Autointent-Chem
Models face manual and synthetic dangerous and benign chemistry queries. xAI reports refusal recall on the dangerous ones.
In-house xAI evaluation, run with the full safeguard stack.
- Scored
- Graded by a model A language model scores each response, usually against a rubric.
- Answers
- Open-ended Free-form text, documents, designs or media judged as a whole.
Grok 4.6 leads with 100%
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.
2 models have held number one since its release in Jul 2026
Lab claims count from the day the card came out, independent runs from the model’s release.
The best score passed 85% in Jul 2026
All the scores we have, one dot each, so a model tested at several settings appears more than once.
Labs report it only in system cards
We read the headline results of 479 launch posts from 51 labs, and none of them list this benchmark. Some system cards do, below.
3 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 |
|---|---|---|---|---|
| 21 Sep 2026 | Grok 4.7 model card | 99.9 | Chem refusal recall (Autointent-Chem), high (9.3 table) | Safety |
| 12 Aug 2026 | Grok 4.6 model card | 100 | Chem refusal recall (Autointent-Chem), high (10.3 table) | Safety |
| 14 Jul 2026 | Grok 4.5 model card | 96.7 | Chem refusal accuracy (Autointent-Chem), full safeguards, high (10.3 table) | Safety |
More in Biology and chemistry
- SUPERChem In use Models solve 500 expert-written chemistry reasoning problems, offered in multimodal and text-only forms. Expert solution paths also allow a reasoning path score.
Tencent Hy4 preview 66.4%
- GeneBench Pro In use Agents analyze messy simulated genomics and biology datasets to estimate a decision-relevant quantity across 129 problems. Scored automatically against known answers. GPT-6 Astra 37.1%
- LifeSciBench In use Models complete 750 expert-written life science research tasks, often with attached figures, tables, or sequence files. Graded against expert rubrics as score and pass rate. GPT-6 Astra 60.3%
- MolecularIQ In use Models answer questions about molecular graphs, such as counting, indexing and constrained generation, and a symbolic checker verifies each answer.
Intern-S2-397B 62.4%
- TOMG-Bench In use Models generate molecules from natural language requests in three tasks: molecule editing, molecule optimization and customized generation. Each output is checked for validity and for meeting the requested properties.
Intern-S2-397B 66.8%
- BABE Fading Models reason from experimental results in peer-reviewed biology papers to draw conclusions, with causal and cross-scale inference.
Seed2.0 Lite (0428) 57.9%