Mercor✓

Cognition @cognition

Founded by three 22-year-olds in 2023, @mercor crossed $2B ARR in June, only 4 months after hitting $1B. Today, millions of experts on the platform bring their knowledge of medicine, law, engineering, and beyond to frontier AI - helping models take on increasingly complex and consequential work in the real world. Mercor's engineers move at extraordinary speed. They give Devin long-running tasks before bed, during meetings, or on their way to the gym. They come back to tested features and PRs ready to merge.

Mercor @mercor

Workers in six occupations do 79 tasks the government's official occupational taxonomy has yet to add. That work is worth about $140B a year in wages. That taxonomy is called O*NET, the U.S. Department of Labor's database of the tasks each occupation involves. Workers use it to help with job transitions, and economists use it to estimate which jobs AI will change. If the lists are incomplete, so are the estimates. Today we're releasing TaskNet, an open-source task catalog built on O*NET and updated for how AI is changing work. Built with researchers from Stanford, the O*NET Resource Center, and NBER. Thank you to @robinsonaisaac @erikbryn @diyi_yang @jennjwang @andreas_h0wpt @Digeconlab @stanfordnlp @stanfordhai @stanfordAIlab for contributing to this work. Read the blog:

Mercor @mercor

GPT-6.1 Terra is OpenAI’s #1 model on APEX-Agents. 68.3% Pass@1 (#6) 79.3% mean score (#6) Compared to last generation GPT-5.6 Terra, the new model gains 10.1 points on Pass@1. Management consulting: 80.9% Pass@1 (#2) Investment banking: 66.2% Pass@1 (#7) Corporate law: 57.7% Pass@1 (#39) The model is strongest on management consulting tasks, ranking second overall, behind Gemini 4 Argon (88.4%). Against GPT-5.6 Terra, it gains 24.7 points in consulting and 10.9 in banking. Law goes the other way, down 5.4 points from GPT-5.6 Terra. GPT-6.1 Terra still averages 80.6% of rubric criteria on law tasks, but fully completes only 57.7% of them. It gets most of the way and misses the last requirements. Across 4 runs per task, GPT-6.1 Terra passed 134 of 240 tasks every time and 187 at least once. 22 tasks it never scored on, 14 of them in investment banking. GPT-6.1 Terra’s strength comes from reading more than its siblings: 735k input tokens per attempt, vs 497k for GPT-6 Astra and 489k for GPT-6.1 Sol. Despite reading more, it costs less. GPT-6.1 Terra: $0.19 per task ($1/$5 per 1M tokens) GPT-6.1 Sol: $0.26 GPT-6 Astra: $1.30 It’s also a fast model. The median attempt finishes 1.2x faster than GPT-6 Astra and on par with GPT-6.1 Sol. Congrats @OpenAI on the launch. See full leaderboard:

MTS @MTSlive

OPENAI PHYSICS BREAKTHROUGHS | BLUECHIP LAPTOP | QWEN NEURONS

Mercor @mercor

Workers in six occupations do 79 tasks the government's official occupational taxonomy has yet to add. That work is worth about $140B a year in wages. That taxonomy is called O*NET, the U.S. Department of Labor's database of the tasks each occupation involves. Workers use it to help with job transitions, and economists use it to estimate which jobs AI will change. If the lists are incomplete, so are the estimates. Today we're releasing TaskNet, an open-source task catalog built on O*NET and updated for how AI is changing work. Built with researchers from Stanford, the O*NET Resource Center, and NBER. Thank you to @robinsonaisaac @erikbryn @diyi_yang @jennjwang @andreas_h0wpt @Digeconlab @stanfordnlp @stanfordhai @stanfordAIlab for contributing to this work. Read the blog:

Mercor @mercor

Workers in six occupations do 79 tasks the government's official occupational taxonomy has yet to add. That work is worth about $140B a year in wages. That taxonomy is called O*NET, the U.S. Department of Labor's database of the tasks each occupation involves. Workers use it to help with job transitions, and economists use it to estimate which jobs AI will change. If the lists are incomplete, so are the estimates. Today we're releasing TaskNet, an open-source task catalog built on O*NET and updated for how AI is changing work. Built with researchers from Stanford, the O*NET Resource Center, and NBER. Thank you to @robinsonaisaac @erikbryn @diyi_yang @jennjwang @andreas_h0wpt @Digeconlab @stanfordnlp @stanfordhai @stanfordAIlab for contributing to this work. Read the blog:

Mercor @mercor

Workers in six occupations do 79 tasks the government's official occupational taxonomy has yet to add. That work is worth about $140B a year in wages. That taxonomy is called O*NET, the U.S. Department of Labor's database of the tasks each occupation involves. Workers use it to help with job transitions, and economists use it to estimate which jobs AI will change. If the lists are incomplete, so are the estimates. Today we're releasing TaskNet, an open-source task catalog built on O*NET and updated for how AI is changing work. Built with researchers from Stanford, the O*NET Resource Center, and NBER. Thank you to @robinsonaisaac @erikbryn @diyi_yang @jennjwang @andreas_h0wpt @Digeconlab @stanfordnlp @stanfordhai @stanfordAIlab for contributing to this work. Read the blog: