SciAgentBench - L3 (8 or More Steps): leaderboard

Metric: Success rate (%, 0-100) on the tasks of difficulty L3 (8 steps or more); 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 May 2027. 17 models tracked.

Top models

#ModelScoreOverall rank
1GPT-534.6#91
2Grok 4.128.6#218
3O323.1#121
4GLM-4.6V22.2#309
5Gemini 2.5 Flash22.1#237
6Claude Sonnet 420.3#194
7Qwen 3 VL 235B A22B (Thinking)17.4#228 (Qwen 3 VL 235B A22B)
8Gemini 2.5 Pro17.3#145
9Qwen 3 VL 32B (Thinking)16.9#287 (Qwen 3 VL 32B)
10Qwen 3 VL 32B Instruct14.5#276
11O4 Mini11.7#172
12GPT-4o9.2#333
13Qwen 3 VL 4B Instruct8.3#506
14Qwen 3 VL 8B Instruct7#401
15Pixtral-12B5.1#795

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

Interactive version: theaggregate.ai/benchmark?slug=sciagentbench-l3-8-or-more-steps · How It Works · Data refreshed daily, snapshot 2026-10-11.