数据与训练

We are starting to release GPT-6 Astra and we are doing it as carefully and quickly as possible. It was very important to us that we bring it to all Plus users and not only Pro, Business and Enterprise. It will take a few days for the rollout to complete and behind the scenes many novel systems will operate at scale for the first time and we are bringing a lot of compute up. It is pure magic. https://t.co/WUdXA3xSoh

The Trump administration has now formally put the U.S. government behind OpenAI’s core fair-use argument in its copyright fight with The New York Times. some of the most conclusive statements they said in their filed document. - “the ‘training of AI models on copyrighted material,’ in and of itself, ‘does not violate copyright laws.’” - “For all these reasons, the United States has a strong interest in this Court rejecting any argument that training LLMs on copyrighted texts violates copyright law.” - “The fourth fair use factor … supports the conclusion that OpenAI’s model training using New York Times articles is fair use.” - “The copying of protected text articles as part of training an LLM is a use of a different kind or character that is ‘transformative—spectacularly so.’” - “In sum, the use of copies to train LLMs is extraordinarily transformative.” “Rules of law that make it significantly more difficult to develop a robust AI industry in the United States therefore threaten national security and give a competitive advantage to foreign adversaries who are not so encumbered.” - “In this litigation, the New York Times seeks to narrow fair-use doctrine to exclude the training of OpenAI’s large language models (LLMs). That result would be inconsistent with basic copyright law principles and severely hamper ‘the Progress of Science and useful Arts.’” “But it would be problematic—and legally incorrect—to impose broad copyright liability that would generally render training of AI models impermissible without licensing.”

Qwen 3.8 Flash-Next official released: A 6B-active open model just beat Claude Opus 4.6 Max across 8 of 9 comparable benchmarks! Qwen3.8-Flash-Next is a highly sparse MoE: • 125B model parameters • 51B additional n-gram embeddings • Only 6B parameters active per token It scores: • 62.5 SWE-bench Pro • 81.0 SWE-bench Multilingual • 73.9 CoworkBench • 55.7 JobBench • 73.5 Toolathlon • 81.3 IFBench • 91.7 GPQA Diamond • 91.9 LiveCodeBench It also outperforms Qwen3.8-27B and DeepSeek-V4-Flash across most of the table. Super cool release!!