LiveCodeBench¶

What it is¶

LiveCodeBench is a holistic benchmark for evaluating Large Language Models (LLMs) on coding tasks using a "contamination-free" approach. It continuously collects new problems from competitive programming platforms (LeetCode, AtCoder, Codeforces) to ensure models are tested on data they haven't seen during training.

What problem it solves¶

Traditional coding benchmarks like HumanEval and MBPP suffer from data contamination, as their problems are widely available in pre-training datasets. LiveCodeBench provides a dynamic, time-indexed evaluation that measures a model's true generalization and reasoning abilities rather than its ability to recall training data.

Where it fits in the stack¶

Eval. It is a critical tool for model providers and researchers to benchmark coding intelligence across time, specifically tracking performance on 'frontier' problems released after a model's knowledge cutoff.

Typical use cases¶

Frontier Model Evaluation: Comparing the coding capabilities of newly released models (e.g., Claude 4.8 vs. GPT-5.5).
Contamination Analysis: Determining if a model's performance on older benchmarks is due to memorization.
Holistic Assessment: Evaluating code generation, self-repair, and test-case generation in a unified framework.

Strengths¶

Contamination-Free: Problems are sourced from competitive programming contests released after model cutoffs.
Holistic: Covers multiple scenarios including code generation, execution reasoning, and debugging.
Time-Indexed: Allows for evaluating models based on the specific date a problem was released.
High Quality: Leverages curated problems with comprehensive test cases from top competitive platforms.

Limitations¶

Focus on Competitive Programming: Less focus on large-scale repository-level software engineering tasks.
Python Centricity: While the platforms support many languages, most LLM evaluation focuses on the Python subset.
High Difficulty: Problems can be significantly harder than typical enterprise coding tasks.

When to use it¶

When evaluating the "true" coding intelligence of a new model released after 2023.
When you need to understand a model's ability to reason about code execution rather than just writing it.
To track progress in LLM coding capabilities over time.

When not to use it¶

For base models that have not undergone instruction tuning for coding tasks.
If you require evaluation of large-scale repository-level software engineering (consider SWE-bench instead).
For evaluating simple API usage or boilerplate generation.

Getting started¶

LiveCodeBench is primarily used as a leaderboard and a dataset for model evaluation. You can interact with it via its official website or by using the evaluation scripts provided in its GitHub repository.

1. Installation¶

git clone https://github.com/LiveCodeBench/LiveCodeBench
cd LiveCodeBench
pip install -r requirements.txt

2. Setting up Credentials¶

Configure your LLM provider API keys in a .env file or as environment variables.

CLI examples¶

Running Evaluation¶

You can use the LiveCodeBench runner to evaluate a model's generations.

python -m lcb_runner.evaluation.main \
    --model_name "openai/gpt-5.5" \
    --scenario "codegeneration" \
    --release_date "2026-01-01"

Filtering by Difficulty¶

Run evaluation on a specific difficulty tier:

python -m lcb_runner.evaluation.main \
    --model_name "anthropic/claude-4.8" \
    --scenario "codegeneration" \
    --difficulty "Hard"

Execution Reasoning¶

Evaluate the model's ability to predict the output of a given code snippet:

python -m lcb_runner.evaluation.main \
    --model_name "openai/gpt-5.5" \
    --scenario "execution"

API examples¶

Data Structure (JSON)¶

Each problem in the benchmark includes metadata about its release date and platform.

{
    "question_id": "1234",
    "title": "Example Problem",
    "platform": "LeetCode",
    "release_date": "2026-02-15",
    "difficulty": "Hard",
    "test_cases": [...]
}

Programmatic Access¶

from lcb_runner.utils.scenarios import Scenario
from lcb_runner.runner.parser import get_args

# Conceptual example of initializing a runner
args = get_args()
args.model = "openai/gpt-5.5"
args.scenario = Scenario.codegeneration

# Execute evaluation
# (Requires local LCB repository setup)

HumanEval — The legacy standard for Python code generation.
MBPP — Mostly Basic Python Problems benchmark.
EvalPlus — Enhancing benchmarks with automated test case generation.
BigCodeBench — Evaluating LLMs on complex, library-heavy coding tasks.
SWE-bench — Software engineering benchmark for resolving GitHub issues.
Chatbot Arena — Human-centric evaluation of LLM capabilities.
Terminal-Bench — Evaluation of LLM-to-shell interaction.
Inspect AI — General evaluation framework supporting coding tasks.

Licensing and cost¶

Open Source: Yes (MIT License).
Cost: Free to use (software and dataset). Requires LLM API credits for model evaluation.

Sources / references¶

Contribution Metadata¶

Last reviewed: 2026-06-23
Confidence: high