Deep Dive into OpenBench: Your All-in-One LLM Evaluation Toolkit

OpenBench is an open-source benchmarking framework designed for researchers and developers who need reliable, reproducible evaluations of large language models (LLMs). Whether you’re testing knowledge recall, reasoning skills, coding ability, or math proficiency, OpenBench offers a consistent CLI-driven experience—no matter which model provider you choose.

1. What Makes OpenBench Stand Out?

  1. Comprehensive Benchmarks

    • 20+ Evaluation Suites: Includes MMLU, GPQA, SuperGPQA, OpenBookQA, HumanEval, AIME, HMMT, and more.
    • Broad Coverage: From general knowledge to competition-grade math, it’s all in one place.

  2. Provider-Agnostic

    • Plug-and-Play: Works with Groq, OpenAI, Anthropic, Cohere, Google, AWS Bedrock, Azure, Ollama (local models), and other Inspect AI–compatible providers.
    • Easy Switching: Just update the --model flag or your BENCH_MODEL environment variable.

  3. User-Friendly CLI

    • bench list: Display all available benchmarks and models.
    • bench describe: Show detailed information on any benchmark.
    • bench eval: Run evaluations and generate logs.
    • bench view: Launch an interactive viewer to inspect past runs.

  4. Highly Extensible

    • Built on the industry-standard Inspect AI framework.
    • Add new benchmarks, custom metrics, or scoring scripts with minimal boilerplate.

2. Key Features at a Glance

Category Benchmarks
Knowledge MMLU (57 subjects), GPQA (graduate level), SuperGPQA (285 disciplines), OpenBookQA
Coding HumanEval (164 problems)
Math AIME 2023–2025, HMMT Feb 2023–2025, BRUMO 2025
Reasoning SimpleQA (factual accuracy), MuSR (multi-step reasoning)

3. Speedrun: Evaluate a Model in 60 Seconds

Prerequisite: Install uv first (installation guide).

# 1. Create a virtual environment & install OpenBench (~30s)
uv venv
source .venv/bin/activate
uv pip install openbench

# 2. Configure your API key (any provider)
export GROQ_API_KEY=your_key    # or OPENAI_API_KEY, ANTHROPIC_API_KEY, etc.

# 3. Run a quick MMLU test (~30s)
bench eval mmlu --model groq/llama-3.3-70b-versatile --limit 10

# 4. View your results
bench view
Sample OpenBench Results

4. Supported Model Providers

  • Groq (ultra-fast):

    bench eval gpqa_diamond --model groq/meta-llama/llama-4-maverick-17b-128e-instruct

  • OpenAI:

    bench eval humaneval --model openai/o3-2025-04-16

  • Anthropic:

    bench eval simpleqa --model anthropic/claude-sonnet-4-20250514

  • Google:

    bench eval mmlu --model google/gemini-2.5-pro

  • Ollama (local):

    bench eval musr --model ollama/llama3.1:70b

Tip: Any provider supported by Inspect AI works seamlessly with OpenBench.

5. Configuration Options

Customize your benchmarking runs via environment variables or CLI flags:

Option Environment Variable Default Description
--model BENCH_MODEL groq/meta-llama/llama-4-scout-17b-16e-instruct Model ID to evaluate
--epochs BENCH_EPOCHS 1 Number of passes per evaluation
--max-connections BENCH_MAX_CONNECTIONS 10 Max parallel API requests
--temperature BENCH_TEMPERATURE 0.6 Sampling temperature
--top-p BENCH_TOP_P 1.0 Nucleus sampling threshold
--max-tokens BENCH_MAX_TOKENS None Max tokens in responses
--seed BENCH_SEED None Random seed for reproducibility
--limit BENCH_LIMIT None Limit to a subset of examples
--logfile BENCH_OUTPUT None Custom path for log output
--sandbox BENCH_SANDBOX None Execution environment (local/docker)
--timeout BENCH_TIMEOUT 10000 API request timeout (seconds)
--display BENCH_DISPLAY None Result display mode (full/conversation/rich/plain/none)
--reasoning-effort BENCH_REASONING_EFFORT None Reasoning effort level (low/medium/high)
--json — False Output results in JSON format

6. Quick Command Reference

Command Description
bench Show main menu and available commands
bench list List all benchmarks, models, and flags
bench describe <benchmark> Show detailed info for a specific benchmark
bench eval <benchmark> Run an evaluation on the specified model
bench view Interactive viewer for past run results

7. Build Your Own Benchmarks

  1. Clone the repo

    git clone https://github.com/groq/openbench.git
cd openbench

  2. Install dev dependencies

    uv venv && uv sync --dev
source .venv/bin/activate

  3. Add a new benchmark

    • Create a folder under benchmarks/, include __init__.py, data download scripts, and scoring logic.
    • Follow the interface patterns in existing modules like MMLU or HumanEval.

  4. Run tests

    pytest

  5. Submit a PR

    • Fork, branch, and push your changes.
    • Open a pull request at github.com/groq/openbench with clear descriptions and test results.

8. Frequently Asked Questions

Q1: How does OpenBench differ from Inspect AI?

A: Inspect AI provides the base framework. OpenBench builds on it with 20+ benchmark implementations, shared tools, and a streamlined CLI—so you don’t reinvent the wheel.

Q2: How can I support additional providers?

A: Simply specify --model provider/model in your command or set BENCH_MODEL. OpenBench will route through Inspect AI to the chosen provider.

Q3: My evaluation scores differ from published numbers—why?

A: Differences in prompt design, model quantization, or dataset versions can lead to slight score variances. For consistent comparison, use the same OpenBench version and identical settings.

Q4: What’s the best way to run in Docker?

A: Add --sandbox docker, or build an image first:

docker build -t openbench .
docker run openbench bench eval mmlu --model groq/...

9. Contributing and Community

We welcome contributions! To get involved:

  1. Fork and clone the OpenBench repository.
  2. Create a feature branch, implement your changes, and add tests.
  3. Submit a pull request with clear descriptions and benchmarks.
  4. Engage with the community on GitHub discussions and issues.

10. Acknowledgments

  • Inspect AI: The foundational benchmarking framework
  • EleutherAI’s lm-evaluation-harness: Pioneers of standardized LLM evaluation
  • Hugging Face’s lighteval: Robust dataset and evaluation utilities