reasoning benchmark
LiveCodeBench v6 is a benchmark for evaluating large language models on code using newly collected programming contest problems.
Updated Sep 5, 2026
Higher score ranks better on this benchmark.
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelAC | Score91.90% | Percentile100.00% | Participants62 | EvidenceC | Evaluated |
| Rank02 | ModelAC | Score91.90% | Percentile98.36% | Participants62 | EvidenceC | Evaluated |
| Rank03 | ModelAC | Score91.60% | Percentile96.72% | Participants62 | EvidenceC | Evaluated |
| Rank04 | ModelSA | Score90.33% | Percentile95.08% | Participants62 | EvidenceC | Evaluated |
| Rank05 | ModelAC | Score90.30% | Percentile93.44% | Participants62 | EvidenceC | Evaluated |
| Rank06 | ModelMA | Score89.60% | Percentile91.80% | Participants62 | EvidenceC | Evaluated |
| Rank07 | ModelAC | Score89.60% | Percentile90.16% | Participants62 | EvidenceC | Evaluated |
| Rank08 | ModelNV | Score89.00% | Percentile88.52% | Participants62 | EvidenceC | Evaluated |
| Rank09 | ModelBY | Score87.80% | Percentile86.89% | Participants62 | EvidenceC | Evaluated |
| Rank10 | ModelMI | Score87.70% | Percentile85.25% | Participants62 | EvidenceC | Evaluated |
| Rank11 | ModelAC | Score87.10% | Percentile83.61% | Participants62 | EvidenceC | Evaluated |
| Rank12 | ModelST | Score86.40% | Percentile81.97% | Participants62 | EvidenceC | Evaluated |
| Rank13 | ModelMA | Score85.00% | Percentile80.33% | Participants62 | EvidenceC | Evaluated |
| Rank14 | ModelZA | Score84.90% | Percentile78.69% | Participants62 | EvidenceC | Evaluated |
| Rank15 | ModelAC | Score83.90% | Percentile77.05% | Participants62 | EvidenceC | Evaluated |
| Rank16 | ModelAC | Score83.60% | Percentile75.41% | Participants62 | EvidenceC | Evaluated |
| Rank17 | ModelMA | Score83.10% | Percentile73.77% | Participants62 | EvidenceC | Evaluated |
| Rank18 | ModelZA | Score82.80% | Percentile72.13% | Participants62 | EvidenceC | Evaluated |
| Rank19 | ModelOP | Score81.90% | Percentile70.49% | Participants62 | EvidenceC | Evaluated |
| Rank20 | ModelBY | Score81.70% | Percentile68.85% | Participants62 | EvidenceC | Evaluated |
| Rank21 | ModelLA | Score81.40% | Percentile67.21% | Participants62 | EvidenceC | Evaluated |
| Rank22 | ModelLA | Score80.70% | Percentile65.57% | Participants62 | EvidenceC | Evaluated |
| Rank23 | ModelAC | Score80.70% | Percentile63.93% | Participants62 | EvidenceC | Evaluated |
| Rank24 | ModelXI | Score80.60% | Percentile62.30% | Participants62 | EvidenceC | Evaluated |
| Rank25 | ModelAC | Score80.40% | Percentile60.66% | Participants62 | EvidenceC | Evaluated |
| Rank26 | ModelGO | Score80.00% | Percentile59.02% | Participants62 | EvidenceC | Evaluated |
| Rank27 | ModelAC | Score78.90% | Percentile57.38% | Participants62 | EvidenceC | Evaluated |
| Rank28 | ModelGO | Score77.10% | Percentile55.74% | Participants62 | EvidenceC | Evaluated |
| Rank29 | ModelIB | Score75.77% | Percentile54.10% | Participants62 | EvidenceC | Evaluated |
| Rank30 | ModelAC | Score74.60% | Percentile52.46% | Participants62 | 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 v6 measures and how its scores work.
LiveCodeBench v6 is a benchmark for large language models for code that collects problems from LeetCode, AtCoder, and CodeForces and annotates them with release dates.
It measures code generation, self-repair, code execution, and test output prediction, reported as a Score ratio.
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 LiveCodeBench v6.
Qwen3.8-Flash-Next is currently ranked first with 91.90%.
The current leaders are Qwen3.8-Flash-Next (91.90%), Qwen3.8 Flash (91.90%), and Qwen3.7 Max (91.60%).
Step-3.5-Flash has the lowest matched official input price at $0.10 input / $0.30 output per 1M tokens.
The fastest matched records are GPT OSS 120B High (100.00 tok/s via OpenAI), GLM-4.6 (85.00 tok/s via DeepInfra), and K-EXAONE-236B-A23B (50.00 tok/s via FriendliAI).
No. This benchmark measures one defined capability or task. The overall LLMBoard score uses a separate aggregation across eligible benchmark evidence.
It measures code generation, self-repair, code execution, and test output prediction, reported as a Score ratio.
Yes. Higher values rank better for this benchmark.
62 model results are currently shown.
No. This benchmark is shown for reference but does not contribute to the overall score.
Ranking basisThis livecodebench v6 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
Qwen3.8-Flash-Next currently leads LiveCodeBench v6 with 91.90%. 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.