MuSR
Multistep Soft Reasoning (MuSR) evaluates models on complex reasoning tasks that require multiple inference steps in domains like murder mysteries, team allocation puzzles, and object placements. Problems require 2-7 reasoning steps.
Metrics
Accuracy (%) on multistep reasoning
Created By
UT Austin
Paper
View paper →Website
Visit website →Top Model Scores
| Rank | Model | Score | Date |
|---|---|---|---|
| 1 | GPT-5.2 | 71.8% | 2026-03 |
| 2 | Claude Opus 4.6 | 69.4% | 2026-02 |
| 3 | Gemini 3 Ultra | 66.7% | 2026-01 |
| 4 | Grok 4 | 63.2% | 2026-02 |
| 5 | DeepSeek V3 | 59.8% | 2026-01 |
Related Reasoning Benchmarks
HellaSwag
HellaSwag is a commonsense reasoning benchmark that tests whether AI models can predict the most plausible continuation of a given scenario. It uses adversarially constructed wrong answers that are challenging for models but easy for humans.
Top: GPT-5.2 — 97.8%
ARC (AI2 Reasoning Challenge)
The AI2 Reasoning Challenge contains 7,787 genuine grade-school science questions, split into Easy and Challenge sets. The Challenge set contains only questions that are answered incorrectly by both a retrieval-based algorithm and a word co-occurrence algorithm.
Top: GPT-5.2 — 98.2%
GPQA (Diamond)
Graduate-Level Google-Proof Q&A (GPQA) Diamond is a challenging benchmark of expert-level questions in biology, physics, and chemistry. Questions are designed to be answerable by domain experts but extremely difficult for non-experts, even with web search.
Top: GPT-5.2 — 94.7%
BBH (BIG-Bench Hard)
BIG-Bench Hard is a suite of 23 challenging tasks from the BIG-Bench benchmark where language models previously performed below average human raters. Tasks include boolean expressions, causal judgement, date understanding, disambiguation, and more.
Top: GPT-5.2 — 95.3%