Multi-agent search (Anthropic internal benchmark)
Claude Opus 4.5 74.8% Only in system cards
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 Nov 2025 Deep research Tasks kept private Last in a system card Nov 2025
Last model added Nov 2025
- Level
- Expert Specialists with years of training, often over hours of work
- Scored
- Graded by a model A language model scores each response, usually against a rubric.
- Answers
- Short answer A number, word or short string matched to a key.
1
Model scored in lab cards
1
System card reports it, from 1 lab
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%
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | Claude Opus 4.5 | 74.8% | Nov 2025 | Model card |
Scores over time
All the scores we have, one dot each, so a model tested at several settings appears more than once.
Reported by the lab in its model card
Labs report it only in system cards
We read the headline results of 479 launch posts from 51 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 |
|---|---|---|---|---|
| 24 Nov 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 |
More in Tool use
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Gemini 4 Carbon 53.9% - MCP Atlas In use Multi-step workflows over real MCP servers. Scores tool-use competency on realistic tasks.
Muse Spark 1.1 88.1% - BFCL V4 In use Models call functions and tools, including multi-turn agentic tasks with web search and memory. Checked against expected calls and outcomes.
Qwen3.7-Max 75% - τ³-bench Banking In use Agents act as banking customer-service reps over many turns with a simulated user, following policy. Pass^1 on the final database state. Qwen 3.8 Max 55.1%
- Claw-Eval In use 300 human-verified agent tasks (service orchestration, multimodal, multi-turn dialogue) audited via traces and snapshots. Rubric scores with Pass^3 consistency. Claude Sonnet 4.6 81.4%
- DeepSearchQA In use Questions that need multi-step search and synthesis. Final answers graded against references. Kimi K3 95%