xbench-Recruitment
Agents do 50 headhunting tasks from live business cases: company mapping, finding facts about people and finding people who fit a profile. A judge scores answers 1 to 5.
Profession Aligned track. Gemini 2.5 Flash judges and scores map to 0 to 100.
In the authors’ words
We introduce xbench, a dynamic, profession-aligned evaluation suite designed to bridge the gap between AI agent capabilities and real-world productivity. While existing benchmarks often focus on isolated technical skills, they may not accurately reflect the economic value agents deliver in professional settings. To address this, xbench targets commercially significant domains with evaluation tasks defined by industry professionals. Our framework creates metrics that strongly correlate with productivity value, enables prediction of Technology-Market Fit (TMF), and facilitates tracking of product capabilities over time. As our initial implementations, we present two benchmarks: Recruitment and Marketing. For Recruitment, we collect 50 tasks from real-world headhunting business scenarios to evaluate agents' abilities in company mapping, information retrieval, and talent sourcing. For Marketing, we assess agents' ability to match influencers with advertiser needs, evaluating their performance across 50 advertiser requirements using a curated pool of 836 candidate influencers. We present initial evaluation results for leading contemporary agents, establishing a baseline for these professional domains. Our continuously updated evalsets and evaluations are available at https://xbench.org.
- 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
- Agent task Many steps in a terminal, browser, desktop, app, game or robot.
o3 leads with 78.5 among models released by May 2025
Best score per entry from the benchmark's own leaderboard, read on 2 Nov 2026. Where a model ran with several settings or harnesses, we keep its best. Names in gray aren't in our model list: fine-tunes, agent systems, products and older models. They don't count toward leaders.
The newest model with a score here came out in May 2025, and nobody has tested a newer one. Among the 20 highest-ranked models on the capability index, these have no score yet:
- GPT-6 Astra
- Claude Opus 5.5
- GPT-6.1 Terra
- GPT-6.1 Sol
- and 16 more
- 1 o3 78.5
- 2 Perplexity-Search 64.4
- 3 Claude 3.7 Sonnet 61.4
- 4 o4-mini high 61.4
- 5
Gemini 2.5 Flash May 2025 60.6
- 6 Perplexity-Research 59.1
- 7
Gemini 2.5 Pro May 2025 57.3
- 8
DeepSeek-R1 48.3
- 9 Grok 3 47.1
- 10 GPT-4o Mar 2025 38.9
| # | Entry | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | o3 | 78.5 | Apr 2025 | Official leaderboard | |
| 2 | Perplexity-Search | 64.4 | May 2025 | Official leaderboard | |
| 3 | Claude 3.7 Sonnet | 61.4 | Feb 2025 | Official leaderboard | |
| 4 | o4-mini | 61.4 | high | Apr 2025 | Official leaderboard |
| 5 | | 60.6 | May 2025 | Official leaderboard | |
| 6 | Perplexity-Research | 59.1 | May 2025 | Official leaderboard | |
| 7 | | 57.3 | May 2025 | Official leaderboard | |
| 8 | | 48.3 | Jan 2025 | Official leaderboard | |
| 9 | Grok 3 | 47.1 | search | Apr 2025 | Official leaderboard |
| 10 | GPT-4o Mar 2025 | 38.9 | Mar 2025 | Official leaderboard |
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.
No lab model card reports it yet
We read the headline results of 479 launch posts from 51 labs, and none of them list this benchmark.
More in Economic work
- JobBench In use 130 tasks from 35 occupations workers most want delegated. Agent works through messy reference files, graded by fact-anchored binary rubrics. GLM-5.3 61.4%
- GDPval In use Real work tasks from 44 occupations with reference files. Experts blindly compare model deliverables with expert work. Metric is win-or-tie rate.
Seed2.1 Pro 87.9%
- GDPval-AA v2.1 In use Agents with shell and web access complete 220 GDPval gold tasks from 44 occupations. Blind pairwise comparisons give Elo ratings. Claude Opus 5.5 1,846
- AA-Briefcase v1.1 In use Version 1.1 of Artificial Analysis's multi-hour office work evaluation, with Elo ratings fitted by Crowd-BT. Claude Opus 5.5 1,822
- CoWorkBench In use Agent completes long-horizon productivity tasks across computer science, finance, law and medicine. Qwen in-house scoring (method unpublished).
Qwen3.8-Omni-Flash 75.3%
- OfficeQA In use Grounded reasoning over a large corpus of U.S. Treasury Bulletin documents: locate tables and compute precise numeric answers. Claude Opus 4.7 86.3%