SciAgentBench: leaderboard

Metric: Success rate (%, 0-100) on all 259 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 March 2027. 17 models tracked.

Top models

#ModelScoreOverall rank
1GPT-541.3#91
2Grok 4.140.3#218
3Claude Sonnet 435.9#194
4Gemini 2.5 Flash32.7#237
5Gemini 2.5 Pro32.6#145
6O332#121
7O4 Mini31.1#172
8GLM-4.6V30.9#309
9Qwen 3 VL 235B A22B (Thinking)28#228 (Qwen 3 VL 235B A22B)
10Qwen 3 VL 32B (Thinking)27.9#287 (Qwen 3 VL 32B)
11Qwen 3 VL 32B Instruct27.4#276
12Qwen 3 VL 235B A22B Instruct23.9#264
13Qwen 3 VL 8B Instruct23.4#401
14Qwen 3 VL 4B Instruct19.7#506
15GPT-4o18.7#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 · How It Works · Data refreshed daily, snapshot 2026-10-11.