Benchmark Almanac

xbench-Recruitment

o3 78.5 leads since Jun 2025 Not in model cards

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.

Abstract of xbench: Tracking Agents Productivity Scaling with Profession-Aligned Real-World Evaluations (arXiv:2506.13651, CC0)
Made by xbench (HongShan) Released Jun 2025 Running a business Tasks kept private Last model added May 2025 Scores refreshed 2 Nov 2026
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.
10
Entries on its own leaderboard, 8 models we track

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:

  1. 1 o3 78.5
  2. 2 Perplexity-Search 64.4
  3. 3 Claude 3.7 Sonnet 61.4
  4. 4 o4-mini high 61.4
  5. 5 Gemini 2.5 Flash May 2025 60.6
  6. 6 Perplexity-Research 59.1
  7. 7 Gemini 2.5 Pro May 2025 57.3
  8. 8 DeepSeek-R1 48.3
  9. 9 Grok 3 47.1
  10. 10 GPT-4o Mar 2025 38.9
#EntryScoreSettingDateSource
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 Gemini 2.5 Flash May 2025 60.6 May 2025 Official leaderboard
6 Perplexity-Research 59.1 May 2025 Official leaderboard
7 Gemini 2.5 Pro May 2025 57.3 May 2025 Official leaderboard
8 DeepSeek-R1 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.

OpenAI
Own leaderboard Not tested since Jun 2025
o3 78.5

Scores over time

All the scores we have, one dot each, so a model tested at several settings appears more than once.

On the benchmark’s own leaderboard
30.0 40.0 50.0 60.0 70.0 80.0 2025 30.0 40.0 50.0 60.0 70.0 80.0 2025

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.

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