groq/openbench

What it solves

openbench is a provider-agnostic, open-source infrastructure for benchmarking Large Language Models (LLMs). It solves the problem of fragmented and inconsistent evaluation processes by providing a standardized, reproducible way to test models across a wide variety of tasks without being locked into a single model provider.

How it works

Built on top of the Inspect AI framework, openbench provides a curated library of over 95 benchmarks (including MMLU, GPQA, and HumanEval) and a simple CLI tool (bench) to execute them. It supports over 30 model providers—including Groq, OpenAI, Anthropic, and local options like Ollama—allowing users to run the same evaluation suite against different models via API keys or local setups. It also supports private evaluations by allowing users to point the tool directly at local evaluation files.

Who it’s for

AI developers and researchers who need to evaluate LLM performance across diverse domains (such as math, coding, and reasoning) and compare models from different providers using a consistent methodology.

Highlights

• Broad Provider Support: Works with 30+ model providers out of the box.
• Extensive Benchmark Library: Includes 95+ benchmarks covering knowledge, science, and long-context recall.
• Simple CLI: Streamlined commands for listing, running, and viewing evaluations.
• Extensible Architecture: Supports a plugin system via Python entry points for distributing custom benchmarks as packages.
• Hugging Face Integration: Ability to push evaluation results directly to Hugging Face datasets.