reasoning benchmark
ARC-AGI v2 is a benchmark for evaluating AI systems on abstract reasoning and problem-solving through visual grid transformation tasks.
Updated Sep 5, 2026
Higher score ranks better on this benchmark.
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelOP | Score95.00% | Percentile100.00% | Participants18 | EvidenceC | Evaluated |
| Rank02 | ModelOP | Score85.00% | Percentile94.12% | Participants18 | EvidenceC | Evaluated |
| Rank03 | ModelGO | Score77.10% | Percentile88.24% | Participants18 | EvidenceC | Evaluated |
| Rank04 | ModelOP | Score73.30% | Percentile82.35% | Participants18 | EvidenceC | Evaluated |
| Rank05 | ModelGO | Score72.10% | Percentile76.47% | Participants18 | EvidenceC | Evaluated |
| Rank06 | ModelAN | Score68.80% | Percentile70.59% | Participants18 | EvidenceC | Evaluated |
| Rank07 | ModelAN | Score58.30% | Percentile64.71% | Participants18 | EvidenceC | Evaluated |
| Rank08 | ModelOP | Score54.20% | Percentile58.82% | Participants18 | EvidenceC | Evaluated |
| Rank09 | ModelOP | Score52.90% | Percentile52.94% | Participants18 | EvidenceC | Evaluated |
| Rank10 | ModelME | Score42.50% | Percentile47.06% | Participants18 | EvidenceC | Evaluated |
| Rank11 | ModelTM | Score40.10% | Percentile41.18% | Participants18 | EvidenceC | Evaluated |
| Rank12 | ModelAN | Score37.60% | Percentile35.29% | Participants18 | EvidenceC | Evaluated |
| Rank13 | ModelGO | Score33.60% | Percentile29.41% | Participants18 | EvidenceC | Evaluated |
| Rank14 | ModelGO | Score31.10% | Percentile23.53% | Participants18 | EvidenceC | Evaluated |
| Rank15 | ModelXA | Score15.90% | Percentile17.65% | Participants18 | EvidenceC | Evaluated |
| Rank16 | ModelAN | Score8.60% | Percentile11.76% | Participants18 | EvidenceB | Evaluated |
| Rank17 | ModelOP | Score6.50% | Percentile5.88% | Participants18 | EvidenceB | Evaluated |
| Rank18 | ModelGO | Score4.90% | Percentile0.00% | Participants18 | EvidenceB | Evaluated |
The leading models and scores on this benchmark.
A closer view of the leading 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.
What ARC-AGI v2 measures and how its scores work.
It presents input-output grid pairs with colored cells and requires models to identify transformation rules from demonstration examples and apply them to test cases.
It measures abstract reasoning, problem-solving, fluid intelligence, spatial reasoning, pattern recognition, and compositional generalization using the Score metric in ratio units.
Scores are shown in ratio. 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 ARC-AGI v2.
GPT-6 Astra is currently ranked first with 95.00%.
The current leaders are GPT-6 Astra (95.00%), GPT-5.5 (85.00%), and Gemini 3.1 Pro (77.10%).
Gemini 3 Flash has the lowest matched official input price at $0.50 input / $3.0 output per 1M tokens.
The fastest matched records are Gemini 3.5 Flash (210.94 tok/s via Google), GPT-5.5 (134.94 tok/s via OpenAI), and Gemini 3 Flash (124.32 tok/s via Google).
No. This benchmark measures one defined capability or task. The overall LLMBoard score uses a separate aggregation across eligible benchmark evidence.
It measures abstract reasoning, problem-solving, fluid intelligence, spatial reasoning, pattern recognition, and compositional generalization using the Score metric in ratio units.
Yes. Higher values rank better for this benchmark.
18 model results are currently shown.
No. This benchmark is shown for reference but does not contribute to the overall score.
Ranking basisThis arc-agi v2 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
GPT-6 Astra currently leads ARC-AGI v2 with 95.00%. 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.