Opencompass
OpenCompass (司南) benchmarks LLMs, VLMs, and AI4S models on 100+ open evaluation datasets with dated leaderboards
For an independent read on how frontier LLMs, VLMs, and AI4S models stack up, OpenCompass publishes the dimensions it scores and stamps every board with a refresh date — 2026-08-24 for LLMs, 2026-06-11 for VLMs, 2026-02-09 for AI4S. The Arena gives you crowdsourced head-to-head voting, and the toolkit is MIT-licensed. Just budget for Python, GPUs, and hand-configuring proprietary models.
Verified 4d ago · liveness 60/100 · cite: rightaichoice.com/tools/opencompass
- Research teams needing standardized, reproducible cross-model benchmarks
- Application developers choosing a base model with third-party evidence
- Model vendors wanting published, dimension-by-dimension comparisons
- Academic labs running offline experiments on custom datasets and tasks
- Teams wanting production monitoring or real-time inference observability
- Non-technical users who need a no-code, point-and-click interface
- Buyers who require an SLA, paid support, or a vendor contract
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Skip OpenCompass if you want a point-and-click model picker or production monitoring rather than a Python evaluation framework you run yourself — the leaderboards are readable by anyone, but the toolkit underneath assumes command-line work.
Running a full multi-model sweep across the task set consumes serious GPU hours, and that compute bill is yours — the MIT license covers the software, not the hardware.
OpenCompass is MIT-licensed and free to use, which puts it at the bottom of the cost curve against commercial evaluation services and hosted benchmarking platforms that charge per evaluation run or per seat. The trade is inverted: your spend shifts from license fees to compute and engineering time, so a lab with spare GPUs pays close to nothing while a team that would rather buy hours than spend them should look at a managed eval service instead.
In short
Opencompass — OpenCompass (司南) benchmarks LLMs, VLMs, and AI4S models on 100+ open evaluation datasets with dated leaderboards. Best for Research teams needing standardized, reproducible cross-model benchmarks, Application developers choosing a base model with third-party evidence, Model vendors wanting published, dimension-by-dimension comparisons. Free to use.
What's new in Opencompass
Checked 4 days agoAcross the latest 4 updates: 1 launch and 3 changelog entries.
LLM leaderboard refreshed: Claude Opus 5 (high) tops the board at 83.3
The LLM ranking was refreshed on an official closed-source evaluation set, scoring language, reasoning, knowledge, math, code, instruction following, and agent dimensions. Claude Opus 5 (high) leads at 83.3, ahead of GPT-5.6-Sol (high) at 80.8.
VLM leaderboard refreshed: Qwen3.7-Plus leads at 72.96
The vision-language board covers general perception, spatial perception, infographic perception, multimodal reasoning, multimodal creation, and agents. Qwen3.7-Plus takes first at 72.96, ahead of Gemini-3.1-Pro-Preview at 72.58.
AI4S leaderboard refreshed: Gemini-3-Pro leads at 48.74
The scientific-AI board scores knowledge understanding, code generation, symbolic reasoning, hypothesis generation, and multimodal perception, understanding, and reasoning. Gemini-3-Pro leads at 48.74, ahead of Claude-4.5-Opus at 45.78.
司南竞技场焕新升级 — Arena refresh under the v2026.06 release
The site now carries a v2026.06 version stamp and promotes a revamped OpenCompass Arena, the human-vote layer where anyone can compare model outputs head to head alongside the fixed-rubric leaderboards.
