Free
Free
Open-source evaluation codebase and datasets are published via GitHub and Hugging Face without a listed SaaS fee.
- Access
- Open-source evaluation codebase and datasets are published via GitHub and Hugging Face without a listed SaaS fee.
Research · Vendor site 2026-10-04
LiveBench is a contamination-aware LLM benchmark that releases fresh questions monthly with verifiable ground-truth answers. It spans 18 tasks across six categories and provides open-source tooling to generate answers, score models, and publish leaderboard results.
ML researchers and model vendors who need objective, automatically scored benchmarks that refresh to reduce training-data contamination.
Agentic coding tasks require Docker.
LiveBench combines frequently updated tasks, objective graders, and an public leaderboard for comparing frontier models across diverse categories.
What it can do
Unknown is different from unavailable. Each fact carries its own evidence.
| Capability | Value | Evidence | Checked |
|---|---|---|---|
| Monthly refresh | Project README states new questions release monthly to limit benchmark contamination. | Facts sourced | 2026-10-04 |
| Objective scoring | README emphasizes verifiable ground-truth answers scored automatically without an LLM judge. | Facts sourced | 2026-10-04 |
Understand the total cost
Free
Free
Open-source evaluation codebase and datasets are published via GitHub and Hugging Face without a listed SaaS fee.
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Explore toolThe practical questions
LiveBench is a contamination-aware LLM benchmark that releases fresh questions monthly with verifiable ground-truth answers. It spans 18 tasks across six categories and provides open-source tooling to generate answers, score models, and publish leaderboard results.
Open-source evaluation codebase and datasets are published via GitHub and Hugging Face without a listed SaaS fee.. This record lists ongoing free access; check the plan limits before starting.
No paid monthly price is listed; this record treats the product as free to start. See the plan cards for entitlements, billing commitments and seat minimums.
Evaluation uses Python CLI scripts and optional OpenAI-compatible APIs for model inference; local model paths are unmaintained per README.. API access and subscription access may have different terms; consult the linked sources.
Agentic coding tasks require Docker.
No retained pricing changes yet. A current price alone does not establish a historical trend.
Reviewed vendor source
Read original source ↗Facts apply to the named version and check date. Send a sourced correction if something changed.