Jev
Jev is TypeSafe AI’s “System One” decision model: it generates no text, but returns a structured decision (option, score, or probability) over known options so your code can branch on it directly.
About Jev
Jev is the flagship model from TypeSafe AI, which the vendor calls the first product in the “System One model” category. Instead of generating text token by token, it evaluates typed questions asked about a given state (state) and returns a structured decision — a selected option, a score, or a probability — that your code can branch on directly. In other words, it only does one thing: choose from a known set of options. It fits decisioning scenarios that are high-concurrency, low-latency, and closed-answer-space, such as routing every ticket, scoring every retrieval result, or gating every tool call. It produces no prose, so it cannot write summaries or answer open-ended questions; those tasks still need a traditional LLM. The model is available only as a hosted API, with no open weights and no self-hosting or VPC deployment options, and it offers Python and JavaScript SDKs while staying compatible with TypeSafe’s /v1/systemone request format and an OpenAI-compatible endpoint.
Best for
Best use cases
Automatically route every inbound ticket to a queue, rate its urgency, and flag churn risk (official ruleset example: support-ticket-triage)
Score every retrieved passage to decide whether it enters the downstream flow
Gate every tool call with a decision
Bulk decisioning scenarios that need high concurrency and low latency (the vendor states 70–500ms end-to-end)
Compare before choosing
The official comparison page states that LLMs win on accuracy — the vendor’s own table: Jev 67.8%, GPT-5.6 Sol 74.1%, Opus 5 73.1%
The official comparison page states a latency of 0.4 seconds versus 10–38 seconds (vendor report, measured per decision)
The official comparison page states a cost of $0.0004 per decision versus $0.03–$0.18 (vendor report)
Features
Structured decision output
Ask typed questions about a state and get back the selected option, score, or probability, so your code can branch on it directly.
Low-latency decisions
The vendor states end-to-end latency of 70–500ms, suited to real-time decisions on every tool call or data record.
Batch requests
Up to 20 items per request, with failures reported per item without affecting the rest of the batch.
Python / JavaScript SDK
Python and JavaScript SDKs, with the same request format as TypeSafe’s /v1/systemone, plus an OpenAI-compatible endpoint.
64k context
64k token context window, with a 64k token cap per request (state + longest question totals 32k).
Pros
- The vendor states about $0.0004 per decision, roughly 75x cheaper and 25x faster than a traditional LLM (vendor-reported figures); the official pricing page measures a single Choice call at only $0.0000144, about 20x below that figure, because a typical state is only a few hundred tokens
- Output is free, so cost scales only with input tokens (state + question), and multiple questions can be merged into one call that shares the state
- An API key is issued on signup, with no waitlist and no card required
- Purchased credits never expire, and failed requests are not billed
- No plan has a per-minute limit; usage is counted in credits
Cons
- The official comparison page states that its raw decision accuracy is below mainstream LLMs (vendor table: Jev 67.8%, GPT-5.6 Sol 74.1%, Opus 5 73.1%)
- Fully closed source: hosted API only, no open weights, no self-hosting or VPC option
- Rate limits (100k tokens per second, 80 requests per second) are being adjusted according to the vendor and may change without notice
- Text input only at launch, with no image or audio support