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Summary
Claude Opus 4.5 from Anthropic scored 76.8 on LiveCodeBench, placing it third of 43 on this benchmark. This places the model in the high-tier band for LiveCodeBench. Production-deployable on this benchmark family with margin for prompt and judge variance.
Model details
- Provider: Anthropic
- Model key:
anthropic/claude-opus-4.5 - Context length: 200,000 tokens
- License: Proprietary
- Open weights: no
Benchmark methodology
Benchmark goal: Evaluate the LLM's ability to solve competitive programming problems, requiring logical reasoning, algorithm design, and coding skills.
Scoring metrics:
- Pass Rate: Percentage of problems for which the LLM generated code that passed all test cases.
Analysis
Key takeaways:
- Claude Opus 4.5 exhibits strong performance on problems that can be solved with straightforward greedy, dynamic programming, or simulation approaches without overly complex edge-case interactions.
- The model struggles with problems requiring highly optimized algorithms for very large constraints (e.g., N > 10^5) or those requiring deep theoretical insights.
- Its ability to self-correct during problem-solving is evident in many successful completions, but the thought process sometimes goes astray when encountering novel or highly abstract problem types.
- The model's performance indicates a need for improved complex combinatorial and number-theoretic reasoning.
Failure modes observed
Common failure modes:
- Misinterpretation of complex logical conditions.
- Suboptimal algorithmic choice for problems with large constraints, leading to Time Limit Exceeded (TLE) or incorrect logic for specific edge cases.
- Incorrect derivation of mathematical properties or recurrence relations for combinatorial problems.
- Failure to account for all permutations or symmetries in counting problems.
- Incorrectly applying or deriving properties of number theory.
Example: In solve(N, M, C, K, A): The model's approach to calculate the modulo sum involves a period-based computation. While conceptually sound, it misapplies the logic when K is very large compared to the period, leading to an incorrect result.
Secondary metrics
- Readability score: 0.0
- Toxicity score: 0.000
- Ethics score: 0.000
Run this evaluation yourself
LayerLens evaluates Claude Opus 4.5 continuously across 11+ benchmarks. To replicate this LiveCodeBench evaluation on your own model, traces, or a different benchmark configuration, open the model in $LayerLens/$.
Source: LayerLens evaluation 69259eb39171c00f3f0768e9. Updated 2025-11-25.