{
 "body": "\n## What it measures\n\nLiveCodeBench asks whether a model can genuinely solve competitive-programming problems rather than\nrecall them, by drawing continuously from LeetCode, AtCoder and Codeforces and stamping every problem with\nits original release date. Beyond plain code generation, it also scores self-repair (correcting a wrong\nsolution given error feedback), test output prediction (working out what a given program prints for given\ninput) and code execution (reasoning through what a snippet does without running it) \u2014 four related but\ndistinct ways of testing code understanding rather than one.\n\n## How it is scored\n\nFor code generation, a submitted solution is run against test cases and scored Pass@1 (and Pass@5, sampling\nseveral attempts); the reference runner notes up to half a point of run-to-run variation from timing\neffects. The self-repair, test-output-prediction and code-execution scenarios reuse the same underlying\nproblem pool but change the task and the expected output format. The benchmark's headline feature is not\nthe metric itself but the ability to restrict scoring to a chosen date window \u2014 typically, problems\nreleased after a model's training cutoff \u2014 so a score can be read as a genuine out-of-distribution result\nrather than a possibly-memorised one.\n\n## Dataset and licence\n\nReleased under CC BY 4.0. The pool of problems grows continuously rather than being fixed: the original\npaper (submitted March 2024) evaluated roughly 400 problems from May 2023 to May 2024, while the live\nleaderboard checked for this page showed 454 problems in its own selected window (August 2024 to May\n2025), with an older \"release_v5\" snapshot linked separately \u2014 so any stated dataset size only makes sense\nalongside the date window it was measured over.\n\n## Who publishes it\n\nLiveCodeBench comes from Naman Jain, King Han, Alex Gu, Wen-Ding Li, Fanjia Yan, Tianjun Zhang, Sida Wang,\nArmando Solar-Lezama, Koushik Sen and Ion Stoica, researchers at UC Berkeley, MIT and Cornell, first\nsubmitted to arXiv in March 2024 with a revised version in June 2024. The project's own site\n(livecodebench.github.io) hosts the current leaderboard.\n\n## Lineage\n\nNo predecessor, successor or variant is catalogued for this benchmark in this repository.\n\n## Saturation and contamination\n\nThe leaderboard accessed for this page showed a top Pass@1 of 80.2% (O4-Mini, high reasoning effort)\nwithin its default August 2024-May 2025 window, with a link to an older leaderboard snapshot suggesting\nnewer, larger releases exist beyond what this pass opened. Because the site is explicitly built around a\nmoving date window rather than one fixed ranking, this number describes that window, not an all-time\nceiling. Contamination risk is low by design: problems are dated, and the reference tooling lets anyone\nrestrict evaluation to problems released after a model's training cutoff, which is precisely the mechanism\nthat gives the benchmark its name.\n\n## How to run it\n\nThe reference implementation is the `lcb_runner` package in the LiveCodeBench repository\n(`python -m lcb_runner.runner.main --model <name> --scenario codegeneration --evaluate`), with a\n`--release_version` flag selecting a dated snapshot and a separate scoring script for computing rankings\nover an arbitrary date range. It was not confirmed in the lm-evaluation-harness, HELM, OpenCompass or\nBIG-bench task lists, so a score quoted from one of those suites should not be assumed to use the same\ndate-window convention as the reference leaderboard.\n\n## Reading the numbers\n\nA high LiveCodeBench score is a stronger contamination signal than most fixed-benchmark code scores,\nprovided the reporter states which date window was used \u2014 a model evaluated only on problems from before\nits training cutoff tells you little about genuine generalisation. Always check the window (and which of\nthe four scenarios) before comparing two LiveCodeBench numbers: a code-generation Pass@1 on one window is\nnot comparable to a self-repair or test-output-prediction score, or to the same scenario on a different\nwindow.\n",
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   "provider_display": "Meta",
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   "as_of": "2026-04",
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   "provider_display": "Meta",
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  {
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   "provider_display": "Google DeepMind",
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   "as_of": "2026-04",
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   "display_name": "Qwen2.5 Coder 32B Instruct",
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   "provider_display": "Alibaba / Qwen Team",
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 ],
 "page": {
  "aliases": [
   "LCB",
   "livecodebench"
  ],
  "category": "coding",
  "contamination": {
   "note": "Contamination resistance is this benchmark's core design goal: every problem is tagged with its original contest release date, and the reference runner lets a user filter to problems released after a chosen cutoff, so a model can be scored only on problems it could not have seen during training. Risk rises for the oldest problems in the pool as more time passes and more models are trained after their release dates.\n",
   "risk": "low"
  },
  "dataset": {
   "languages": [],
   "license": "CC BY 4.0",
   "modalities": [
    "code",
    "text"
   ],
   "public_test_set": true,
