OpenAI released GPT-6 Astra as its new flagship model, but first independent benchmarks show it matching its predecessor and trailing top competitors on headline intelligence metrics. Artificial Analysis reports an Intelligence Index score of 61 points, equal to GPT-5.6 Sol and 5 points behind Anthropic’s top model at 66. Costs rise 2.5x with mixed per-token efficiency improvements, while hallucination rate at max effort falls from 92% to 51%.
Simon Willison’s Weblog·1 month ago·
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OpenAI is rolling out GPT-6 Astra to a limited set of organizations before expanding availability to all ChatGPT Plus, Pro, Business, and Enterprise users and to the OpenAI API and AWS. The API pricing is $10 per million input tokens and $50 per million output tokens. Model access expands broadly across ChatGPT and developer platforms, and Astra’s published benchmark results emphasize improvements in ARC-AGI 3 (99.9%) and security/long-context tasks.
GPT-6 Astra posted a 98.6% result on OpenAI’s ARC-AGI-3 benchmark, far above GPT-5.6 Sol’s 7.8%. The score was evaluated via the Responses API harness with two settings changed for real-world use, and OpenAI notes other models used different setups. The result broadens Astra’s performance claims across other benchmarks and is paired with math progress reports, while OpenAI stresses that the ARC-AGI-3 setup makes direct comparisons less straightforward.
OpenAI launched GPT-6 Astra as its newest flagship model and said it marks the start of an “AGI era.” Astra’s API pricing is $10 per 1 million input tokens and $50 per 1 million output tokens. Access begins with enterprise customers on OpenAI’s Daybreak program and then expands to Plus, Pro, Business, and Enterprise plus the API and AWS in the coming days.
Meta launched Muse Spark 1.3 and said it delivers its biggest improvements yet for coding and agentic tasks, with rollout through Muse Code and the Meta Model API plus open-weight releases planned. It reported 75.4% on the DeepSWE coding benchmark and 98.1% on the 512K-1M MRCR long-context test for Spark 1.3. Outside benchmarks found Spark 1.3 xhigh at 61 on an Intelligence Index at about $0.55 per task, pushing Gemini 3.8 Flash off the cost-versus-intelligence frontier and changing which models look most efficient for developers.
Google DeepMind and Google Research released WeatherNext 3, a new AI weather-forecasting model intended to predict atmospheric behavior more accurately and with faster updates. WeatherNext 3 achieves 5km resolution and is reported to be 60% better at rain than WeatherNext 2 based on evaluations on Operational WeatherBench. Google plans to feed the model into Search, Google Maps, and Gemini, and also offer it to users and researchers via Google Cloud.
Google is rolling out WeatherNext 3, an updated AI weather forecasting model meant to improve accuracy for rain and snowfall predictions. The update claims global forecasts that are five times sharper than Google’s previous AI model. As a result, WeatherNext 3 uses real-time weather observations to produce higher-resolution global predictions than before.
Meta rolled out Muse Spark 1.3 and evaluations placed it near the top for coding and agentic performance. Muse Spark 1.3 (xhigh) scored 61 on the Intelligence Index. Its progress mainly improves agentic/scientific reasoning and pricing, and whether open-weight 1.3 arrives could shift the open-weight rankings versus top labs.
Google launched Gemini 3.8 Flash and Gemini 3.8 Flash Cyber, plus the Fairwind early access program for the cyber-focused model. Gemini 3.8 Flash scored 73.7% on DeepSWE-1.1, outperforming GPT-5.6 Sol by 1% but trailing Claude Opus 5 by a few fractions of a percent. The release shifts Google’s model lineup toward higher-effort reasoning (more iterative tool calls and steps) and expands early access cybersecurity use via CodeMender for selected participants.
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