reasoning benchmark
LiveCodeBench is a benchmark for evaluating large language models on coding problems collected from LeetCode, AtCoder, and CodeForces.
Updated Sep 5, 2026
Higher score ranks better on this benchmark.
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelDE | Score93.50% | Percentile100.00% | Participants75 | EvidenceC | Evaluated |
| Rank02 | ModelDE | Score91.60% | Percentile98.65% | Participants75 | EvidenceC | Evaluated |
| Rank03 | ModelDE | Score88.40% | Percentile97.30% | Participants75 | EvidenceC | Evaluated |
| Rank04 | ModelUP | Score87.80% | Percentile95.95% | Participants75 | EvidenceC | Evaluated |
| Rank05 | ModelDE | Score83.30% | Percentile94.59% | Participants75 | EvidenceC | Evaluated |
| Rank06 | ModelDE | Score83.30% | Percentile93.24% | Participants75 | EvidenceC | Evaluated |
| Rank07 | ModelMI | Score83.00% | Percentile91.89% | Participants75 | EvidenceC | Evaluated |
| Rank08 | ModelME | Score82.80% | Percentile90.54% | Participants75 | EvidenceC | Evaluated |
| Rank09 | ModelNV | Score81.19% | Percentile89.19% | Participants75 | EvidenceC | Evaluated |
| Rank10 | ModelXA | Score80.40% | Percentile87.84% | Participants75 | EvidenceC | Evaluated |
| Rank11 | ModelXA | Score80.00% | Percentile86.49% | Participants75 | EvidenceC | Evaluated |
| Rank12 | ModelXA | Score79.40% | Percentile85.14% | Participants75 | EvidenceC | Evaluated |
| Rank13 | ModelXA | Score79.40% | Percentile83.78% | Participants75 | EvidenceC | Evaluated |
| Rank14 | ModelME | Score79.40% | Percentile82.43% | Participants75 | EvidenceC | Evaluated |
| Rank15 | ModelXA | Score79.00% | Percentile81.08% | Participants75 | EvidenceC | Evaluated |
| Rank16 | ModelMI | Score78.00% | Percentile79.73% | Participants75 | EvidenceC | Evaluated |
| Rank17 | ModelAM | Score74.60% | Percentile78.38% | Participants75 | EvidenceC | Evaluated |
| Rank18 | ModelDE | Score74.10% | Percentile77.03% | Participants75 | EvidenceC | Evaluated |
| Rank19 | ModelDE | Score73.30% | Percentile75.68% | Participants75 | EvidenceC | Evaluated |
| Rank20 | ModelZA | Score72.90% | Percentile74.32% | Participants75 | EvidenceC | Evaluated |
| Rank21 | ModelNV | Score71.10% | Percentile72.97% | Participants75 | EvidenceC | Evaluated |
| Rank22 | ModelAM | Score71.00% | Percentile71.62% | Participants75 | EvidenceC | Evaluated |
| Rank23 | ModelZA | Score70.70% | Percentile70.27% | Participants75 | EvidenceC | Evaluated |
| Rank24 | ModelAC | Score70.70% | Percentile68.92% | Participants75 | EvidenceC | Evaluated |
| Rank25 | ModelGO | Score69.00% | Percentile67.57% | Participants75 | EvidenceC | Evaluated |
| Rank26 | ModelIN | Score67.00% | Percentile66.22% | Participants75 | EvidenceC | Evaluated |
| Rank27 | ModelNV | Score66.31% | Percentile64.86% | Participants75 | EvidenceC | Evaluated |
| Rank28 | ModelAC | Score65.70% | Percentile63.51% | Participants75 | EvidenceC | Evaluated |
| Rank29 | ModelMI | Score65.00% | Percentile62.16% | Participants75 | EvidenceC | Evaluated |
| Rank30 | ModelMA | Score64.60% | Percentile60.81% | Participants75 | EvidenceC | 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 LiveCodeBench measures and how its scores work.
LiveCodeBench continuously collects programming contest problems and annotates them with release dates to support evaluation on problems released after a model's training cutoff.
It measures Score, reported as a ratio, across code generation, self-repair, code execution, and test output prediction.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of C.
LLM Stats. Benchmark scores retain their original unit. Overall score eligibility is shown separately.
Common questions about LiveCodeBench.
DeepSeek-V4-Pro-Max is currently ranked first with 93.50%.
The current leaders are DeepSeek-V4-Pro-Max (93.50%), DeepSeek-V4-Flash-Max (91.60%), and DeepSeek-V4-Flash-0423 (88.40%).
Min istral 3 (3B Reasoning 2512) has the lowest matched official input price at $0.04 input / $0.04 output per 1M tokens.
The fastest matched records are Min istral 3 (3B Reasoning 2512) (1,222.00 tok/s via Mistral AI), Llama 4 Scout (776.10 tok/s via Groq), and Mercury 2 (346.33 tok/s via Inception).
No. This benchmark measures one defined capability or task. The overall LLMBoard score uses a separate aggregation across eligible benchmark evidence.
It measures Score, reported as a ratio, across code generation, self-repair, code execution, and test output prediction.
Yes. Higher values rank better for this benchmark.
75 model results are currently shown.
No. This benchmark is shown for reference but does not contribute to the overall score.
Ranking basisThis livecodebench 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
DeepSeek-V4-Pro-Max currently leads LiveCodeBench with 93.50%. 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.