BrowseComp tests whether an AI model can find answers on the web, not just recall them from training. A model must plan a search, inspect sources, filter noise, and synthesize a correct answer. It is one of the most important benchmarks for evaluating research agents and web-integrated AI workflows.
BrowseComp is a benchmark for a very specific skill: finding the answer on the web when the answer is not already obvious from the model's internal knowledge.
That makes it one of the best public tests for research-oriented agents.
What BrowseComp tests
The model has to:
- decide what to search for
- open and inspect sources
- gather relevant evidence
- avoid shallow or misleading pages
- synthesize a correct answer
This is a different problem than scoring well on MMLU or GPQA. Those knowledge benchmarks mostly test what the model already knows. BrowseComp tests whether it can go get what it needs.
Why it matters
Many practical AI workflows now involve web research:
- market scans
- competitor analysis
- technical documentation lookup
- citation gathering
- open-ended question answering
If a model is weak at browsing, it may still sound confident while missing key evidence. BrowseComp helps separate fluent models from models that can actually do useful research.
What a high score usually means
A strong BrowseComp score suggests the model is better at:
- planning a search strategy
- filtering noisy sources
- staying grounded in evidence
- answering with more factual discipline
It does not automatically make the model the best option for coding or math. It makes it a stronger candidate for research-heavy products and assistants.
Best companion benchmarks
BrowseComp is especially useful when paired with:
- SimpleQA for short-form factual accuracy
- HLE for frontier-difficulty knowledge
- OSWorld-Verified for full workflow execution
Together, those benchmarks tell you whether a model both knows things and can go find things.
→ See agentic model rankings · Full leaderboard
When BrowseComp should decide your pick
The best model for research is not always the model with the highest static knowledge score. If your workflow depends on evidence gathering and open-web synthesis, put BrowseComp ahead of the static knowledge rows when you shortlist, and watch how the ranking reshuffles.
See the live leaderboard: BrowseComp scores
Reader questions
Frequently asked questions
01What is BrowseComp?
BrowseComp is a benchmark that tests whether AI models can find answers on the web rather than relying on their internal training knowledge. The model must plan a search strategy, open and inspect sources, filter noise, gather evidence, and synthesize a correct answer. It is one of the best public benchmarks for evaluating research-oriented AI agents.
02What does a high BrowseComp score mean?
A strong BrowseComp score indicates the model is better at planning search strategies, filtering noisy sources, staying grounded in evidence, and answering with factual discipline. It does not automatically make a model the best choice for coding or math. It is a strong signal for research-heavy products and assistants that need to gather evidence from the web.
03How is BrowseComp different from static knowledge benchmarks like MMLU?
MMLU and GPQA test what a model already knows from training. BrowseComp tests whether a model can go find what it needs on the web — search, inspect sources, filter noise, and synthesize an answer. Many practical AI workflows now involve web research, and a model weak at browsing may miss key evidence even while sounding confident.
04What benchmarks should I use alongside BrowseComp?
Pair BrowseComp with SimpleQA for short-form factual accuracy, HLE for frontier-difficulty knowledge, and OSWorld-Verified for full software interface execution. Together they tell you whether a model both knows things and can go find things while also completing multi-step tasks.
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