Until September 2026, “AI model” meant LLM: a model that generates text token by token. With the launch of Jev, there’s a concrete alternative for a significant portion of enterprise tasks: models that return typed values with probabilities instead of free text.
Two paradigms, two problems
Generative AI (LLMs)
An LLM receives text and produces text. Flexible but can hallucinate. Cost: $2-50/M tokens. Latency: seconds.
Typed AI (System One)
A typed model receives data and returns predefined format values: yes/no, score 0-10, a category from a closed list. Can’t generate anything outside the defined schema. Cost: $0.042/M tokens (Jev). Latency: 70-500ms.
Decision map by task
| Task | AI type | Why |
|---|---|---|
| Classify emails by intent | Typed | Closed output (sales, support, spam) |
| Reply to the email | Generative | Free text output |
| Score contract risk | Typed | Numeric output (0-10) |
| Explain why it’s risky | Generative | Free text argumentation |
| Detect spam | Typed | Binary output |
| Classify support ticket | Typed | Categorical output |
| Resolve support ticket | Generative | Requires reasoning and text |
The dividing line: if the output is a value from a defined set, typed AI. If the output is free text, LLM.
Mixed pipeline
The most efficient architecture uses each type where it performs best. For a system processing 100,000 queries/day:
| Architecture | Daily cost approx. |
|---|---|
| All LLM (Sonnet 5.5) | ~$200 |
| Mixed (typed + LLM only when needed) | ~$15 |
The LLM only runs on the 20% of cases requiring text generation. The rest uses typed AI at $0.042/M tokens.
For a complete model overview, see AI model map October 2026. Our AI agents team builds mixed pipelines.
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