LiveCodeBench v5
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.
Gemini 2.5 Pro from Google currently leads the LiveCodeBench v5 leaderboard with a score of 0.756 across 9 evaluated AI models.
Gemini 2.5 Pro leads with 75.6%, followed by Gemini 2.5 Flash at 63.9% and 
Qwen3 VL 235B A22B Instruct at 61.4%.
Progress Over Time
Interactive timeline showing model performance evolution on LiveCodeBench v5
LiveCodeBench v5 Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 | Google | — | 1.0M | $1.25 / $10.00 | ||
| 2 | Google | — | 1.0M | $0.30 / $2.50 | ||
| 3 |  Alibaba Cloud / Qwen Team | 236B | 262K | $0.30 / $1.49 | ||
| 4 |  OpenBMB | 9B | — | — | ||
| 5 | Google | — | — | — | ||
| 6 | Google | 8B | — | — | ||
| 6 | 2B | — | — | |||
| 8 | 2B | — | — | |||
| 8 | Google | 8B | — | — |
FAQ
Common questions about LiveCodeBench v5.
More evaluations to explore
Related benchmarks in the same category
A challenging dataset of 448 multiple-choice questions written by domain experts in biology, physics, and chemistry. Questions are Google-proof and extremely difficult, with PhD experts reaching 65% accuracy.
A more robust and challenging multi-task language understanding benchmark that extends MMLU by expanding multiple-choice options from 4 to 10, eliminating trivial questions, and focusing on reasoning-intensive tasks. Features over 12,000 curated questions across 14 domains and causes a 16-33% accuracy drop compared to original MMLU.
All 30 problems from the 2025 American Invitational Mathematics Examination (AIME I and AIME II), testing olympiad-level mathematical reasoning with integer answers from 000-999. Used as an AI benchmark to evaluate large language models' ability to solve complex mathematical problems requiring multi-step logical deductions and structured symbolic reasoning.
Massive Multitask Language Understanding benchmark testing knowledge across 57 diverse subjects including STEM, humanities, social sciences, and professional domains
A verified subset of 500 software engineering problems from real GitHub issues, validated by human annotators for evaluating language models' ability to resolve real-world coding issues by generating patches for Python codebases.
Humanity's Last Exam (HLE) is a multi-modal academic benchmark with 2,500 questions across mathematics, humanities, and natural sciences, designed to test LLM capabilities at the frontier of human knowledge with unambiguous, verifiable solutions