reasoning benchmark
CRUX-O is the output-prediction part of the CRUXEval benchmark, comprising 800 Python functions of 3–13 lines that evaluate AI models' code reasoning, understanding, and execution.
Updated Sep 5, 2026
Higher score ranks better on this benchmark.
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelAC | Score0.79 points | Percentile100.00% | Participants1 | EvidenceC | Evaluated |
The leading models and scores on this benchmark.
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
Ranking basisThis crux-o AI model leaderboard uses descending score in the benchmark original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.
Selection summary
Qwen3 235B A22B currently leads CRUX-O with 0.79 points. It is the top model on this specific benchmark, while the best LLM for the broader task should also be checked against other benchmarks, price, and runtime.
Use this leaderboard with the supporting benchmark results and coverage details above. A leaderboard position summarizes the selected ranking signal; it does not replace workload-specific testing.
What CRUX-O measures and how its scores work.
CRUX-O (output prediction) is part of the CRUXEval benchmark and consists of 800 Python functions designed to assess AI models by asking them to predict function outputs from code and inputs.
It measures the ability to predict correct Python function outputs from function code and inputs, reported as Score in points.
Scores are shown in points. This benchmark is not independently verified and has an evidence level of B.
LLM Stats. Benchmark scores retain their original unit. Overall score eligibility is shown separately.
Common questions about CRUX-O.
Qwen3 235B A22B is currently ranked first with 0.79 points.
The current leaders are Qwen3 235B A22B (0.79 points).
Qwen3 235B A22B has the lowest matched official input price at $0.70 input / $2.8 output per 1M tokens.
The fastest matched records are Qwen3 235B A22B (21.74 tok/s via DeepInfra).
No. This benchmark measures one defined capability or task. The overall LLMBoard score uses a separate aggregation across eligible benchmark evidence.
It measures the ability to predict correct Python function outputs from function code and inputs, reported as Score in points.
Yes. Higher values rank better for this benchmark.
1 model results are currently shown.
No. This benchmark is shown for reference but does not contribute to the overall score.