// AI news · 2026-11-02 · Dev.to LLM
LLMs, Jev, CDMs: Three Answering Models Compared
Autoregressive LLMs generate text token-by-token, while Jev and CDMs return structured decisions without writing.
Published 2026-11-02 02:53 (Beijing time)Source Dev.to LLMAuto-summarised, fact-checked against source
01Summary
LLMs excel in open-ended tasks but are slow and costly for narrow queries. Jev provides probabilistic choices or scores directly, with no visible training details. CDMs extract structured data in one pass, ideal for judgment-based fields like post-credit-note totals. All three differ in latency, cost, and output format.
Summarised from the original article by an open-source model, then checked automatically: every number and English name must appear in the source. The original article is authoritative.
02Key points
- LLMs generate text token-by-token, making them slow and costly for narrow queries.
- Jev returns probabilities or choices directly without text generation, with no public training details.
- CDMs extract structured data in one input pass, suitable for judgment-based fields.
- Performance varies by task: LLMs for open-ended tasks, Jev/CDMs for decision-focused ones.
03Source
LLMs vs Jev vs CDMs: three ways a model can answer
Dev.to LLM · published 2026-11-02 02:53 Beijing time
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