Opencompass
Open-source LLM & VLM evaluation platform for standardized benchmarking
OpenCompass is the most comprehensive open-source evaluation suite for LLM and VLM benchmarking. Supporting 50+ models and 100+ datasets, it's the go-to for researchers and developers who need standardized, reproducible comparisons. The modular design and community leaderboard add real value. It's a must-try for anyone serious about model evaluation, but you'll need technical chops to set it up. For simpler needs, consider Hugging Face's Evaluate library.
Verified 1d ago · liveness 36/100 · cite: rightaichoice.com/tools/opencompass
- LLM researchers conducting standardized model benchmarking
- AI developers evaluating model performance across diverse tasks
- Model vendors seeking objective, third-party evaluation
- Academic labs performing reproducible experiments
- Users seeking a production monitoring or real-time inference solution
- Non-technical users expecting a no-code interface
- Those needing private evaluation without any data sharing
We scan live Reddit threads, YouTube comments, X posts, G2 reviews and other communities — and hand you an honest verdict in under a minute.
- Honest verdict, not marketing
- Real pros & cons from real users
- Attributed quotes with receipts
3 free scans · no card needed
Skip OpenCompass if you need a managed, no-code evaluation tool with customer support, real-time monitoring, or a cloud-hosted service — this is a DIY, open-source platform built for technical users who can handle Python and compute infrastructure.
Running large models locally can rack up significant cloud or GPU compute costs, which you bear yourself since OpenCompass is self-hosted.
OpenCompass is free and open-source, so the only cost is your own compute and engineering time. It's far cheaper than commercial evaluation platforms like LangSmith or Weights & Biases, which charge per-seat or usage fees, making it ideal for academics and budget-conscious teams. But if you value managed infrastructure and support, those paid tools may justify their cost.
In short
Opencompass — Open-source LLM & VLM evaluation platform for standardized benchmarking. Best for LLM researchers conducting standardized model benchmarking, AI developers evaluating model performance across diverse tasks, Model vendors seeking objective, third-party evaluation. Free to use.
Viability Score
How well maintained and how widely used is Opencompass? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: August 2026
How we score →Key Features
- 100+ evaluation datasets
- 50+ LLMs and VLMs support
- Online and offline evaluation modes
- Standardized metrics (accuracy, BLEU, ROUGE, etc.)
- Automated report generation with visualizations
- Multi-task and multi-model comparison
- Safety and bias evaluation
- Instruction following evaluation
- Agent and tool-use evaluation
- Multimodal perception and spatial awareness evaluation
- AI4S (AI for Science) evaluation
- Community leaderboard
- Plugin system for custom datasets and models
- Modular pipeline design
- Open-source (MIT license)
About Opencompass
OpenCompass (司南) is an open-source platform for standardized evaluation of large language models (LLMs) and vision-language models (VLMs). It supports 50+ models, including GPT-5, DeepSeek-V4, Claude Opus 4.7, Qwen3, and Kimi-K2.6, and 100+ datasets across language, reasoning, math, coding, instruction following, agents, multimodal perception, spatial awareness, and AI for Science. You can run evaluations online or offline, compare models on standardized metrics (accuracy, BLEU, ROUGE, etc.), and generate automated reports with visualizations. The modular plugin system lets you add custom models and datasets. A community leaderboard tracks model rankings. OpenCompass is free, open-source (MIT), and actively maintained on GitHub. It's built for AI researchers, developers, model vendors, and academic labs who need objective, reproducible model comparisons. The platform requires technical expertise in Python and command-line tools, and can be resource-intensive for large models.
Behind the Verdict
OpenCompass stands out as a deep, flexible evaluation platform. Its breadth of models and datasets is unmatched, and the ability to run evaluations online or offline gives you control over your environment. The plugin system is a highlight — you can hook in custom datasets and models, which is crucial for teams with proprietary stacks. The standardized metrics and automated reports make results shareable within your org or with external stakeholders. However, the learning curve is real. You'll need Python and command-line comfort, and running large models locally can tax your compute budget. There's no managed cloud, so you're on your own for infrastructure. For teams that want a plug-and-play solution with support, commercial tools like Weights & Biases or LangSmith might be easier, but they come with costs and less flexibility. OpenCompass is ideal for academic labs and evaluation-focused teams who prioritize reproducibility and are willing to invest in setup.
