Decision Models (DMs)

Canonical version: Decision Models (DMs).

A decision model (DM) is an AI model whose output is a decision, not text. You give it an input and a bounded set of possible answers, and it gives you back numbers: which option, how likely yes is, what score. It's the category Jev belongs to, and I think it deserves its own name next to Large Language Models (LLMs) and Small Language Models (SLMs).

Why bother with a separate term? Because a LOT of what we ask LLMs to do today is actually deciding. Is this email urgent? Which tool should the agent call? Is this paragraph off-topic? Did the agent get stuck? We ask a text generator to write the answer as JSON, parse it, validate it, and retry when it breaks. A decision model answers that kind of question directly, and that changes the economics: when a decision costs a fraction of a cent and takes 200 ms, you can afford to ask it everywhere.

What makes a model a decision model

• The answer space is bounded and known upfront. Options, yes/no, or a numeric range. If you can't list the possible answers, you need an LLM
• The output is typed by construction. There's no text to parse, so malformed output can't happen
• The output is a probability. Ideally a calibrated one: when it says 80%, it's right about 80% of the time. That lets you set real thresholds ("send to a human below 0.7"), something you can't do with an LLM writing "I'm 90% sure"
• No generation loop. The input is read once and the answer comes out of a readout layer, not token by token. That's where the speed comes from

An old idea with a new twist

Decision models aren't new. We've had them for decades under other names:

• Classic classifiers: spam filters, sentiment analysis, intent routing (see Binary Classification and Data classification). Fast and cheap, but trained for ONE task with your own labeled data
• Zero-shot classifiers: NLI-based models (e.g., BART fine-tuned on MNLI) that score arbitrary labels without training. Closer to the idea, but limited in quality
• Rerankers and reward models: they score an input against a query or a preference. Decisions again, just packaged for search and for training LLMs
• LLMs used as deciders: prompt an LLM, force LLM Structured Outputs, or read the token probabilities of each option. It works, but you pay for a text generator to pick a label

What changed in 2025-2026 is generality. A general decision model answers arbitrary questions over arbitrary options at inference time, with no task-specific training, at a quality close to a frontier LLM. Laya went in that direction in March 2025. TypeSafe AI made it a product with Jev in September 2026, and calls the category System One Models, after Daniel Kahneman's fast, intuitive System 1. I prefer "decision model" as the generic term: it says what the thing outputs, and it doesn't depend on one company's branding.

DMs vs LLMs

They're complementary. The pattern I find the most useful (from Nate B. Jones) is to think in three building blocks: code for rules and calculations, decision models for bounded judgment, LLMs for open reasoning, writing and exceptions. A decision model can even decide when an LLM is worth calling, which is Model routing done cheaply.

Current examples

• Jev by TypeSafe AI: closed, the first commercial general decision model
• Laya: open, multilingual, published in 2025
• Kev: open models on top of Qwen with a Jev-compatible API
• SemIf (formerly OpenJev): reads the probabilities of each answer from an LLM's output, runs locally
• Jevlike: a small trainable option scorer, closer to a classic classifier
• fast-jev-compaction: an example of what DMs enable, compacting a Claude Code session by scoring each tool call instead of summarizing

Where to use them

A decision model rewards a specific way of building: many narrow questions answered in parallel, combined in code (Atomic Question Decomposition), or fed as features to a small classical model trained on your labels (LLM-Generated Features for Classical ML).

• Classification at scale: tickets, emails, leads, posts, ads
• Guardrails and checks inside AI Agents: is this relevant, is the agent stuck, which tool, how much reasoning effort
• Asking many small questions about the same input (ten yes/no checks per paragraph for a prose linter, for instance)
• Triage before an LLM: let the DM filter, and only send the hard cases to the expensive model

Watch out for

• No reasoning to inspect. You get a number, not a why. Bias and blind spots are harder to see, so you need MORE evals, not fewer
• Calibration isn't universal. Calibrated on the vendor's data doesn't mean calibrated on yours. Measure it on your own distribution before trusting thresholds
• Wrong tool for open questions. If the right answer isn't in your list of options, the model will still confidently pick one. Pair the pick with an absolute yes/no check per option, or a "does an answer exist at all?" question (Relative vs Absolute AI Judgments)
• Vendor dependence. The best general DM today (Jev) is closed, and its terms forbid publishing benchmarks. The open alternatives are behind but closing the gap

My take: this is one of the most practical ideas in AI this year. Not because the tech is magic (it isn't), but because it makes judgment cheap enough to sprinkle everywhere. I expect many of the "small" LLM calls in my own automations to move to a decision model over time.

References

• Introducing System One Models & Jev (TypeSafe AI)
• Jev notes (Simon Willison)
• Nate B. Jones on Jev build patterns
• Jev's architecture unmasked (Archer Hume)

Related

• Decision Models (MoC)
• System One Models
• Jev
• TypeSafe AI
• Laya
• Kev
• SemIf
• Jevlike
• Large Language Models (LLMs)
• Small Language Models (SLMs)
• LLM Structured Outputs
• Model routing
• Binary Classification
• AI Agents
• System One Primitives
• Atomic Question Decomposition
• Relative vs Absolute AI Judgments
• Self-Consistency
• LLM-Generated Features for Classical ML
• Machine Native Intelligence
• AI Model Calibration
• Confidence-Gated Routing
• AI Model Cascades
• Jevons Paradox
• Zero-Shot Classification

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