WANDR
500 wide-search tasks: find tens to hundreds of entities and facts, then return structured records with citations.
- Level
- Everyday Most adults, without training
- Scored
- Graded by a model A language model scores each response, usually against a rubric.
- Answers
- Agent task Many steps in a terminal, browser, desktop, app, game or robot.
Claude Opus 5.5 leads with 72.3%
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 5.5 72.3%
- 2 Claude Sonnet 5.5 70%
- 3 Claude Haiku 5.5 49.9%
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | Claude Opus 5.5 | 72.3% | Sep 2026 | Model card | |
| 2 | Claude Sonnet 5.5 | 70% | Sep 2026 | Model card | |
| 3 | Claude Haiku 5.5 | 49.9% | Oct 2026 | Model card |
Number one over time
Lab claims count from the day the card came out, independent runs from the model’s release.
Scores over time
All the scores we have, one dot each, so a model tested at several settings appears more than once.
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.
3 system cards report it, from 1 lab
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 |
|---|---|---|---|---|
| 7 Oct 2026 | Claude Haiku 5.5 system card | 49.9 | WANDR, soft F1 at max effort, frozen web index, printed on chart (Figure 8.8.3.A) | Capabilities |
| 28 Sep 2026 | Claude Sonnet 5.5 system card | 70 | WANDR soft F1 at max effort, modified offline version (8.11.3, Figure 8.11.3.A) | Capabilities |
| 22 Sep 2026 | Claude Opus 5.5 system card | 72.3 | WANDR soft F1 at max effort, modified offline version with frozen index, printed on chart (8.11.3, Figure 8.11.3.A) | Capabilities |
More in Tool use
- AutomationBench In use Agent completes end-to-end business workflows inside a simulated company exposing dozens of REST APIs. Graded on resulting state.
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%