Multi-agent search (Anthropic internal benchmark)
Agents solve difficult information retrieval problems with web search, alone or as orchestrators that delegate to subagents.
In-house Anthropic evaluation. The tasks are not public.
- Made by
- Anthropic
- Released
- November 2025
- Kind
- Deep research
- Level
- Expert
- Scored
- Graded by a model
- Answers
- Short answer
- Tasks
- Kept private
- Strongest model tested
- Claude Opus 4.5 #61 on the capability index
- Last in a system card
- November 2025
- Last model added
- November 2025
Claude Opus 4.5 leads with 74.8%
Best score per model as labs report them in their own cards. Labs pick their own settings, so these compare less cleanly than independent runs.
- 1 Claude Opus 4.5 74.8%
Source: Labs’ model cards
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | Claude Opus 4.5 | 74.8% | Nov 2025 | Lab model card |
Scores over time
All the scores we have, one dot each, so a model tested at several settings appears more than once.
Source: Labs’ model cards
Labs report it only in system cards
We read the headline results of 522 launch posts from 52 labs, and none of them list this benchmark. Some system cards do, below.
One system card reports it
Besides launch posts, we read the system cards, model cards and technical reports labs publish with a release. They report far more, including safety and frontier-risk tests.
| Date | Document | Score | As the document writes it | Section |
|---|---|---|---|---|
| Nov 24, 2025 | Claude Opus 4.5 system card | 74.8 | Multi-agent search internal benchmark, Claude Opus 4.5 single agent (Figure 2.7.1.A; orchestrator with Haiku 4.5 subagents 87.0%, Sonnet 4.5 subagents 85.4%, Opus 4.5 subagents 92.3%) | Capabilities |
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