Benchmark Guide

What is LiveCodeBench? A Complete Guide for 2026

Last updated: October 2, 2026

Benchmark contamination is one of the biggest problems in AI evaluation. When a model's training data includes the questions it will later be tested on, its scores are artificially inflated and tell us little about genuine capability. LiveCodeBench was designed to solve this problem for coding benchmarks by continuously adding fresh problems that models could not have seen during training.

Current top scores: LiveCodeBench

Generated from the id8 dataset on October 2, 2026. Scores quoted further down this article were correct when it was written and may since have been overtaken; this table is the current picture.

#	Model	Provider	LiveCodeBench
1	DeepSeek V4-Pro	DeepSeek	93.5
2	DeepSeek V4-Flash	DeepSeek	91.6
3	Kimi K2.6	Moonshot AI	89.6
4	Nemotron 3 Ultra 550B A55B	NVIDIA	89
5	Step-3.5-Flash	Stepfun	86.4
6	Kimi K2.5	Moonshot AI	85
7	Qwen 3.5	Alibaba	83.6
8	DeepSeek V3.2	DeepSeek	83.3

See every model in the LLM Leaderboard.

What Does LiveCodeBench Measure?

LiveCodeBench measures real algorithmic problem-solving ability on competitive programming tasks. Problems are sourced from three major competitive programming platforms — Codeforces, LeetCode, and AtCoder — but crucially only from contests held after a given model's training cutoff date. This means the model cannot have seen these specific problems during pre-training, making scores a genuine test of reasoning rather than recall.

The benchmark covers a range of difficulty levels from introductory algorithm questions through hard competitive programming problems. It tests the model's ability to write correct, efficient code — not just plausible-looking code — because solutions are validated against test cases. Source: livecodebench.github.io

Why LiveCodeBench Is More Reliable Than HumanEval

The original HumanEval benchmark from 2021 has become largely saturated and heavily contaminated — many models score above 90%, and there is good evidence that training datasets include the exact function signatures and expected outputs. LiveCodeBench addresses these problems structurally:

Continuous refresh — New problems are added as competitive programming contests run, keeping the benchmark ahead of training cutoffs. A problem published on Codeforces in March 2026 cannot appear in a model's pre-training data that was frozen in December 2025.
Real competitive difficulty — Problems range from Codeforces Div. 2 A-level (simple) through Div. 1 D-level (very hard). This difficulty spread reveals capability differences between models that HumanEval's easy problems miss.
Multiple platforms — Using Codeforces, LeetCode, and AtCoder diversifies the problem style, rewarding genuine algorithmic knowledge over familiarity with any single platform's conventions.
Test-validated correctness — Partial or plausible-looking solutions that fail edge cases are marked wrong, the same way a real judge would evaluate them.

How LiveCodeBench Scores Work

LiveCodeBench reports pass@1 accuracy — the percentage of problems solved correctly on the first attempt. The scoring window typically covers problems from a specific date range (for example, the past 6 months), which is why reported scores can vary based on the exact evaluation window used. Here is what the score range means:

Below 20% — Only handles easy problems; limited algorithmic reasoning
20–40% — Solid on beginner/intermediate problems; struggles with harder algorithmic tasks
40–55% — Good general coding ability; can handle medium Codeforces and LeetCode problems
55–70% — Strong competitive programmer-level reasoning; top frontier model range for 2026
70%+ — Exceptional; approaches top human competitive programmers on benchmark range

Note that LiveCodeBench scores look lower than HumanEval scores for the same model. A model scoring 95% on HumanEval might only score 45% on LiveCodeBench — and the LiveCodeBench number is the more honest reflection of real coding capability.

2026 Top-Scoring Models on LiveCodeBench

Rank	Model	LiveCodeBench Score
1	DeepSeek R1 671B	65.9%
2	Phi-4-reasoning-plus 14B	53.1%
3	Llama 4 Scout	32.8%

Scores sourced from official model release papers and the LiveCodeBench leaderboard. Updated June 2026.

Known Weaknesses of LiveCodeBench

LiveCodeBench is a significant improvement over older coding benchmarks but it is not perfect:

Competitive programming bias — The problems reflect algorithmic contest style: optimized solutions, clever data structures, strict time limits. Real software engineering is mostly not like this. A model that excels at LiveCodeBench may still struggle with SWE-bench-style repository navigation and bug fixing.
Language coverage — The benchmark is evaluated primarily in Python. Models tuned for Python may score better than equally capable models that are stronger in other languages.
Problem difficulty distribution shifts — As the benchmark window moves forward in time, the mix of hard vs. easy problems can shift, making cross-window score comparisons imprecise.
No natural language instruction following — Problems are stated as competitive programming specifications, which have a specific format. This does not test how well a model handles vague or ambiguous real-world requests.
Evaluation date dependency — Because scores depend on which problem window is used, two published scores for the same model may differ by several percentage points if they use different date ranges.

Primary Source

LiveCodeBench project: livecodebench.github.io

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