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: October 2026
How we score →Key Features
- Benchmark LLMs on language, reasoning, knowledge, math, code, instruction following, and agents
- Evaluate VLMs on general, spatial, and infographic perception plus multimodal reasoning and creation
- Score AI4S models on knowledge understanding, code generation, symbolic reasoning, and hypothesis generation
- Track dated leaderboards: LLM 2026-08-24, VLM 2026-06-11, AI4S 2026-02-09
- Compare models head to head with human votes in OpenCompass Arena (司南竞技场)
- Run evaluations online or fully offline on private infrastructure
- Standardized metrics including accuracy, BLEU, and ROUGE
- Generate automated visual evaluation reports
- Compare multiple models and tasks in a single run
- Register custom models and datasets through a plugin system
- Extend tasks with a modular evaluation pipeline
- Safety and bias evaluation tasks
- Agent and tool-use evaluation
- Open-source under the MIT license on GitHub (open-compass/opencompass)
About Opencompass
OpenCompass — 司南 in Chinese — is an open evaluation platform and MIT-licensed benchmarking toolkit for large language models, vision-language models, and scientific AI. Its published leaderboards rank frontier systems on a fixed rubric with visible refresh dates, so you can tell which model generation a score belongs to. The LLM board (v2026.06, refreshed 2026-08-24) covers language, reasoning, knowledge, math, code, instruction following, and agent ability, with Claude Opus 5 (high) at 83.3, GPT-5.6-Sol (high) at 80.8, and Qwen3.8-Max at 80.6. A separate VLM board (2026-06-11) spans general, spatial, and infographic perception, multimodal reasoning, creation, and agents, led by Qwen3.7-Plus at 72.96. The AI4S board (2026-02-09) scores knowledge understanding, code generation, symbolic reasoning, hypothesis generation, and multimodal perception and reasoning, with Gemini-3-Pro at 48.74. The platform is built in three layers. Leaderboards publish periodically refreshed rankings. OpenCompass Arena (司南竞技场) adds a human-vote layer where anyone can pit model outputs head to head — the Arena was revamped in the v2026.06 release. Underneath sits the evaluation toolkit: a modular pipeline for online or fully offline runs, standardized metrics such as accuracy, BLEU, and ROUGE, automated visual reports, and a plugin system for registering custom models and datasets. The site maps four paths onto those layers: AI enthusiasts tracking frontier releases, application developers picking a base model, model vendors wanting third-party validation, and academic institutions running reproducible experiments. Running your own evaluations means Python and command-line work, not a point-and-click dashboard. Against lighter options — Hugging Face's Evaluate library, for instance — OpenCompass is what you reach for when you need broad, standardized, leaderboard-grade comparisons across modalities rather than a single metric on a single task.
Behind the Verdict
We reach for OpenCompass when the question is "which model is actually ahead right now" and the answer needs to be defensible. The dated boards are the draw: a score is only meaningful next to the rubric version and refresh date that produced it, and OpenCompass shows both. Pick it in three situations. You're a developer choosing a base model and want third-party evidence rather than a vendor's own chart. You're a lab that needs to run the same benchmark on your own hardware, or on datasets you can't send to a hosted service — the pipeline supports fully offline evaluation, which matters when your data can't leave the building. Or you're a vendor who wants an external ranking to point at. Pass when you need production observability. OpenCompass measures models, not deployments — no latency dashboards, no drift alerts, no inference monitoring. If that's the ask, a managed eval or LLMOps product fits better. The Arena is the part people underrate. Fixed-rubric scores reward what the rubric measures; human head-to-head votes surface preferences the rubric doesn't capture. Used together they're more useful than either alone, and the v2026.06 refresh made the Arena a first-class entry point rather than an afterthought. Where it bites: this is research infrastructure. Reproducing a board means Python, a GPU budget, and manual configuration for proprietary models. Nothing here is point-and-click, and nobody is on the hook for your run. Budget engineering time, not a subscription. Closest alternative for quick work is Hugging Face's Evaluate library — lighter, narrower, fine for one metric on one task. OpenCompass is the heavier instrument you bring out when you need a lot of models scored the same way.
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Real-world workflow fit
Concrete scenarios for the personas Opencompass actually fits — and what changes day-one when you adopt it.
You pull the current LLM board (refreshed 2026-08-24) and see the top five clustered between 83.3 and 80.5, then use the dimension breakdown to check which model leads on the specific skills your product needs — code, instruction following, agent tool use.
Outcome: You shortlist two candidates by evidence rather than vendor claims, then run both through OpenCompass on your own task subset to confirm the choice on your data.
You register your custom model and dataset through the plugin system, run the standard task suite offline on private infrastructure, and generate a visual report automatically.
Outcome: Your checkpoint is scored on the same tasks as the public board, so the comparison holds up in a paper or a review meeting without you hand-assembling baselines.