Researching Opencompass? Get your full AI stack in 60 seconds.
Free, no signup — tell us your goal and get tools matched to your budget & existing stack.
Real-world workflow fit
Concrete scenarios for the personas Opencompass actually fits — and what changes day-one when you adopt it.
You need to benchmark a fine-tuned model against a set of reasoning datasets to compare with GPT-5.
Outcome: You install OpenCompass, configure the model and datasets, run the evaluation, and get a standardized accuracy report you can include in your paper.
You want to ensure your internal chatbot doesn't regress on safety metrics after each update.
Outcome: You set up a CI/CD pipeline that runs OpenCompass safety evaluations on each commit, automatically flagging any performance drops.
You're teaching a course on reproducible LLM evaluation and need a hands-on tool for students.
Outcome: You provide students with OpenCompass tutorials, and they run standardized evaluations on openly available models, learning best practices.
Use Cases
- Benchmark your fine-tuned model against GPT-5 on 20 reasoning datasets
- Compare safety scores across open-source LLMs using built-in adversarial prompts
- Automate weekly evaluation of your internal chatbot model with custom metrics
- Generate a comprehensive report for enterprise model readiness review
- Integrate OpenCompass into your CI/CD pipeline to catch performance regressions
- Train students on reproducible LLM evaluation methodology using standardized tasks
- Evaluate a new VLM's spatial reasoning capabilities across 10 multimodal datasets
Models Under the Hood
as of 2026-08-27
Limitations
- As an open-source platform, OpenCompass lacks dedicated customer support and SLAs.
- The evaluation process can be resource-intensive, requiring significant compute for large models.
- Integration with proprietary models may require manual configuration.
- The platform focuses on offline evaluation and does not provide real-time monitoring.
- Setup requires technical expertise in Python and command-line tools.
as of 2026-09-01
Verification history
We have re-verified Opencompass 8 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
Showing the 6 most recent of 8 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Opencompass tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Open Source
$0/mo
Ideal for
Researchers, developers, and students who need a free, self-hosted evaluation platform and can handle the technical setup.
What this tier adds
This is the only tier and it's free, providing full access to all datasets, models, and features with no recurring cost.
Where the pricing makes sense
The company stage and team size where Opencompass's pricing actually pencils out — and where peers do it cheaper.
OpenCompass is free and open-source, so the only cost is your own compute and engineering time. It's far cheaper than commercial evaluation platforms like LangSmith or Weights & Biases, which charge per-seat or usage fees, making it ideal for academics and budget-conscious teams. But if you value managed infrastructure and support, those paid tools may justify their cost.
Setup time & first value
How long it actually takes to get something useful out of Opencompass — broken out by persona, not the marketing-page minute.
For a technical user familiar with Python, getting OpenCompass running and executing a basic benchmark can take a few hours. Deeper customization, like adding custom datasets or models, may take a day or two. Non-technical users should expect a longer ramp-up to learn the CLI and configuration.
Switching to or from Opencompass
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From Hugging Face Evaluate: You can replace simpler evaluation scripts with OpenCompass for more comprehensive benchmarks, though you'll need to adapt to its CLI and config structure.
- ↗To LangSmith: If you need managed tracing and evaluation, you can export your results and integrate with LangSmith, though you'll lose OpenCompass's offline, open-source flexibility.
Integrations
Resources & Guides
Tutorials & Learning
Official links
Tools that pair well with Opencompass
Common stack mates teams adopt alongside Opencompass, with the specific reason each pairing earns its keep.
Featured Head-to-Head Comparisons
Opencompass vs Screenplayiq
If your goal is to get data-backed feedback on your screenplay's marketability and ROI potential, ScreenplayIQ (starting free, Pro $19/mo) is purpose-built for you. If you need to benchmark or evaluate LLMs objectively across dozens of datasets, OpenCompass is the free, open-source choice. They serve entirely different domains—pick based on whether you write scripts or build models.
Opencompass vs Praktika
These tools serve entirely different purposes. Choose Praktika if you're a language learner seeking real-time AI conversation practice with structured feedback. Choose OpenCompass if you're an AI developer or researcher needing a comprehensive, open-source LLM evaluation platform. There's no overlap.
Alternatives to Opencompass
View allWeights & Biases
ML experiment tracking and LLM development platform for teams
Frequently Asked Questions
Used Opencompass? Help shape our editorial sentiment research.