SciAgentBench - L2 (4 to 7 Steps): leaderboard

Metric: Success rate (%, 0-100) on the tasks of difficulty L2 (4 to 7 steps); 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
1Grok 4.143.9#218
2GPT-538.4#91
3Claude Sonnet 436.5#194
4O4 Mini35#172
5Gemini 2.5 Pro33.6#145
6Gemini 2.5 Flash33.1#237
7O331.4#121
8GLM-4.6V27.5#309
9Qwen 3 VL 235B A22B (Thinking)27.4#228 (Qwen 3 VL 235B A22B)
10Qwen 3 VL 32B Instruct27.3#276
11Qwen 3 VL 32B (Thinking)27.3#287 (Qwen 3 VL 32B)
12Qwen 3 VL 8B Instruct25.2#401
13Qwen 3 VL 235B A22B Instruct22.6#264
14GPT-4o17.4#333
15Qwen 3 VL 4B Instruct16.5#506

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

Interactive version: theaggregate.ai/benchmark?slug=sciagentbench-l2-4-to-7-steps · How It Works · Data refreshed daily, snapshot 2026-10-11.