reasoning benchmark
LiveCodeBench Leaderboard
LiveCodeBench is a holistic and contamination-free evaluation benchmark for large language models for code. It continuously collects new problems from programming contests (LeetCode, AtCoder, CodeForces) and evaluates four different scenarios: code generation, self-repair, code execution, and test output prediction. Problems are annotated with release dates to enable evaluation on unseen problems released after a model's training cutoff.
Updated Aug 17, 2026
Models75
Model coverage75
MetricScore
EvidenceC
LiveCodeBench Ranking
Higher score ranks better on this benchmark.
Sort by
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelDE | Score93.5% | Percentile100.0% | Participants75 | EvidenceC | Evaluated |
| Rank02 | ModelDE | Score91.6% | Percentile98.7% | Participants75 | EvidenceC | Evaluated |
| Rank03 | ModelDE | Score88.4% | Percentile97.3% | Participants75 | EvidenceC | Evaluated |
| Rank04 | ModelUP | Score87.8% | Percentile96.0% | Participants75 | EvidenceC | Evaluated |
| Rank05 | ModelDE | Score83.3% | Percentile94.6% | Participants75 | EvidenceC | Evaluated |
| Rank06 | ModelDE | Score83.3% | Percentile93.2% | Participants75 | EvidenceC | Evaluated |
| Rank07 | ModelMI | Score83.0% | Percentile91.9% | Participants75 | EvidenceC | Evaluated |
| Rank08 | ModelME | Score82.8% | Percentile90.5% | Participants75 | EvidenceC | Evaluated |
| Rank09 | ModelNV | Score81.2% | Percentile89.2% | Participants75 | EvidenceC | Evaluated |
| Rank10 | ModelXA | Score80.4% | Percentile87.8% | Participants75 | EvidenceC | Evaluated |
| Rank11 | ModelXA | Score80.0% | Percentile86.5% | Participants75 | EvidenceC | Evaluated |
| Rank12 | ModelXA | Score79.4% | Percentile85.1% | Participants75 | EvidenceC | Evaluated |
| Rank13 | ModelXA | Score79.4% | Percentile83.8% | Participants75 | EvidenceC | Evaluated |
| Rank14 | ModelME | Score79.4% | Percentile82.4% | Participants75 | EvidenceC | Evaluated |
| Rank15 | ModelXA | Score79.0% | Percentile81.1% | Participants75 | EvidenceC | Evaluated |
| Rank16 | ModelMI | Score78.0% | Percentile79.7% | Participants75 | EvidenceC | Evaluated |
| Rank17 | ModelAM Nova 2 ProAmazon | Score74.6% | Percentile78.4% | Participants75 | EvidenceC | Evaluated |
| Rank18 | ModelDE | Score74.1% | Percentile77.0% | Participants75 | EvidenceC | Evaluated |
| Rank19 | ModelDE | Score73.3% | Percentile75.7% | Participants75 | EvidenceC | Evaluated |
| Rank20 | ModelZA | Score72.9% | Percentile74.3% | Participants75 | EvidenceC | Evaluated |
| Rank21 | ModelNV | Score71.1% | Percentile73.0% | Participants75 | EvidenceC | Evaluated |
| Rank22 | ModelAMNova 2 LiteAmazon | Score71.0% | Percentile71.6% | Participants75 | EvidenceC | Evaluated |
| Rank23 | ModelZA | Score70.7% | Percentile70.3% | Participants75 | EvidenceC | Evaluated |
| Rank24 | ModelAC | Score70.7% | Percentile68.9% | Participants75 | EvidenceC | Evaluated |
| Rank25 | ModelGO | Score69.0% | Percentile67.6% | Participants75 | EvidenceC | Evaluated |
| Rank26 | ModelIN | Score67.0% | Percentile66.2% | Participants75 | EvidenceC | Evaluated |
| Rank27 | ModelNV | Score66.3% | Percentile64.9% | Participants75 | EvidenceC | Evaluated |
| Rank28 | ModelAC | Score65.7% | Percentile63.5% | Participants75 | EvidenceC | Evaluated |
| Rank29 | ModelMI | Score65.0% | Percentile62.2% | Participants75 | EvidenceC | Evaluated |
| Rank30 | ModelMA | Score64.6% | Percentile60.8% | Participants75 | EvidenceC | Evaluated |
| Rank31 | ModelMA | Score63.6% | Percentile59.5% | Participants75 | EvidenceC | Evaluated |
| Rank32 | ModelAC | Score63.4% | Percentile58.1% | Participants75 | EvidenceC | Evaluated |
| Rank33 | ModelAC | Score62.6% | Percentile56.8% | Participants75 | EvidenceC | Evaluated |
| Rank34 | ModelMI | Score62.3% | Percentile55.4% | Participants75 | EvidenceC | Evaluated |
