Tech riderRev. 20 Sept 2026

LiveBench

Ch 096.9of 29 LLM Evaluation Tools

1Runs onLinux, Mac, Web, Windows
2CostsNot stated by the maker
3Deployment optionsboth
4LLM-as-a-judgeNo

3 lines stated Written from the maker's own pages: livebench.ai

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Overview

LiveBench is ranked #9 of 29 in LLM evaluation tools on Specifiction. It runs on Linux, macOS, Web, Windows.

Compared on LLM evaluation tools

Deployment options: bothlivebench.ai
LLM-as-a-judge: Nolivebench.ai

Facts

Product: LiveBench is described as a challenging, contamination-free LLM benchmark.livebench.ai · 4 Oct 2026
Benchmark scope: The current site describes 23 objective tasks across 7 categories, refreshed every six months.livebench.ai · 4 Oct 2026
Categories: The site lists Reasoning, Coding, Agentic Coding, Mathematics, Data Analysis, Language, and Instruction Following.livebench.ai · 4 Oct 2026
Leaderboard: The leaderboard shows overall and category scores, with model rows expandable to subtask scores.livebench.ai · 4 Oct 2026
Insights: Insights include quality-versus-cost, ranked cost, and category profile views.livebench.ai · 4 Oct 2026
Cost metric: The site defines cost per successful task as (Σ cost ÷ Σ questions ÷ score) × 100 over the selected scope.livebench.ai · 4 Oct 2026
Contamination controls: The project README says it limits potential contamination with newly released questions and questions based on recent datasets, papers, news, and movie synopses.github.com · 4 Oct 2026
Scoring: The README says questions have verifiable objective ground-truth answers and can be scored automatically without an LLM judge.github.com · 4 Oct 2026
Open source: The project publishes its code in a public GitHub repository and links to benchmark data on Hugging Face.github.com · 4 Oct 2026
Run evaluations: The README documents a Python command-line pipeline for generating model answers, scoring them, and displaying results.github.com · 4 Oct 2026
Model compatibility: The README says OpenAI-compatible API endpoints can be used and lists implemented inference support for Anthropic, Cohere, Mistral, Together, and Google models.github.com · 4 Oct 2026
Local models: The README says local model inference is unmaintained and recommends serving models through an OpenAI-compatible API using vLLM.github.com · 4 Oct 2026
Agentic coding requirements: The README says evaluating agentic coding tasks requires Docker and that storing the task-specific images may take up to 150 GB.github.com · 4 Oct 2026
Support: The README directs users to open a GitHub issue or email [email protected] for model evaluation support.github.com · 4 Oct 2026