Catalog Systems Memory benchmarks & evaluation BrowseComp

BrowseComp

https://openai.com/index/browsecomp/

OpenAI benchmark that measures whether browsing agents can find hard-to-locate information that may require visiting tens or hundreds of websites.

Active · Verified 2026-10-29 · fresh as of 2026-10-29 · 13 cited facts, 0 with a third-party source · 1 recorded change · Suggest a correction

At a glance

Type
Benchmark vendor fix
Tier
T3
Section
Memory benchmarks & evaluation
Created
2024-04-11 repo fix
Latest release
last commit 2026-04-22 repo fix
License
MIT vendor fix
GitHub
https://github.com/openai/simple-evals (4686★) repo fix
Pricing
searched not found fix
Funding
searched not found fix
Freshness
2026-10-29 fix
Layer
agent-infra repo fix
Licensing
open-source vendor fix
SDK languages
python repo fix

Memory taxonomy

storage
n/a
retrieval
n/a
persistence
n/a
update
read-only
unit
n/a
governance
n/a
conflict
n/a

When to use

Optimised for: searched not found

Anti-fit: searched not found

Pros & cons

Pros

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Cons

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Claims & capabilities

Abstract: We present BrowseComp, a simple yet challenging benchmark for measuring the ability for agents to browse the web. BrowseComp comprises 1,266 questions that require persistently navigating the internet in search of hard-to-find, entangled information. Despite the difficulty of the questions, BrowseComp is simple and easy-to-use, as predicted answers are short and easily verifiable against reference answers. BrowseComp for browsing agents can be seen as analogous to how programming competitions are an incomplete but useful benchmark for coding agents. While BrowseComp sidesteps challenges of a true user query distribution, like generating long answers or resolving ambiguity, it measures the important core capability of exercising persistence and creativity in finding information. BrowseComp can be found at this https URL .

Technical surface

API surface
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Backend storage
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Deployment
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Embedding model
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Multi-tenancy
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MCP
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A2A
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OpenTelemetry
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Similar systems

Other memory benchmarks & evaluation in the catalog, ranked by inbound references.

  • LoCoMo T3

    Conversations spanning up to 35 sessions / 300 turns / ~9K tokens, built from LLM-agent personas grounded on temporal event graphs and verified by human annotators. Tests QA (single-hop, multi-hop, temporal, commonsense, adversarial), event-graph summarisation, multimodal dialogue.

  • GAIA T3

    Benchmark for general AI assistants made of real-world questions that require reasoning, multimodality, web browsing and tool use, with a public leaderboard on Hugging Face. Traction: ~917 likes on the Hugging Face dataset

  • LongBench T3

    Tsinghua THUDM. 21 datasets across 6 categories (single-doc QA, multi-doc QA, summarisation, few-shot ICL, synthetic, code completion) in English + Chinese. ~6.7K words English / 13.4K chars Chinese average. v2 at ACL 2025 with harder, longer instances.

  • LongMemEval T3

    5 core memory abilities — info extraction, multi-session reasoning, temporal reasoning, knowledge updates, abstention — via 500 curated questions in synthetic chat histories. Two scales: LongMemEval-S (115K tokens), LongMemEval-M (up to 1.5M).

  • RULER T3

    NVIDIA. Extends NIAH with multi-needle retrieval, multi-hop entity tracing, aggregation, QA — configurable for arbitrary length / difficulty. Evaluated 17 long-context models on 13 tasks.

  • InfiniteBench (∞Bench) T3

    Tsinghua / OpenBMB. First benchmark with average input >100K tokens. 12 tasks across English + Chinese: long novel QA, code debugging, math in long context, summarisation, retrieval. Realistic documents (novels, code) rather than synthetic filler.

Sources (12 cited facts, 0 third-party)

Each fact links to the page it came from; the badge says who published it (how to read sources).

What changed

Changes to this record's lifecycle, licence, release, pricing and hosting facts, from the catalog's git history. Subscribe (Atom)

  1. 2026-10-30 Added to the catalog #347

Related systems

Referenced by (2)