| Rank35 | ModelMA | Score61.6% | Percentile54.0% | Participants75 | EvidenceC | Evaluated |
| Rank36 | ModelDE | Score57.5% | Percentile52.7% | Participants75 | EvidenceC | Evaluated |
| Rank37 | ModelDE | Score57.2% | Percentile51.4% | Participants75 | EvidenceC | Evaluated |
| Rank38 | ModelDE | Score56.4% | Percentile50.0% | Participants75 | EvidenceC | Evaluated |
| Rank39 | ModelAC | Score55.5% | Percentile48.6% | Participants75 | EvidenceC | Evaluated |
| Rank40 | ModelMA | Score54.8% | Percentile47.3% | Participants75 | EvidenceC | Evaluated |
| Rank41 | ModelMI | Score53.8% | Percentile46.0% | Participants75 | EvidenceC | Evaluated |
| Rank42 | ModelMA | Score53.7% | Percentile44.6% | Participants75 | EvidenceC | Evaluated |
| Rank43 | ModelDE | Score53.1% | Percentile43.2% | Participants75 | EvidenceC | Evaluated |
| Rank44 | ModelMI | Score53.1% | Percentile41.9% | Participants75 | EvidenceC | Evaluated |
| Rank45 | ModelMA | Score51.3% | Percentile40.5% | Participants75 | EvidenceC | Evaluated |
| Rank46 | ModelMA | Score50.3% | Percentile39.2% | Participants75 | EvidenceC | Evaluated |
| Rank47 | ModelDE | Score50.0% | Percentile37.8% | Participants75 | EvidenceC | Evaluated |
| Rank48 | ModelAC | Score50.0% | Percentile36.5% | Participants75 | EvidenceC | Evaluated |
| Rank49 | ModelDE | Score49.2% | Percentile35.1% | Participants75 | EvidenceC | Evaluated |
| Rank50 | ModelME | Score48.0% | Percentile33.8% | Participants75 | EvidenceC | Evaluated |
| Rank51 | ModelME | Score43.4% | Percentile32.4% | Participants75 | EvidenceC | Evaluated |
| Rank52 | ModelDE | Score39.6% | Percentile31.1% | Participants75 | EvidenceC | Evaluated |
| Rank53 | ModelDE | Score37.6% | Percentile29.7% | Participants75 | EvidenceC | Evaluated |
| Rank54 | ModelDE | Score37.6% | Percentile28.4% | Participants75 | EvidenceC | Evaluated |
| Rank55 | ModelGO | Score35.1% | Percentile27.0% | Participants75 | EvidenceC | Evaluated |
| Rank56 | ModelMA | Score34.4% | Percentile25.7% | Participants75 | EvidenceC | Evaluated |
| Rank57 | ModelMA | Score34.4% | Percentile24.3% | Participants75 | EvidenceC | Evaluated |
| Rank58 | ModelMA | Score34.4% | Percentile23.0% | Participants75 | EvidenceC | Evaluated |
| Rank59 | ModelMA | Score34.4% | Percentile21.6% | Participants75 | EvidenceC | Evaluated |
| Rank60 | ModelGO | Score33.7% | Percentile20.3% | Participants75 | EvidenceC | Evaluated |
| Rank61 | ModelME | Score32.8% | Percentile18.9% | Participants75 | EvidenceC | Evaluated |
| Rank62 | ModelAC | Score31.4% | Percentile17.6% | Participants75 | EvidenceC | Evaluated |
| Rank63 | ModelGO | Score30.9% | Percentile16.2% | Participants75 | EvidenceC | Evaluated |
| Rank64 | ModelGO | Score29.7% | Percentile14.9% | Participants75 | EvidenceC | Evaluated |
| Rank65 | ModelAC | Score28.7% | Percentile13.5% | Participants75 | EvidenceC | Evaluated |
| Rank66 | ModelAC | Score26.6% | Percentile12.2% | Participants75 | EvidenceC | Evaluated |
| Rank67 | ModelGO | Score24.6% | Percentile10.8% | Participants75 | EvidenceC | Evaluated |
| Rank68 | ModelAC | Score18.2% | Percentile9.5% | Participants75 | EvidenceC | Evaluated |
| Rank69 | ModelDE | Score16.9% | Percentile8.1% | Participants75 | EvidenceC | Evaluated |
| Rank70 | ModelGO | Score13.2% | Percentile6.8% | Participants75 | EvidenceC | Evaluated |
| Rank71 | ModelGO | Score13.2% | Percentile5.4% | Participants75 | EvidenceC | Evaluated |
| Rank72 | ModelGO | Score13.2% | Percentile4.0% | Participants75 | EvidenceC | Evaluated |
| Rank73 | ModelGO | Score13.2% | Percentile2.7% | Participants75 | EvidenceC | Evaluated |
| Rank74 | ModelGO | Score12.6% | Percentile1.4% | Participants75 | EvidenceC | Evaluated |
| Rank75 | ModelGO | Score1.9% | Percentile0.0% | Participants75 | EvidenceC | Evaluated |
LiveCodeBench Highlights
The leading models and scores on this benchmark.
