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What's happening in robotics and AI — curated by the wujec.ai editors.
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.
DeepSeek-V4-Pro →The model Europe was not allowed to have: Tencent drops the regional carve-outs from Hy3 and goes global
Chinese open-weight models have spent the past two years competing on size and price. Tencent's Hy3 has quietly been competing on something else: who is allowed to download it. When the preview build appeared in April 2026, its licence carried a geographic exclusion — the European Union, the United Kingdom and South Korea were carved out. The official release on 6 July 2026 removed the clause entirely and put the weights under a plain Apache 2.0 licence, one of the most permissive terms yet attached to a model of this scale. On 5 August Tencent completed the move by announcing global availability: the WorkBuddy assistant, the Miora design tool and Tencent Cloud TokenHub, plus third-party access through OpenRouter and downloads on Hugging Face and ModelScope. The model itself is a sparse mixture-of-experts design: 295 billion parameters in total, roughly 21 billion activated per token across 192 experts with top-8 routing, 80 layers, and a separate 3.8-billion-parameter multi-token-prediction stack that speeds up decoding. The context window is 256,000 tokens. The distinctive feature is that the amount of deliberation is a setting rather than a separate product — no_think answers immediately, think_low reasons briefly, think_high runs an extended chain — so one endpoint covers both a chat reply and a long analytical job. Tencent's own framing is that Hy3 performs comparably to flagship models with two to five times as many parameters, across reasoning, instruction following, in-context learning, code generation and agentic work. The company also reports that within a week of launch, API traffic to Hy3 ran 68 times higher than to the previous generation, putting it first on OpenRouter's usage leaderboard. Listed OpenRouter pricing is 0.1288 US dollars per million input tokens and 0.5336 per million output; access through WorkBuddy is free until 31 August 2026. The usage number is the one to watch. Benchmark parity claims from vendors are routine; a 68-fold jump in paid API calls is a statement about how many developers actually switched. Independent coverage broadly agrees with the performance claims, with coding the one area where rival open-weight models still hold an edge.
Tencent Hy3 →Moonshot AI ships Kimi K3, the largest open-weight model ever
Moonshot AI released Kimi K3 on July 16, 2026, open-sourcing the 2.8-trillion-parameter weights under a modified MIT license eleven days later. The mixture-of-experts model activates 104B parameters (16 of 896 experts) per token, adds a 1M-token context window and native image/video understanding, and topped Arena's blind Frontend Code evaluation ahead of Claude Fable 5 — the clearest sign yet that open-weight labs out of China are setting the pace at the very top of the frontier race, not just chasing it. K3 succeeds the original Kimi K2 through the K2 Thinking, K2.5, K2.6 and K2.7-Code line, and developers have already wired it into Cursor and Vercel, running it on consumer GPUs within days of release.
Kimi K3 →DeepSeek's 1.6-trillion-parameter flagship goes GA — and starts charging by the clock
DeepSeek-V4-Pro left preview on 19 July 2026, three months after its first public release. The weights are the same ones published on 24 April: a sparse mixture-of-experts model with 1.6 trillion total parameters, roughly 49 billion of them active per token, a one-million-token context window and an MIT licence that allows anyone to download and self-host it. What general availability changed was the commercial side. DeepSeek introduced peak-time pricing for the first time, doubling rates during Beijing business hours; off-peak the API costs 0.435 US dollars per million input tokens and 0.87 per million output. Cached input is charged at roughly a hundredth of the off-peak input rate. On 24 July the company retired the legacy deepseek-chat and deepseek-reasoner endpoints, so integrations now have to name a model explicitly. On DeepSeek's own GA figures the model scores 80.6% on SWE-bench Verified — the strongest published result for an open-weights model — with a Codeforces rating of 3,206, 57.9% on SimpleQA Verified and 37.7% on Humanity's Last Exam. Independent measurement tells a subtler story: Artificial Analysis rated V4-Pro at 44 on its Intelligence Index at GA, down from 52 at preview. The model did not get worse; the field moved during the three months it spent in preview. The smaller DeepSeek-V4-Flash and the earlier DeepSeek-R1 remain available, and all three now have profiles in the wujec.ai catalogue.
DeepSeek-V4-Pro →