AI Frameworks · open-compass

opencompass

OpenCompass is an open-source LLM evaluation platform supporting 100+ datasets and a wide range of models (Llama, Mistral, GPT-4, Claude, Qwen, etc.). It provides standardized benchmarking tools and leaderboards for assessing language model quality and effectiveness.

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Source: GitHub — github.com/open-compass/opencompass

7.2k

GitHub stars

799

Forks

Python

Primary language

Apache-2.0

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	open-compass/opencompass
Owner	open-compass
Primary language	Python
License	Apache-2.0 — OSI-approved
Stars	7.2k
Forks	799
Open issues	436
Latest release	0.5.3 (2026-06-29)
Last updated	2026-07-07
Source	https://github.com/open-compass/opencompass

What opencompass is

Python-based evaluation framework offering configurable test harnesses for multiple LLM backends (HuggingFace, vLLM, LMDeploy), dataset management via local/ModelScope sources, and composable evaluation pipelines including LLM-as-judge and math-specific evaluators. Recent additions include CascadeEvaluator for sequential evaluation chains and post-processing via XFinder.

Quickstart

Get the opencompass source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/open-compass/opencompass.git2cd opencompass3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Internal model benchmarking and comparison

Standardized evaluation across proprietary and open-source models using 100+ curated datasets. Useful for pre-training, fine-tuning, or deployment decisions within your org.

LLM leaderboard and public rankings

Use CompassHub/CompassRank infrastructure or run privately. Supports public-facing model rankings with reproducible, transparent evaluation methodology.

Custom evaluation pipelines for specialized tasks

Configure CascadeEvaluator chains, integrate custom datasets, and combine LLM judges with domain-specific verifiers (e.g., MATHVerifyEvaluator for reasoning tasks).

Implementation considerations

Python 3.8+ environment; install via pip or source. Requires GPU(s) for practical LLM inference; CPU-only is very slow for benchmarks.
Decide on backend: HuggingFace (default), vLLM (faster inference), or LMDeploy. Configure via command-line flags or deployment APIs.
Define or import datasets; OpenCompass provides 100+ built-in configs. For custom datasets, extend Dataset class and register in YAML.
Plan evaluation scope: dataset selection, model list, metric preferences. Large evaluations (many models × many datasets) take hours/days.
For LLM-as-judge flows, ensure target judge model (e.g., GPT-4) is accessible and budgeted; costs scale with evaluation size.

When to avoid it — and what to weigh

Real-time or low-latency inference systems — OpenCompass is a batch evaluation platform, not an inference server. For serving models in production, use vLLM, TensorRT, or similar independently.
Minimal infrastructure or single-machine deployment — Evaluating 100+ datasets across multiple models requires significant compute. Distributed setups across GPUs/TPUs are expected; not suitable for lightweight deployments.
Closed/proprietary model evaluation without API keys — Evaluation of OpenAI, Claude, or other closed models requires valid API credentials. If you cannot provide these, you cannot evaluate those models.
Turnkey evaluation without configuration — OpenCompass requires familiarity with YAML configs, model definitions, and dataset specifications. It is not a no-code platform; setup and tuning are essential.

License & commercial use

Apache License 2.0 (Apache-2.0). Permissive OSI-approved license: allows commercial use, distribution, modification, and private use with attribution and license notice retention.

Apache-2.0 is permissive for commercial use. You may run OpenCompass to evaluate models for commercial products, publish rankings, and modify code. Ensure LICENSE file is retained in distributions. No warranty or liability limitations from licensor. Review terms for closed-model evaluation (e.g., OpenAI API ToS are separate).

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Strong
License clarity	Clear
Deployment complexity	Moderate
DEV.co fit	Good
Assessment confidence	High

Security considerations

Runs model inference locally or via third-party APIs (OpenAI, Claude, etc.). Ensure API keys are stored securely (environment variables, not in configs). Input data (datasets) should be reviewed for PII/sensitivity. No independent security audit mentioned; supply-chain risk depends on Python dependencies (100+ listed). If evaluating private/confidential models, sandbox appropriately.

Alternatives to consider

LMSys Chatbot Arena / LMSYS Leaderboard

Crowdsourced human preference evaluation; lighter-weight but focuses on chat, not specialized benchmarks. No self-hosted option; relies on LMSys infrastructure.

EleutherAI Evaluation Harness

Lightweight Python toolkit for standard benchmarks (MMLU, HellaSwag, etc.). Simpler setup but fewer datasets and no built-in LLM-judge or advanced evaluators.

Hugging Face's Lighteval / Model Evaluation

Integrated into Hugging Face Hub; supports model leaderboards and standard evals. Easier integration if already in HF ecosystem but less flexible for custom pipelines.

Software development agency

Build on opencompass with DEV.co software developers

Start with OpenCompass for standardized, reproducible benchmarking. Deploy locally or leverage CompassHub for leaderboards. Let's discuss your evaluation strategy and infrastructure needs.

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opencompass FAQ

Can I run OpenCompass without a GPU?

Technically yes, but CPU inference is impractically slow for LLMs. GPU(s) are strongly recommended; even modest models (7B params) need minutes per sample on CPU.

How do I evaluate a custom/private model?

Register your model in configs/models/ with a custom class or HuggingFace path. If it's a local checkpoint, point to the path; if it's an API (e.g., internal deployment), create a custom Backend.

What is the cost of evaluating with OpenCompass?

Self-hosted models (Llama, Qwen, etc.) cost only compute (GPU time). Closed-model evaluation via API (GPT-4, Claude as judge) incurs per-token charges; budget scales with dataset size and model complexity.

Can I publish results from OpenCompass on a leaderboard?

Yes, you can run privately and publish rankings. CompassHub/CompassRank are public platforms; you can also self-host results. Ensure reproducibility by documenting config versions and hardware.

Software development & web development with DEV.co

DEV.co is a software development agency delivering custom software development services to companies building on open source. Our software developers and web developers design, integrate, and ship production systems — spanning web development, APIs, AI, data, and cloud. If opencompass is part of your ai frameworks roadmap, our team can implement, customize, migrate, and maintain it.

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