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te the model on our internal evaluation framework using the harness for which it was primarily developed: Claude Code for Anthropic models, Codex for OpenAI models, Grok Build for xAI models, and Devin CLI for open-weight models. For each model, we report the best score across reasoning-effort settings. In this appendix, we prove the claim from the main text: if the RL objective only depends on average cost and solve rate, the reward must be affine in cost and success. For simplicity, we allow S

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le product teams giving our models the chance to reach billions of users and create immense positive impact. If you’re a brilliant, highly-ambitious and low ego individual, you’ll fit right in—come and join us as we work on our next generation of models! Related Stories MAI-Transcribe-2 is the fastest, most accurate and cheapest speech recognition model in the world MAI-Code-1.1-Flash: Better, faster, at a quarter of the cost MAI-Image-2.6 launches at No. 2 on Arena ahead of Google, Meta and xAI

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further to 186 on the same day. Sen. Bernie Sanders teamed up with Rep. Greg Casar on a new bill to outlaw superintelligent AI outright, with penalties modeled on laws for nuclear weapons. Mira Murati’s Thinking Machines Labs is reportedly in talks for a new funding round that would value the company at $40B, according to The Information. Widespread outages occurred on Thursday across the major AI providers, with OpenAI, Anthropic, xAI, and Google all facing stability issues throughout the day.

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o build with AI while giving IT and security complete visibility, control, and governance. 💻 Slack Code - Slack's channels for building software with agents as a team 🤖 Grok Bot - xAI's always-on agent team with its own cloud computer 📄 Precision Mode - Databricks’ document extraction tool beating GPT-5.6 Sol Sponsored Listing Sonar Vortex makes your code conform from the first line, without agents wasting tokens guessing at your architecture. Learn more and drop your token costs by up to 36% .

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labs in adoption decisions at the frontier. To put it simply – the Chinese labs use all the time that American labs do pre-release testing to keep hillclimbing on benchmarks (SpaceXAI is likely far closer to the Chinese labs here). With the pace of progress being so fast, this is likely the largest determining factor of why Chinese labs stay at the frontier. This, so far, has been economically acceptable for the American labs, as they’ve still had massive demand for their models. As model self-i

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