AgentBench vs LiveBench
AgentBench
github.comLiveBench
livebench.aiSide by side
| Figure | AgentBench | LiveBench |
|---|---|---|
| Price from | — | — |
| Free plan | — | — |
| Free trial | — | — |
| Open source | — | — |
| Web | — | Yes |
| Windows | — | Yes |
| macOS | — | Yes |
| Linux | Yes | Yes |
| API | Yes | — |
| Self-hosted | Yes | Yes |
| Plans published | 0 | 0 |
| Spec score · LLM Evaluation Tools | 7.8 (#10) | 7.2 (#22) |
| Purpose | AgentBench is a benchmark for evaluating the reasoning and decision-making abilities of LLMs acting as agents. | LiveBench is an LLM benchmark designed to limit test set contamination and use objective evaluation. |
| Support | The project invites users to join its Slack for questions and collaboration, and lists a Google Group for questions about AgentBench FC. | The repository invites model evaluation requests by GitHub issue or email at [email protected]. |
In their own words
LiveBench
LiveBench is an open-source benchmark for evaluating large language models, designed to limit test-set contamination and use objective scoring. Its current scope is 23 tasks across Reasoning, Coding, Agentic Coding, Mathematics, Data Analysis, Language, and Instruction Following, with a refresh every six months. The leaderboard presents overall and category scores, and model rows expand to show subtasks. Insight views cover quality versus cost, ranked cost, and category profiles; cost per successful task is calculated as (Σ cost ÷ Σ questions ÷ score) × 100 for the selected scope. Questions have verifiable ground-truth answers and can be scored automatically without an LLM judge. The public project provides benchmark code and links to data on Hugging Face, as well as a Python command-line evaluation pipeline. It supports OpenAI-compatible endpoints and implemented inference for Anthropic, Cohere, Mistral, Together, and Google models. Local inference is unmaintained. Agentic coding evaluations require Docker, and task images may take up to 150 GB to store.