Benchmark Almanac

PHYSICS

Intern-S1 44% Dropped

Models solve 1,297 PhD qualifying exam physics problems across six subfields, 298 of them with images. Answers are checked by symbolic equivalence.

Shanghai AI Lab Intern-S1 reports it as Physics.

In the authors’ words

We introduce Physics, a benchmark for evaluating foundation models on advanced physics problem solving. It contains 1034 annotated, open-ended problems from PhD-qualifying exams across six core physics domains. Solving these problems requires applying domain-specific knowledge to physical setups and carrying out multi-step mathematical reasoning. Each problem includes a worked solution and independently checked reference answers. An LLM-based grading protocol evaluates individual answers and complete multi-part problems, supporting numeric, symbolic, and text responses. We evaluate 53 proprietary and open-weight models. On test problems without figures, high accuracy is concentrated in a small group of thinking models, with the strongest open-weight models approaching proprietary performance. Most open-weight models still solve fewer than half of these problems. Our analysis of Qwen3.5-9B reveals failures in applying physics knowledge and interpreting figures, with misread figures affecting several subquestions.

Abstract of Physics: Benchmarking Foundation Models on University-Level Physics Problem Solving (arXiv:2503.21821, CC0)
Made by Yale University (Kaiyue Feng, Arman Cohan et al.) Released Mar 2025 Science questions All tasks public Last in a launch post Jul 2025 , dropped before it saturated Last model added Aug 2025
Level
Expert Specialists with years of training, often over hours of work
Scored
Checked automatically A program checks the result against an answer key, unit tests or the expected final state.
Answers
Short answer A number, word or short string matched to a key.
1
Model scored in lab cards
1
Launch post lists it, from 1 lab
1
System card reports it, from 1 lab

Intern-S1 leads with 44%

Best score per model as labs report them in their own cards. Labs pick their own settings, so these compare less cleanly than independent runs.

  1. 1 Intern-S1 44%
#ModelScoreSettingDateSource
1 Intern-S1 44% Jul 2025 Model card

Scores over time

All the scores we have, one dot each, so a model tested at several settings appears more than once.

Reported by the lab in its model card
0% 20% 40% 60% 80% 100% Near the ceiling (85%) Intern-S1 0% 20% 40% 60% 80% 100% Near the ceiling (85%) Intern-S1

No launch post has listed it since Jul 2025

Launch posts from every lab that has listed it, one tick per post. Tall colored ticks include it, and short gray ones leave it out.

Shanghai AI Lab
DateLaunch postScoreAs the post writes it
24 Jul 2025 Intern-S1 44 Physics

One system card reports it

Besides launch posts, we read the system cards, model cards and technical reports labs publish with a release. They report far more, including safety and frontier-risk tests.

DateDocumentScoreAs the document writes itSection
21 Aug 2025 Intern-S1 technical report 44 Physics, PHYSICS by Feng et al. (Table 4) Capabilities

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