Benchmark · coding

LiveCodeBench

20 results 18 models

LiveCodeBench evaluates how well large language models solve competitive-programming problems, using coding challenges collected continuously from recent contests to avoid training-data contamination. The headline metric is pass@1 — the share of problems the model solves correctly on its first attempt.

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Example
A typical item is a contest-style coding problem — for example, given an array of integers, write a function that returns the length of the longest strictly increasing subsequence — which the model must solve by generating working code.
Scoring
Each generated solution is run against the problem's test cases; pass@1 is the fraction of problems for which the model's first submission passes all the tests.
Verification
A solution is accepted automatically when the generated code passes every hidden and public test case for that problem — no human judging or exact-text matching is involved.
Why it matters
Because its problems come from contests released after a model's training cutoff, LiveCodeBench measures genuine reasoning and coding ability rather than memorized answers, making it a trusted, contamination-resistant gauge of coding skill.
Worked example
Task
Competitive-programming style (stdin/stdout). Given n integers, count the pairs (i, j) with i < j such that nums[i] + nums[j] is even. Input: first line is n, second line is n space-separated integers; print the count.
Solution
import sys

def main():
    data = sys.stdin.read().split()
    n = int(data[0])
    nums = list(map(int, data[1:1 + n]))
    odd = sum(x % 2 for x in nums)
    even = n - odd
    print(odd * (odd - 1) // 2 + even * (even - 1) // 2)

main()
Walkthrough
A sum is even exactly when both addends have the same parity, so the answer is C(evens, 2) + C(odds, 2); counting parities is O(n). LiveCodeBench runs the model's program against hidden unit tests and scores it pass@1 — correct only if every test passes.
0 23 46 69 92 2024-07-15 2025-07-07 2026-06-30 Qwen2-72B-Instruct · 35.7 · 2024-07-15 Qwen2-72B-Instruct · 35.7 · 2024-07-15 QwQ-32B-Preview · 50 · 2024-11-28 DeepSeek-R1-Distill-Qwen-32B · 57.2 · 2025-01-22 Kimi k1.5 (short-CoT) · 47.3 · 2025-01-22 Grok 3 (Think) · 79.4 · 2025-02-19 Grok 3 mini · 80.4 · 2025-02-19 Grok 3 · 79.4 · 2025-03-11 DeepSeek-V3-0324 · 49.2 · 2025-03-24 Gemini 2.5 Pro · 70.4 · 2025-03-26 Gemini 2.0 Flash · 34.5 · 2025-04-15 Qwen3-235B-A22B · 70.7 · 2025-05-14 Deepseek-R1-0528 · 73.3 · 2025-05-30 Kimi K2 · 53.7 · 2025-07-28 Kimi K2 · 53.7 · 2025-07-28 GLM-4.6 · 82.8 · 2025-09-30 Kimi K2 Thinking · 83.1 · 2025-11-07 Step 3.5 Flash · 86.4 · 2026-02-10 Grok 4 · 81.9 · 2026-02-25 Agents-A1 · 44.3 · 2026-06-30
Qwen2-72B-Instruct QwQ-32B-Preview DeepSeek-R1-Distill-Qwen-32B Kimi k1.5 (short-CoT) Grok 3 (Think) Grok 3 mini Grok 3 DeepSeek-V3-0324 Gemini 2.5 Pro Gemini 2.0 Flash Qwen3-235B-A22B Deepseek-R1-0528 Kimi K2 GLM-4.6 Kimi K2 Thinking Step 3.5 Flash Grok 4 Agents-A1
Timeline
Date Model Score Source
2026-06-30 Agents-A1 44.3pts Scaling the Horizon, Not the Parameters: Reaching Trillion-Parameter Performance with a 35B Agent
2026-02-25 Grok 4 81.9% xAI releases Grok 4 reasoning model with strong agentic and coding benchmarks
2026-02-10 Step 3.5 Flash 86.4% Step 3.5 Flash: open 11B-active MoE model achieves frontier-level agentic performance
2025-11-07 Kimi K2 Thinking 83.1% Moonshot AI releases Kimi K2 Thinking, an open-source 1T parameter reasoning model
2025-09-30 GLM-4.6 82.8% Zhipu AI releases GLM-4.6 with agentic capabilities and 128k context window
2025-07-28 Kimi K2 53.7% Kimi K2: open MoE model with 1T parameters and MuonClip optimizer
2025-07-28 Kimi K2 53.7% Moonshot releases Kimi K2, an open agentic MoE model with 32B activated parameters
2025-05-30 Deepseek-R1-0528 73.3% DeepSeek updates R1 model with improved reasoning and benchmark scores
2025-05-14 Qwen3-235B-A22B 70.7% Qwen3 introduces unified thinking mode and 119-language support
2025-04-15 Gemini 2.0 Flash 34.5% Google releases Gemini 2.0 Flash with enhanced quality and twice the speed of Gemini 1.5 Pro
2025-03-26 Gemini 2.5 Pro 70.4% Google releases experimental Gemini 2.5 Pro reasoning model with 1M token context
2025-03-24 DeepSeek-V3-0324 49.2% DeepSeek-V3-0324 improves reasoning, coding, and Chinese writing over DeepSeek-V3
2025-03-11 Grok 3 79.4% xAI releases Grok 3 with deep reasoning and million-token context
2025-02-19 Grok 3 (Think) 79.4% xAI releases Grok 3 Beta and DeepSearch agent
2025-02-19 Grok 3 mini 80.4% xAI releases Grok 3 Beta and DeepSearch agent
2025-01-22 DeepSeek-R1-Distill-Qwen-32B 57.2% DeepSeek releases R1 reasoning models via reinforcement learning
2025-01-22 Kimi k1.5 (short-CoT) 47.3% Kimi k1.5 scales reinforcement learning with LLMs to match o1 and beat short-CoT models
2024-11-28 QwQ-32B-Preview 50.0% Qwen releases QwQ-32B-Preview, an experimental reasoning model
2024-07-15 Qwen2-72B-Instruct 35.7% Qwen Team releases Qwen2 series with 72B dense and MoE models
2024-07-15 Qwen2-72B-Instruct 35.7% Qwen2 Technical Report introduces dense and MoE models up to 72B