LLM Reference
activeCoding

CRUXEval

Metric: Pass@1Introduced: 2024

About

800 Python function input-output reasoning problems testing code execution understanding rather than code generation. Two tasks: input prediction and output prediction.

How to read this benchmark

This benchmark uses a scoring system where higher is better. Scores are useful for directional filtering and model shortlisting, not for universal quality ranking.

Interpretation checklist: prefer benchmarks that are closest to your workload style, then validate against the linked model pages for pricing, context window, and provider availability.

Trust when:

  • There is a fresh timestamped snapshot (or multiple snapshots) for this benchmark.
  • Model list covers the same version family you can actually deploy today.
  • Top candidates overlap with your required routing and feature requirements.

Don't trust when:

  • There is only one benchmark snapshot or the dataset appears stale.
  • Benchmark metric direction is opposite of your decision objective.
  • The score difference between options is narrow and likely within implementation variance.

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