Remote Labor Index
Agents complete real paid remote freelance projects from many sectors. Reviewers judge whether each deliverable would be accepted as commissioned work, giving an automation rate.
The best agent automated only 2.5% of projects at launch. Only a 10 task public set is released.
- Made by
- Center for AI Safety and Scale AI
- Released
- October 2025
- Kind
- Tasks from real jobs
- Level
- Expert
- Scored
- Graded by people
- Answers
- Open-ended
- Tasks
- Public, plus a private test set
- Strongest model tested
- Claude Fable 5.2 #1 on the capability index
- Last in a launch post
- November 2026
- Last model added
- November 2026
- Scores refreshed
- November 12th, 2026
In the authors’ words
AIs have made rapid progress on research-oriented benchmarks of knowledge and reasoning, but it remains unclear how these gains translate into economic value and automation. To measure this, we introduce the Remote Labor Index (RLI), a broadly multi-sector benchmark comprising real-world, economically valuable projects designed to evaluate end-to-end agent performance in practical settings. AI agents perform near the floor on RLI, with the highest-performing agent achieving an automation rate of 2.5%. These results help ground discussions of AI automation in empirical evidence, setting a common basis for tracking AI impacts and enabling stakeholders to proactively navigate AI-driven labor automation.
Claude Fable 5.2 leads with 41.3%
Best score per entry from the benchmark's own leaderboard, read on November 12th, 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.
Claude Fable 5.2 leads with 41.3%, claims included
Best score per model from every source, including labs’ own numbers and claims Epoch AI collected from reports and papers. Labs pick their own settings, so a claim can beat an independent run of the same model.
- 41.3 Claude Fable 5.2
- 21.3 Claude Opus 5.5
- 20.8 GPT-6 Astra
- 17.9 Claude Fable 5.1
- 17.1
Gemini 4 Argon
- 15.8 Claude Fable 5
- 9.58
DeepSeek V4.1 Flash
- 8.33 Claude Opus 4.8
- 6.25 GPT-5.5
- 5.83
Gemini 3.8 Flash
- 5.00
Gemini 3.7 Flash
- 4.17 Claude Opus 4.6
- 3.75 Claude Opus 4.5
- 2.92 Manus_1.6
- 2.50 Manus 1.0
- 2.50 Manus 1.5
- 2.50 GPT-5.2
- 2.08 Claude Sonnet 4.5
- 1.67 GPT-5
- 1.25 ChatGPT agent
- 1.25
Gemini 3 Pro
- 0.83
Gemini 2.5 Pro
- 1 Claude Fable 5.2 41.3%
- 2 Claude Opus 5.5 21.3%
- 3 GPT-6 Astra 20.8%
- 4 Claude Fable 5.1 17.9%
- 5
Gemini 4 Argon 17.1%
- 6 Claude Fable 5 15.8%
- 7
DeepSeek V4.1 Flash 9.58%
- 8 Claude Opus 4.8 8.33%
- 9 GPT-5.5 6.25%
- 10
Gemini 3.8 Flash 5.83%
- 11
Gemini 3.7 Flash 5.00%
- 12 Claude Opus 4.6 4.17%
- 13 Claude Opus 4.5 thinking 3.75%
- 14 Manus_1.6 max 2.92%
- 15 Manus 1.0 2.50%
- 16 Manus 1.5 2.50%
- 17 GPT-5.2 medium 2.50%
- 18 Claude Sonnet 4.5 2.08%
- 19 GPT-5 1.67%
- 20 ChatGPT agent 1.25%
- 21
Gemini 3 Pro 1.25%
- 22
Gemini 2.5 Pro Jun 2025 0.83%
Source: Official Remote Labor Index leaderboard
- 41.3 Claude Fable 5.2
- 21.3 Claude Opus 5.5
- 20.8 GPT-6 Astra
- 17.9 Claude Fable 5.1
- 17.1
Gemini 4 Argon
- 15.8 Claude Fable 5
- 9.58
DeepSeek V4.1 Flash
- 8.33 Claude Opus 4.8
- 6.25 GPT-5.5
- 5.83
Gemini 3.8 Flash
- 5.00
Gemini 3.7 Flash
- 4.17 Claude Opus 4.6
- 3.75 Claude Opus 4.5
- 2.50 GPT-5.2
- 2.08 Claude Sonnet 4.5
- 1.67 GPT-5
- 1.25 ChatGPT agent
- 1.25
Gemini 3 Pro
- 0.83
Gemini 2.5 Pro
- 1 Claude Fable 5.2 41.3%
- 2 Claude Opus 5.5 21.3%
- 3 GPT-6 Astra 20.8%
- 4 Claude Fable 5.1 17.9%
- 5
Gemini 4 Argon 17.1%
- 6 Claude Fable 5 15.8%
- 7
DeepSeek V4.1 Flash 9.58%
- 8 Claude Opus 4.8 8.33%
- 9 GPT-5.5 6.25%
- 10
Gemini 3.8 Flash 5.83%
- 11
Gemini 3.7 Flash 5.00%
- 12 Claude Opus 4.6 4.17%
- 13 Claude Opus 4.5 thinking 3.75%
- 14 GPT-5.2 medium 2.50%
- 15 Claude Sonnet 4.5 2.08%
- 16 GPT-5 1.67%
- 17 ChatGPT agent 1.25%
- 18
Gemini 3 Pro 1.25%
- 19
Gemini 2.5 Pro Jun 2025 0.83%
Sources: Labs’ model cards and official Remote Labor Index leaderboard
| # | Entry | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | Claude Fable 5.2 | 41.3% | Nov 2026 | Official leaderboard | |
| 2 | Claude Opus 5.5 | 21.3% | Sep 2026 | Official leaderboard | |
| 3 | GPT-6 Astra | 20.8% | Sep 2026 | Official leaderboard | |
| 4 | Claude Fable 5.1 | 17.9% | Sep 2026 | Official leaderboard | |
