Anthropic Fable 5.2 Completes 41.3% of Real Freelance Jobs on Remote Labor Index
Anthropic's new model achieves a massive leap in completing real-world freelance tasks, far outpacing competitors and its own predecessors.
Anthropic's latest model, Fable 5.2, has achieved a significant milestone in AI agent capabilities by completing 41.3% of real freelance jobs at professional quality on the Remote Labor Index (RLI). This benchmark, developed by Scale Labs and the Center for AI Safety (CAIS), measures how often AI agents can successfully finish actual work assignments from platforms like Upwork to a standard acceptable to paying clients.
The progress represents a dramatic acceleration in practical AI utility. When the RLI launched approximately a year ago, the top-performing system, Manus, completed only 2.5% of tasks. Fable 5.2 now exceeds that initial baseline by more than sixteen times. Compared to recent measurements, the leap is equally stark: in July, Anthropic's previous flagship, Fable 5, scored about 16%, while its direct predecessor Fable 5.1 trails at just under 18%. Fable 5.2 effectively doubles the success rate of its immediate family members.
Scale Labs reports that Fable 5.2 shows its most substantial gains in editing audio and music, designing architecture and interior spaces, and creating 3D models. The model also handles long, complex projects that previously caused top-ranked systems to fail. Notably, nearly 50 of Fable 5.2's successful completions were tasks no other AI agent had solved before. However, weaknesses remain in industrial product design and animations, where failures often result in amateur-quality or broken outputs.
The RLI dataset comprises 240 completed projects from 358 verified professionals, with an average value of $632.60 per task and an average human completion time of 29 hours. Unlike tests focusing on isolated skills, these assignments require end-to-end execution using professional software such as Blender, GIMP, and Audacity. Human experts manually verify the AI's output against the original freelancer's work, ensuring that technically flawed but visually polished submissions are rejected.