OlympicArena
Olympiad-level multi-discipline benchmark for stress-testing reasoning in LLMs and multimodal models.
Pick OlympicArena if you are benchmarking frontier LLMs or multimodal models on hard STEM reasoning and want process-level scoring with contamination checks.
Skip it if you need a turnkey eval SaaS, a writing/coding-only benchmark, or a hosted API to run evaluations for you.
OlympicArena is an open evaluation benchmark built by GAIR (Shanghai Jiao Tong University, Shanghai AI Lab) that throws 11,163 problems drawn from 62 Olympic-tier academic competitions at language and multimodal models. The coverage spans mathematics, physics, chemistry, biology, geography, astronomy, and computer science, with problems available in both English and Chinese and with 13 distinct answer types to keep models from gaming a single output format.
What separates it from generic MMLU-style suites is the focus on genuinely hard, multi-step reasoning at Olympiad difficulty plus a fine-grained process-level evaluation: it scores not just the final answer but the reasoning path, covering 8 logical-reasoning types and 5 visual-reasoning types. It also runs instance-level leakage detection so you can see how much of a model's score is contamination rather than capability. It's aimed at researchers and serious model-builders comparing frontier LLMs and VLMs, not casual users.
The dataset lives on Hugging Face, code is on GitHub, and there's a public leaderboard you can submit to. Everything is free for research use; there's no hosted API or commercial tier, so you bring your own inference stack to run evaluations.
One of the more honest hard-reasoning benchmarks: Olympiad problems, bilingual coverage, and explicit leakage detection. The process-level evaluation is the real differentiator versus MMLU-style multiple choice. Use it alongside, not instead of, task-specific evals.
— The AI Tool Bible editorial team
Pros
- ✅ Olympiad-level difficulty pushes past saturated benchmarks like MMLU
- ✅ Covers seven STEM disciplines plus multimodal and bilingual EN/ZH problems
- ✅ Process-level scoring evaluates reasoning steps, not just final answers
- ✅ Built-in leakage detection helps separate capability from contamination
- ✅ Fully open: dataset on Hugging Face, code on GitHub, public leaderboard
Cons
- ⚠️ Research benchmark, not a hosted product or SaaS
- ⚠️ No managed API or runner; you supply the inference infrastructure
- ⚠️ Heavy STEM focus means limited signal for writing or creative tasks
Use cases
Explore related
Compare with similar tools
All in Evaluation →Braintrust
FeaturedEval, monitor, and improve AI products end-to-end.
LangSmith
LangChain's eval + observability platform.
Weights & Biases
The ML experiment tracker, now with LLM eval features.
Helicone
Open-source LLM observability — one-line proxy install.
Humanloop
Prompt management + evals for collaborative AI teams.
PromptLayer
Lightweight prompt logging + management for OpenAI/Claude apps.