SciAgentBench - Chemistry: leaderboard

Metric: Success rate (%, 0-100) on the 81 chemistry tasks; with tools: a ReAct loop over the task-relevant SciAgentGym tools (native function calling, up to 50 tool rounds, 300-second request timeout); SciAgentBench: 259 multi-step scientific tasks (1,134 sub-questions; physics 109, chemistry 81, materials 37, life sciences 32; about 65 percent with images) aggregated from existing benchmarks, kept when four frontier LLMs averaged under 50 percent and SciAgentGym could execute a verified trace; a task counts only when every sub-question is correct (strict JSON matching with 0.05 numeric tolerance, GPT-4.1 checking textual fields); temperature 0.7; higher is better. Source: arxiv.org. Saturation forecast: Around January 2027. 17 models tracked.

Top models

#ModelScoreOverall rank
1GPT-543.8#91
2Claude Sonnet 439.5#194
3Grok 4.138.2#218
4GLM-4.6V37.5#309
5O337.3#121
6O4 Mini35.5#172
7Gemini 2.5 Pro35.1#145
8Gemini 2.5 Flash32.4#237
9Qwen 3 VL 32B (Thinking)31.2#287 (Qwen 3 VL 32B)
10Qwen 3 VL 235B A22B (Thinking)29.5#228 (Qwen 3 VL 235B A22B)
11Qwen 3 VL 32B Instruct29.3#276
12Qwen 3 VL 8B Instruct28.6#401
13Qwen 3 VL 235B A22B Instruct26.5#264
14Qwen 3 VL 4B Instruct20.6#506
15GPT-4o20.5#333

No result here: #3 Claude Opus 5.5, #5 GPT-6 Astra, #8 Claude Fable 5.1.

Interactive version: theaggregate.ai/benchmark?slug=sciagentbench-chemistry · How It Works · Data refreshed daily, snapshot 2026-10-11.