning model, keeping the 685B-parameter mixture-of-experts design with 37B active parameters per token, the same MIT license on the weights, and doubling the context window from 64K to 128K tokens. On DeepSeek's own card, the AIME 2025 pass rate moves from 70.0% to 87.5% and the model shows a large drop in long-form hallucination relative to the original R1, which puts a downloadable checkpoint within touching distance of closed frontier reasoning models at a fraction of the API price.
Weights are on Hugging Face alongside a distilled DeepSeek-R1-0528-Qwen3-8B, so the full stack is self-hostable on commodity hardware at the small end and on rented H100s at the top. For anyone building on a per-token budget, an MIT-licensed 128K reasoning model that clears the old 64K ceiling resets the cost floor: inference you were pricing at frontier rates now has a free-to-run substitute, and the burden shifts to your serving, KV-cache and evals stack rather than to your API bill.
WHY IT MATTERS
The cost floor for near-frontier reasoning moved again. Architects can now plan around a 128K-context, MIT-licensed checkpoint they may fine-tune, quantize and self-host, which turns can we afford frontier reasoning on every request into a serving-capacity problem inside their own infrastructure — and makes the absence of per-token vendor lock-in a legitimate architectural assumption rather than an aspiration.
