BrowseComp Long Context 256k
Models answer BrowseComp questions from a context of 128K to 256K tokens built from the needed web pages and related distractor pages. Scored by accuracy.
The 256k setting of the same dataset as browsecomp-long-context-128k. GPT-5 (high) scored 88.8% in OpenAI's launch table.
- Made by
- OpenAI
- Released
- August 2025
- Kind
- Reasoning over long documents
- Level
- Expert
- Scored
- Checked automatically
- Answers
- Short answer
- Tasks
- All public
No scores here yet
No lab, leaderboard or Epoch AI run has reported a score for BrowseComp Long Context 256k yet.
No lab model card reports it yet
We read the headline results of 522 launch posts from 52 labs, and none of them list this benchmark.
More in Long context
- MRCR v2 In use Multi-round coreference: retrieve the n-th of several similar requests hidden in a long synthetic conversation, up to 1M tokens.
Muse Spark 1.3 98.5% - LongBench v2 In use 503 multiple-choice questions over 8K-2M-word contexts (QA, dialogue, code repos, structured data). Accuracy.
Inkling 2 Small 70.6% - GraphWalks In use A graph is given as an edge list in long context. The model answers BFS or parent queries, scored by F1. Claude Fable 5 99.96%
- OpenAI MRCR v2 In use Models read a long synthetic chat that hides 2, 4 or 8 identical writing requests and must reproduce the response to the i-th one. Scored by mean string match ratio per context bin.
Gemini 3.7 Flash 89% - CL-bench In use Model must learn new knowledge from a supplied long context (rules, procedures, data) and solve tasks. Strict LLM-verified rubrics. GPT-5.4 27.9%
- AA-LCR 1.1 In use Models answer 100 questions that need reasoning across sets of real documents of about 100K tokens. An LLM equality checker with a fixed system prompt compares each answer with the key.
Step 5 Preview 88.3%