| 5 | | 17.1% | Sep 2026 | Official leaderboard | |
| 6 | Claude Fable 5 | 15.8% | Jun 2026 | Official leaderboard | |
| 7 | | 9.58% | Sep 2026 | Official leaderboard | |
| 8 | Claude Opus 4.8 | 8.33% | May 2026 | Official leaderboard | |
| 9 | GPT-5.5 | 6.25% | Codex | Apr 2026 | Official leaderboard |
| 10 | | 5.83% | Sep 2026 | Official leaderboard | |
| 11 | | 5.00% | Aug 2026 | Official leaderboard | |
| 12 | Claude Opus 4.6 | 4.17% | Cowork | Feb 2026 | Official leaderboard |
| 13 | Claude Opus 4.5 | 3.75% | thinking | Nov 2025 | Official leaderboard |
| 14 | Manus_1.6 | 2.92% | max | Official leaderboard | |
| 15 | Manus 1.0 | 2.50% | Official leaderboard | ||
| 16 | Manus 1.5 | 2.50% | Official leaderboard | ||
| 17 | GPT-5.2 | 2.50% | medium | Dec 2025 | Official leaderboard |
| 18 | Claude Sonnet 4.5 | 2.08% | Sep 2025 | Official leaderboard | |
| 19 | GPT-5 | 1.67% | Aug 2025 | Official leaderboard | |
| 20 | ChatGPT agent | 1.25% | Jul 2025 | Official leaderboard | |
| 21 | | 1.25% | Nov 2025 | Official leaderboard | |
| 22 | | 0.83% | Jun 2025 | Official leaderboard |
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | Claude Fable 5.2 | 41.3% | Nov 2026 | Lab model card | |
| 2 | Claude Opus 5.5 | 21.3% | Sep 2026 | Official leaderboard | |
| 3 | GPT-6 Astra | 20.8% | Sep 2026 | Official leaderboard | |
| 4 | Claude Fable 5.1 | 17.9% | Sep 2026 | Official leaderboard | |
| 5 | | 17.1% | Sep 2026 | Official leaderboard | |
| 6 | Claude Fable 5 | 15.8% | Jun 2026 | Official leaderboard | |
| 7 | | 9.58% | Sep 2026 | Official leaderboard | |
| 8 | Claude Opus 4.8 | 8.33% | May 2026 | Official leaderboard | |
| 9 | GPT-5.5 | 6.25% | Codex | Apr 2026 | Official leaderboard |
| 10 | | 5.83% | Sep 2026 | Official leaderboard | |
| 11 | | 5.00% | Aug 2026 | Official leaderboard | |
| 12 | Claude Opus 4.6 | 4.17% | Cowork | Feb 2026 | Official leaderboard |
| 13 | Claude Opus 4.5 | 3.75% | thinking | Nov 2025 | Official leaderboard |
| 14 | GPT-5.2 | 2.50% | medium | Dec 2025 | Official leaderboard |
| 15 | Claude Sonnet 4.5 | 2.08% | Sep 2025 | Official leaderboard | |
| 16 | GPT-5 | 1.67% | Aug 2025 | Official leaderboard | |
| 17 | ChatGPT agent | 1.25% | Jul 2025 | Official leaderboard | |
| 18 | | 1.25% | Nov 2025 | Official leaderboard | |
| 19 | | 0.83% | Jun 2025 | Official leaderboard |
Labs report scores for 1 model in their cards. Switch the scores above to rank them together.
10 models have held number one on the official leaderboard since its release in October 2025 10 models have held number one with claims included since its release in October 2025
Lab claims count from the day the card came out, independent runs from the model’s release.
Source: Official Remote Labor Index leaderboard
Scores over time
All the scores we have, one dot each, so a model tested at several settings appears more than once.
Sources: Official Remote Labor Index leaderboard and labs’ model cards
One lab put it in a launch post in the last six months
Launch posts from every lab that has listed it, one tick per post. Tall colored ticks include it, and short gray ones leave it out.
| Date | Launch post | Score | As the post writes it |
|---|---|---|---|
| Nov 9, 2026 | Claude Fable 5.2 | 41.3 | Knowledge work Remote Labor Index (RLI), High effort (launch table and Table 8.1.A) |
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 9, 2026 | Claude Fable 5.2 system card | 41.3 | Remote Labor Index, automation rate, 99 of 240 projects, default High effort, 200k context, 32k output tokens per response, independently run by Scale AI (Table 8.1.A and Section 8.14.7) | Capabilities |
One lab claim next to the leaderboard
Each score a lab reported for its own model, beside the same model's best score on the leaderboard. Gaps mostly come from different harnesses, effort levels and task subsets. 1 of them match, which usually means one side copied the other. Every benchmark.
| Model | Lab says | Board says | Gap |
|---|---|---|---|
| Claude Fable 5.2 | 41.3% Knowledge work Remote Labor Index (RLI), High effort (launch table and Table 8.1.A) | 41.3% Leaderboard | Same |
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). Claude Fable 5 75.9%
- 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%