System 1 decision models (Jev, Laya, Cua-S1) as brain for agents: Browser use, computer use, games and robotics
[!NOTE] Give your agents a System 1 decision model. Start from a prebuilt agent or build your own.
Describe the task. Claude Code or Codex runs a prebuilt agent or builds a new one, on Jev, Laya or Cua-S1 Nano. Browser use, computer use, robotics and games ship ready to run.
Up to 6× faster and 25× cheaper than a chat model, at the same score.
Quickstart · From Claude Code or Codex · Benchmarks · Docs
scenario Jev chat model speedup Jev cost chat model cost more expensive --------------------- Browser use (Allrecipes)† 35.7 s 138.7 s 3.89× $0.091356 $1.494331 16.4× Custom agent (ticket router, 30 tickets) 12.7 s 65.3 s 5.14× $0.000764 $0.014800 19.4× Computer use (Windows Calculator) 17.2 s 30.1 s 1.75× $0.000447 $0.003002 6.7× Robotics (ALFWorld) 7.4 s 25.7 s 3.47× $0.000221 $0.002800\ 12.7× Games (2048, 20 moves) 27.1 s 68.5 s 2.53× $0.000447 $0.006139 13.7× Games (Millionaire) 15.3 s 21.6 s 1.41× $0.000168 $0.000892 5.3× Games (Blackjack) 2.3 s 14.7 s 6.39× $0.000021 $0.000531 25.3×
† The first Allrecipes task of the WebVoyager task set (He et al., 2024, Apache-2.0, attribution in NOTICE): a vegetarian lasagna with over 100 reviews, 4.5 stars or more, for 6. The chat model of that row is Claude Fable 5.1 through OpenRouter; both models pay it for the typed search text and the answer. \ Estimated; the chat-model run recorded no cost. Each replay below is the episode behind its row, Jev on the left and the chat model on the right, both on the wall clock. The other Allrecipes runs, longer games and the Google Flights driver comparison: docs/benchmarks.md.
From the project README.
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