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Summary
Claude Opus 4.7 from Anthropic scored 90.0 on AIME 2026, placing it top 10 (rank 6 of 13) on this benchmark. This places the model in the saturated band for AIME 2026. Most frontier models cluster near this ceiling, so $cross-benchmark behavior/$ matters more than the headline score for $production decisions/$.
Model details
- Provider: Anthropic
- Model key:
anthropic/claude-opus-4.7 - Context length: 1,000,000 tokens
- License: Proprietary
- Open weights: no
Benchmark methodology
Benchmark goal: The benchmark is designed to evaluate single-shot mathematical problem-solving capabilities of LLMs across various advanced mathematical topics including algebra, geometry, number theory, and combinatorics.
Scoring metrics:
- Accuracy: Accuracy = (Number of Problems with Exact Three-Digit Integer Answers / Total Problems) * 100
Analysis
Key takeaways:
- The model achieved a high accuracy of 93.33% on advanced mathematical problems.
- It demonstrated robust mathematical reasoning, correctly solving complex problems in algebra, geometry, number theory, and combinatorics.
- The primary failure mode observed was in highly intricate enumeration and recursive counting problems, particularly when precise handling of complex definitions and substructure interactions was required.
- Despite high accuracy, certain combinatorial problems involving multi-level recursive definitions and subtle constraints remain challenging.
Failure modes observed
Common failure modes:
- Misinterpretation of non-overlapping conditions in tiling problems, specifically regarding the "cell loop" definition and its implications for recursion.
- Overlooking specific constraints for prime factorization, as seen in the 'number of ways to partition a 10x10 grid' question, leading to incorrect enumeration of sub-problem solutions.
- Minor calculation errors in multi-step problems, such as a miscalculation in the number of possible values for 'h' in the geometry problem, leading to off-by-one errors.
Example: In the question to 'Find the number of ways to partition a 10x10 grid of cells into 5 cell loops...', the model struggled with the recursive definition of 'H(a,b)' which led to an incorrect value calculation for the number of ways.
Secondary metrics
- Readability score: 0.0
- Toxicity score: 0.000
- Ethics score: 0.000
Run this evaluation yourself
LayerLens evaluates Claude Opus 4.7 continuously across 11+ benchmarks. To replicate this AIME 2026 evaluation on your own model, traces, or a different benchmark configuration, open the model in $LayerLens/$.
Source: LayerLens evaluation 69e12d9c7857c3e44c75698b. Updated 2026-04-16.