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Simon Willison4 min readintermediate

Jev introduces a new shape of LLM - System One, aka Decision Models

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Summary

Jev is a new “System One” decision model that consumes text and returns numeric probabilities (yes/no, choice, score) instead of generated text, priced at $0.042 / M input tokens with free output. It’s fast, cheap, and suited for classification‑type tasks, but its opaque numeric output raises bias and explainability concerns.

  • Jev takes a text or semi‑structured "state" and returns floating‑point probabilities for yes/no, choice, or score queries, not text.
  • Pricing is $0.042 per million input tokens with no charge for output, cheaper than GPT‑5 Nano.
  • API lets you batch many questions per state; evaluations run in parallel for similar latency to a single query.
  • Ideal for cheap classification workloads like spam detection, label suggestion, or search reranking, enabling large‑scale experiments.

Product teams needing cheap, fast probabilistic decisions should consider Jev, but must weigh its opacity and bias risks.

6/10
llmapi designml infradecision modelsbias

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