ARC: AI2 Reasoning Challenge
About
Grade-school science multiple-choice questions partitioned into Easy and Challenge (hard) sets. ARC-Challenge is the standard evaluation variant.Compare model scores alongside pricing, API availability, and release date — high benchmark score alone doesn't make a model the right pick.
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.
Current modeled score band for tracked entries is roughly 94.8 – 78.6.
Window
May 28
single timestamp
Mean delta
No trend
need another snapshot
Coverage
4
models in latest snapshot
Leaderboard preview (top 5)
- 1. Llama 3 70B94.8
- 2. Gemma 2 27B88.5
- 3. Mixtral 8x22B v0.186.0
- 4. Phi-3 Mini 128K78.6