ContPhy
Models infer physical properties such as mass and density of fluids and soft bodies from videos and predict their dynamics.
Published at ICML 2024. The format field is a guess.
- Made by
- Zheng et al. (ContPhy team)
- Released
- February 2024
- Kind
- Physical prediction
- Level
- Everyday
- Scored
- Checked automatically
- Answers
- Multiple choice
- Last in a launch post
- April 2026, dropped before it saturated
- Last model added
- June 2026
In the authors’ words
We introduce the Continuum Physical Dataset (ContPhy), a novel benchmark for assessing machine physical commonsense. ContPhy complements existing physical reasoning benchmarks by encompassing the inference of diverse physical properties, such as mass and density, across various scenarios and predicting corresponding dynamics. We evaluated a range of AI models and found that they still struggle to achieve satisfactory performance on ContPhy, which shows that the current AI models still lack physical commonsense for the continuum, especially soft-bodies, and illustrates the value of the proposed dataset. We also introduce an oracle model (ContPRO) that marries the particle-based physical dynamic models with the recent large language models, which enjoy the advantages of both models, precise dynamic predictions, and interpretable reasoning. ContPhy aims to spur progress in perception and reasoning within diverse physical settings, narrowing the divide between human and machine intelligence in understanding the physical world. Project page: https://physical-reasoning-project.github.io
Seed2.0 Pro leads with 67.4% among models released by June 2026
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.
The newest model with a score here came out in June 2026, and nobody has reported a newer one. Among the 20 highest-ranked models on the capability index, these have no score yet:
- Claude Fable 5.2
-
Gemini 4 Carbon
- GPT-6 Astra
- Claude Opus 5.5
- and 16 more
- 1
Seed2.0 Pro 67.4%
- 2
Seed2.1 Pro 63.6%
- 3
Seed2.0 Lite (0428) 62.4%
Source: Labs’ model cards
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | | 67.4% | Feb 2026 | Lab model card | |
| 2 | | 63.6% | Jun 2026 | Lab model card | |
| 3 | | 62.4% | Apr 2026 | Lab model card |
Number one over time
Lab claims count from the day the card came out, independent runs from the model’s release.
Source: Labs’ model cards
Scores over time
All the scores we have, one dot each, so a model tested at several settings appears more than once.
Source: Labs’ model cards
Labs headline it less often now, last in April 2026
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.
| Date | Launch post | Score | As the post writes it |
|---|---|---|---|
| Apr 28, 2026 | 62.4 | ContPhy |
2 system cards report it, from 1 lab
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.
| Date | Document | Score | As the document writes it | Section |
|---|---|---|---|---|
| Jun 23, 2026 | | 63.6 | ContPhy (Table 10) | Capabilities |
| Feb 14, 2026 | | 67.4 | ContPhy (Table 9) | Capabilities |
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- IntPhys 2 In use Models watch simulated videos and judge whether each event is physically possible, testing object permanence, immutability, continuity and solidity. Scored by accuracy.
Muse Spark Open 68.3%
- QwenWorldBench Fading Models act as world models that simulate agent environments in 7 domains, such as terminal, web, and Android. A rubric judge scores five dimensions.
Qwen3.7-Plus 62.1%
- VPCT Fading Models view an image of a ball, ramps, and three buckets and predict which bucket the ball falls into. Scored by accuracy on 100 problems.
Gemini 3 Pro 91%
- RBench Only in system cards 650 image+text prompts for robot video generation (tasks and embodiments). MLLM judge scores task completion, physical plausibility, stability.
Wan 2.6 60.7%
- PAI-Bench Only in system cards Physical-AI video generation from 1,044 prompts in six domains, VLM-QA plus quality metrics. Companion control and understanding tracks.
Cosmos3-Nano 83.7%