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GPT-6-Astra

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3

RESEARCH · 1 SOURCE · The Decoder

TheoPhysEval benchmark: GPT-6-Astra solves 49 of 50 theoretical physics problems

Thirty-five researchers released TheoPhysEval, a 50-problem benchmark of unpublished theoretical physics calculations with verifiable answers. GPT-6-Astra (running in OpenAI's Codex environment) solved 49 of 50 problems (98%) at an average cost under $3 per problem; Anthropic's Opus 5.5 scored 92%, Gemini 3.8 Flash 68%, and several open-weight models scored lower. Tests were run with and without web access and authors note models often performed well without internet, implying substantial memorization of the literature; the dataset and test code are available on Hugging Face.

9.0

RESEARCH · 1 SOURCE · arXiv cs.AI

MathVis benchmark (arXiv:2610.12954v1) evaluates LLM diagram generation for math

MathVis is a new benchmark (arXiv:2610.12954v1) of 512 tasks that tests large language models' ability to visualize mathematical problems and produce executable TikZ/SVG diagrams, providing reference diagrams, a Gold intermediate representation, atomic rubrics, and error codes. In initial evaluations GPT-6-Astra, Claude Fable 5, and Gemini 3.1 Pro Preview scored pass@1 of 31.25%, 26.56%, and 22.85% respectively, and the authors report LLM outputs are often formal-language-heavy and visually confusing rather than demonstrating explicit diagram-mediated reasoning.

6.0

RESEARCH · 1 SOURCE · arXiv cs.AI

OpenProblemBench: benchmark of 82 open math and theoretical-physics problems for AI

OpenProblemBench is a new benchmark (arXiv:2610.11118v1) comprising 82 unresolved problems from mathematics and theoretical physics, each provided with context, assumptions, and prior progress to support investigation. Four evaluator models judged submissions without reference solutions; across seven configurations GPT-6-Astra achieved a mean judged solve rate of 14.0%, versus 5.5–6.7% for evaluated full-size open models and 2.4–3.7% for Flash models.

7.0