Side-by-side comparison
The useful question is not which model is “stronger,” but which output shape fits the current step. A complete product may use Jev, an LLM, and ordinary code together.
| Dimension | Jev | LLM |
|---|---|---|
| Primary output | Predefined choices, scores, probabilities | Free-form text, code, and structured output |
| Best fit | High-volume constrained semantic decisions | Creation, explanation, planning, and open-ended reasoning |
| Sampling | Many decisions returned in parallel | Usually generated token by token |
| Current input | Text only | Text and, depending on model, multimodal input |
| Cost model | $0.042/M input tokens; output free | Input and output usually billed separately |
| Failure mode | Wrong judgment or poorly tuned confidence threshold | Factual, format, and tool-use errors among others |
Prefer Jev for these tasks
- The answer set can be defined in advance, such as departments, tools, or actions.
- You need many independent judgments over the same input.
- Latency and per-call cost materially affect the product.
- Software needs confidence to choose automation or review.
Keep an LLM for these tasks
- A person needs to read a natural-language response.
- The task involves creation, explanation, summarization, or coding.
- Possible answers cannot be reasonably defined before the request.
- The workflow needs long-horizon planning, tool coordination, or multimodal understanding.
The most useful combination
Use an LLM to understand goals, generate candidates, or communicate; use Jev for high-volume filtering, routing, and verification; use code for permissions, money, dates, and final control flow.
Do not hand high-risk decisions entirely to Jev because its output structure is stable. Structural validity and judgment accuracy are separate properties.
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