LiveCodeBench Score Distribution
A closer view of the leading scores on this benchmark.
The Top AI Models for LiveCodeBench
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
Ranking basisThis livecodebench AI model leaderboard uses descending score in the benchmark's original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.
- 01DEDeepSeek-V4-Pro-MaxDeepSeek
- Score
- 93.5%
- Price
- $0.43 input / $0.87 output per 1M tokens
- Speed
- Up to 18 tok/s via Fireworks
Strengths
- Ranks #1 of 75 compared models
- 100th percentile on this benchmark
- C evidence result
Considerations
- This result measures LiveCodeBench, not total model capability
- 02DEDeepSeek-V4-Flash-MaxDeepSeek
- Score
- 91.6%
- Price
- $0.14 input / $0.28 output per 1M tokens
- Speed
- Up to 19 tok/s via Novita
Strengths
- Ranks #2 of 75 compared models
- 99th percentile on this benchmark
- C evidence result
Considerations
- This result measures LiveCodeBench, not total model capability
- 03DEDeepSeek-V4-Flash-0423DeepSeek
- Score
- 88.4%
- Price
- $0.14 input / $0.28 output per 1M tokens
- Speed
- Up to 19 tok/s via Novita
Strengths
- Ranks #3 of 75 compared models
- 97th percentile on this benchmark
- C evidence result
Considerations
- This result measures LiveCodeBench, not total model capability
- 04UPSolar Pro 4Upstage
- Score
- 87.8%
- Price
- $0.30 input / $1.2 output per 1M tokens
Strengths
- Ranks #4 of 75 compared models
- 96th percentile on this benchmark
- C evidence result
Considerations
- This result measures LiveCodeBench, not total model capability
- 05DEDeepSeek-V3.2 (Thinking)DeepSeek
- Score
- 83.3%
- Price
- $0.14 input / $0.28 output per 1M tokens
- Speed
- Up to 50 tok/s via DeepSeek
Strengths
- Ranks #5 of 75 compared models
- 95th percentile on this benchmark
- C evidence result
Considerations
- This result measures LiveCodeBench, not total model capability
Selection summary
Best AI Models for LiveCodeBench
DeepSeek-V4-Pro-Max currently leads LiveCodeBench with 93.5%. 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.
What is LiveCodeBench?
What LiveCodeBench measures and how its scores work.
LiveCodeBench is a holistic and contamination-free evaluation benchmark for large language models for code. It continuously collects new problems from programming contests (LeetCode, AtCoder, CodeForces) and evaluates four different scenarios: code generation, self-repair, code execution, and test output prediction. Problems are annotated with release dates to enable evaluation on unseen problems released after a model's training cutoff.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of C.
- Family
- LiveCodeBench
- Modality
- text
- Primary category
- reasoning
- Score direction
- higher
- LLMBoard eligible
- Yes
- Evaluation key
- livecodebench|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about LiveCodeBench.
Which model scores highest on LiveCodeBench?
DeepSeek-V4-Pro-Max is currently ranked first with 93.5%.
What does LiveCodeBench measure?
LiveCodeBench is a holistic and contamination-free evaluation benchmark for large language models for code. It continuously collects new problems from programming contests (LeetCode, AtCoder, CodeForces) and evaluates four different scenarios: code generation, self-repair, code execution, and test output prediction. Problems are annotated with release dates to enable evaluation on unseen problems released after a model's training cutoff.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
75 model results are currently shown.
Does this benchmark affect the overall score?
Yes. This benchmark can contribute to the current LLMBoard capability score.