BrowseComp

BrowseComp (Browsing Competence) is an OpenAI benchmark for deep web research. Each task is a question whose answer is deliberately hard to find — the model has to navigate many web pages, follow leads and cross-check sources. A correct final answer is the only thing that counts.

BrowseComp measures how well a model acts as a general-purpose agent. In detail, it checks:

• searching the web step by step instead of guessing
• reading and understanding many pages per task
• combining information from several sources
• persistence on questions that are nearly impossible without browsing

The score is accuracy: the percentage of tasks answered or solved correctly. 100% means every task was solved perfectly; higher is better.

What is the BrowseComp benchmark?

BrowseComp (Browsing Competence) is an OpenAI benchmark for deep web research. Each task is a question whose answer is deliberately hard to find — the model has to navigate many web pages, follow leads and cross-check sources. A correct final answer is the only thing that counts.

What does BrowseComp test?

BrowseComp mainly measures how well a model acts as a general-purpose agent. Concretely, it checks: searching the web step by step instead of guessing; reading and understanding many pages per task; combining information from several sources.

How is the BrowseComp score calculated?

The score is accuracy: the percentage of tasks answered or solved correctly. 100% means every task was solved perfectly; higher is better. The declared maximum score of this benchmark is 100.0.

Where do I get BrowseComp data and scores?

The official benchmark page/reference is linked on this page. Every score on Open LLM Radar is also available as open data: download the scores JSON (http://open-llm-radar-api:8000/api/v1/benchmarks/browsecomp/scores) or pull it directly from the Open LLM Radar API with any HTTP client.