You track where your release lands on the LLM and VLM boards and monitor the Arena, where human voters compare outputs head to head rather than scoring against a fixed rubric.
Outcome: You get both a dimension-level public ranking and a preference signal, which lets you see whether a below-top rank reflects a real capability gap or just rubric weighting.
Use Cases
- Benchmark your fine-tuned model against GPT-5.6-Sol on the same reasoning, math, and code tasks used on the public LLM board
- Compare safety scores across open-source LLMs using built-in adversarial prompts
- Automate weekly evaluation of an internal chatbot model with custom metrics
- Generate a dimension-by-dimension report for an enterprise model readiness review
- Wire OpenCompass into a CI/CD pipeline to catch performance regressions before release
- Train students on reproducible LLM evaluation methodology using standardized tasks
- Evaluate a new VLM's spatial and infographic perception across multimodal datasets
- Vote in the Arena to compare two models on outputs where the leaderboard gap is under a point
Models Under the Hood
as of 2026-09-08
Limitations
- As an open-source project, OpenCompass comes with no support desk or SLA — if a run breaks, the fix is yours.
- Evaluation is resource-intensive: sweeping several large models across the full task set needs serious compute.
- Integrating proprietary, closed-source models takes manual configuration.
- The platform is built for offline evaluation and does not provide real-time monitoring or production observability.
- Getting started requires Python and command-line fluency.
- Board freshness varies by track — the LLM board carries a 2026-08-24 date while the AI4S board was last refreshed 2026-02-09, so treat the science ranking as directional rather than current.
as of 2026-10-04
Verification history
We have re-verified Opencompass 11 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-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — 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 11 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 (MIT)
$0
Ideal for
Research labs, ML engineers, and academic groups with their own compute who need reproducible cross-model evaluation and don't want a vendor contract standing in the way.
What this tier adds
Starting tier — the only published tier: full evaluation toolkit, all leaderboards, Arena access, 100+ datasets, plugin system, and online/offline runs at no license 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 MIT-licensed and free to use, which puts it at the bottom of the cost curve against commercial evaluation services and hosted benchmarking platforms that charge per evaluation run or per seat. The trade is inverted: your spend shifts from license fees to compute and engineering time, so a lab with spare GPUs pays close to nothing while a team that would rather buy hours than spend them should look at a managed eval service instead.
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 browsing: zero — the leaderboards and Arena are readable immediately, no account needed. For running the toolkit: plan on a few hours to install from GitHub, sort out model access and a working environment, and land your first evaluation; a full custom task suite with proprietary models is a multi-day job, mostly spent on configuration and compute rather than on the framework itself.
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: for single-metric checks, Evaluate is lighter; move to OpenCompass when you need multi-task, multi-model runs with standardized reporting and leaderboard-comparable tasks.
- →From hand-rolled eval scripts: register your existing datasets as OpenCompass tasks through the plugin system so results become reproducible and reportable instead of notebook-local.
- →From vendor-published benchmark tables: re-run the same models on OpenCompass tasks to get a third-party number you can cite alongside the vendor's own.
- ↗To a managed evaluation service: if you need an SLA, a support contract, or someone else to own the runs.
- ↗To Hugging Face Evaluate: if your need narrows to a single metric or a lightweight one-off scoring job.
- ↗To production observability tooling: OpenCompass is offline evaluation, not real-time monitoring, so live-traffic performance belongs elsewhere.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Opencompass”, and we withheld 6: 6 could not be judged, because “Opencompass” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Opencompass.
Official links
Tools that pair well with Opencompass
Common stack mates teams adopt alongside Opencompass, with the specific reason each pairing earns its keep.
Matharena
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Goodfire
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Weights & Biases
Weights & Biases tracks ML experiments and traces LLM apps so teams ship AI models faster
Featured Head-to-Head Comparisons
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.
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.
Alternatives to Opencompass
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Matharena
Free public leaderboard benchmarking LLMs on competition math and Lean proof benchmarks like ArXivMath and ArXivLean.
Goodfire
Silico is Goodfire's interpretability agent for understanding and debugging the internals of your AI models.
Weights & Biases
Weights & Biases tracks ML experiments and traces LLM apps so teams ship AI models faster
Frequently Asked Questions
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