   "size": null,
   "size_note": "The pool grows continuously as new contest problems are released, so its size depends on which date window is selected rather than being fixed. The original paper (submitted March 2024) evaluated roughly 400 problems released between May 2023 and May 2024; the live leaderboard accessed for this page showed 454 problems in its currently selected window (August 2024 to May 2025), with a link to an older \"release_v5\" leaderboard snapshot, implying later, larger releases exist beyond what this research pass opened.\n",
   "splits": "no fixed train/test split; problems are filtered by a user-selected release-date window",
   "url": "https://github.com/LiveCodeBench/LiveCodeBench"
  },
  "freshness": {
   "researched": "2026-09-08",
   "researched_by": "sonnet-5 agent, batch 1b, slice M",
   "reviewed": "",
   "reviewed_by": ""
  },
  "harness": {
   "bigbench": "",
   "helm": "",
   "inspect_evals": "",
   "lm_eval": "",
   "opencompass": "",
   "other": "Reference implementation is the `lcb_runner` Python package in the LiveCodeBench repository, run as `python -m lcb_runner.runner.main --model <name> --scenario codegeneration --evaluate`, with `--release_version` selecting a dated dataset snapshot and a separate `compute_scores.py` script recomputing rankings over an arbitrary start/end date window. Not confirmed in the lm-evaluation-harness, HELM, OpenCompass or BIG-bench task lists.\n"
  },
  "id": "live_code_bench",
  "last_updated": "",
  "leaderboard_url": "https://livecodebench.github.io/leaderboard.html",
  "lineage": {
   "family": "",
   "predecessor": "",
   "successors": [],
   "variants": []
  },
  "measures": "LiveCodeBench evaluates a model on competitive-programming problems collected continuously from LeetCode, AtCoder and Codeforces, going beyond plain code generation to also test self-repair (fixing a wrong solution given feedback), test output prediction (predicting what a given piece of code outputs) and code execution (simulating running code by hand). Because every problem is tagged with its original release date, a reader can score a model only on problems released after that model's training cutoff, directly addressing whether high scores reflect memorised solutions rather than genuine problem-solving.\n",
  "metric": {
   "baseline_note": "No formal human baseline published; the reference runner notes up to 0.5 points of run-to-run variation in Pass@1/Pass@5 from time-limit effects.",
   "direction": "higher_is_better",
   "human_baseline": null,
   "max_score": 100,
   "name": "Pass@1 (and Pass@5 for code generation)",
   "random_baseline": 0,
   "unit": "%"
  },
  "name": "LiveCodeBench",
  "page_kind": "benchmark",
  "paper": {
   "arxiv": "2403.07974",
   "title": "LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code",
   "url": "https://arxiv.org/abs/2403.07974",
   "year": 2024
  },
  "publisher": {
   "authors": [
    "Naman Jain",
    "King Han",
    "Alex Gu",
    "Wen-Ding Li",
    "Fanjia Yan",
    "Tianjun Zhang",
    "Sida Wang",
    "Armando Solar-Lezama",
    "Koushik Sen",
    "Ion Stoica"
   ],
   "org": "UC Berkeley, MIT and Cornell University",
   "url": "https://livecodebench.github.io/"
  },
  "released": "2024-03",
  "repo_url": "https://github.com/LiveCodeBench/LiveCodeBench",
  "saturation": {
   "as_of": "2025-05",
   "note": "On the code-generation leaderboard accessed 2026-09-08, the top model shown was O4-Mini (high effort) at 80.2% Pass@1 within the page's default problem window (August 2024 to May 2025); it is that window's end date, not the access date, that \"as of\" refers to here. The leaderboard explicitly frames itself around a moving window rather than a single all-time ranking, and links to an older \"release_v5\" snapshot, so the ranking read here is one slice of a benchmark designed to keep changing as new problems and models arrive, not a stable ceiling; a more recent window would likely show different, probably higher, top scores from newer models not evaluated in this pass.\n",
   "status": "watch",
   "top_score": 80.2
  },
  "sources": [
   {
    "accessed": "2026-09-08",
    "title": "LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code",
    "url": "https://arxiv.org/abs/2403.07974"
   },
   {
    "accessed": "2026-09-08",
    "title": "LiveCodeBench Leaderboard",
    "url": "https://livecodebench.github.io/leaderboard.html"
   },
   {
    "accessed": "2026-09-08",
    "title": "LiveCodeBench/LiveCodeBench repository",
    "url": "https://github.com/LiveCodeBench/LiveCodeBench"
   }
  ],
  "status": "active",
  "subcategory": "competitive programming, contamination-resistant via dated problems",
  "summary": "LiveCodeBench scores code generation, self-repair, test-output prediction and code execution on dated competitive-programming problems, filterable by a model's training cutoff.",
  "tags": [
   "coding",
   "competitive-programming",
   "contamination-resistant",
   "code-execution",
   "self-repair"
  ],
  "task_format": "For code generation, the model is given a natural-language problem statement (as posed on the source contest site) and must produce a working solution, evaluated against the contest's own or reconstructed test cases. The other three scenarios reuse the same problem pool but change what the model is asked to produce: a corrected solution given a failing one and error feedback (self-repair), the printed output of a given program on given input (test output prediction), or the result of executing a given snippet by reasoning about it directly (code execution).\n"
 }
}