LLM Reference
ARCactiveReasoning

ARC: AI2 Reasoning Challenge

Metric: Accuracy (higher is better)Introduced: 2018

Grade-school science multiple-choice questions partitioned into Easy and Challenge (hard) sets. ARC-Challenge is the standard evaluation variant.

Models ranked

5

tracked on this benchmark

Score band

94.8 – 78.6

best → lowest tracked

Snapshot trend

+5.69

May 28 → Jun 25 · 1 models

Leaderboard

Tracked models ranked by Accuracy (higher is better).

Compare candidates
#Model variant and provenanceScore
1
Llama 3 70B
Version: Not recordedHarness: Not recordedEvaluator: Not recordedObserved: May 28, 2026Confidence: Not recordedSource
94.8
2
Nemotron-Labs TwoTower 30B-A3B Base
Version: ARC-Challenge, 25-shot, acc_norm; default TwoTower diffusion decoding at confidence_threshold=0.8, block_size=16, BF16 on 2xH100; evaluator/harness not published on the model cardHarness: Not recordedEvaluator: Not recordedObserved: Jun 25, 2026Confidence: Not recordedSource
92.7
3
Gemma 2 27B
Version: Not recordedHarness: Not recordedEvaluator: Not recordedObserved: May 28, 2026Confidence: Not recordedSource
88.5
4
Mixtral 8x22B v0.1
Version: Not recordedHarness: Not recordedEvaluator: Not recordedObserved: May 28, 2026Confidence: Not recordedSource
86.0
5
Phi-3 Mini 128K
Version: Not recordedHarness: Not recordedEvaluator: Not recordedObserved: May 28, 2026Confidence: Not recordedSource
78.6

How to read this benchmark

This benchmark scores models where higher is better. Use scores for directional filtering and shortlisting, not universal quality ranking; then validate pricing, context window, provider availability, and fit for your workload.

Trust this score when

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

Be cautious when

  • There is only one benchmark snapshot or the dataset appears stale.
  • The 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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Last reviewed: Apr 15, 2026