Matbench
Models predict properties of inorganic materials in 13 supervised tasks built from DFT and experimental data. Scored with error or classification metrics per task.
Built for materials ML models. Intern-S models report an LLM version of its property prediction tasks, and the exact subset is not stated.
- 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.
Intern-S1 leads with 75.0
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
Intern-S1 75.0
- 2
Intern-S1-Pro 72.8
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | | 75.0 | Jul 2025 | Model card | |
| 2 | | 72.8 | Feb 2026 | Model card |
Number one over time
Lab claims count from the day the card came out, independent runs from the model’s release.
Scores over time
All the scores we have, one dot each, so a model tested at several settings appears more than once.
Labs headline it less often now, last in Feb 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 |
|---|---|---|---|
| 2 Feb 2026 | 72.8 | MatBench (Materials Property Prediction) | |
| 24 Jul 2025 | 75 | MatBench |
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 |
|---|---|---|---|---|
| 26 Mar 2026 | | 72.8 | MatBench, Materials Property Prediction, non-thinking (Table 2) | Capabilities |
| 21 Aug 2025 | | 75 | MatBench (Table 3) | Capabilities |
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