SciAgentBench - L1 (Up to 3 Steps): leaderboard

Metric: Success rate (%, 0-100) on the tasks of difficulty L1 (3 steps or fewer); 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-558.8#91
2Claude Sonnet 457.4#194
3Qwen 3 VL 235B A22B Instruct57.1#264
4Gemini 2.5 Pro54.2#145
5O4 Mini51#172
6Grok 4.150#218
7Qwen 3 VL 4B Instruct48.8#506
8GLM-4.6V48.8#309
9Gemini 2.5 Flash48#237
10O347.9#121
11Qwen 3 VL 32B Instruct47.1#276
12Qwen 3 VL 235B A22B (Thinking)46.8#228 (Qwen 3 VL 235B A22B)
13Qwen 3 VL 32B (Thinking)45.3#287 (Qwen 3 VL 32B)
14Qwen 3 VL 8B Instruct44.4#401
15GPT-4o36#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-l1-up-to-3-steps · How It Works · Data refreshed daily, snapshot 2026-10-11.