TypeSafe Jev: the First Decision-Only Model Class, Benchmarked and Priced

TypeSafe AI opened early access on September 15, 2026 for Jev, the first "System One" model: a model class that gives up text generation entirely and returns typed decisions with calibrated probabilities instead. The pitch is a change of interface, not just a new benchmark: $0.042 per million input tokens, output that TypeSafe says is free, end-to-end responses of 70-500ms, and a guarantee that the model cannot emit an output outside the schema you defined. On the company's own workflow evals, Jev lands within 3 points of the most expensive frontier models while costing roughly 4,000x less per call. Founder Diogo Almeida, who worked on the instruction-following methods behind ChatGPT at OpenAI, argues chat-shaped models are the wrong interface for automation: software does not want strings, it wants values it can branch on.

Official Sources#

What Shipped#

Jev is an early-access hosted model you reach at POST https://api.typesafe.ai/v1/systemone with model id jev-latest. You send one state (unstructured text or program state) plus any number of typed questions, and get every answer in a single call. Three primitives cover the decision space:

• Choice: pick one option from a defined set, returns the choice, per-option probabilities, and a confidence value.
• Score: rate the state against ordered descriptive levels, returns a score with probabilities and confidence.
• Noul: a boolean question, returns the probability the answer is yes.

Each question is evaluated in parallel and in isolation against the same state, so adding questions barely changes latency and does not create context-rot. TypeSafe's cookbook for parallel questions reports a 13-question briefing in one call being 12.2x cheaper and 10.0x faster than per-question calls, with identical answers.

The architecture is the unusual part. Jev uses a parallel sampler instead of autoregressive token generation: possible outputs are enumerated in advance, so schema conformance is guaranteed rather than sampled. Training uses a new recipe, Reinforcement Learning for Calibrated Decisions (RLCD), which optimizes for "epistemically honest" probabilities instead of human preference: higher confidence means higher accuracy.

The Numbers#

TypeSafe publishes most of its receipts on the workflow evals site: fixed code-defined workflows, reference answers from the average of GPT-6 Astra and Fable 5.1, and every model run through the same constrained answering path. Best workflow row per model, as published:

Read the caveats before quoting these anywhere: the workflows were designed by TypeSafe's own capabilities team, the reference answers bias toward OpenAI and Anthropic models, and the company calls its homepage multipliers (193.6x faster, 444.6x cheaper) "the higher end of real world gains." None of that changes the shape of the result: on these decision tasks, Jev matches the mid-tier frontier models at 25-145x lower latency and hundreds to thousands of times lower cost.

The mechanism cost story is where the numbers get extreme. TypeSafe's own Doom demo bot fires 10 decisions per second for about $7 an hour. An "is this urgent" check on every inbound support message is no longer metered in anything an invoice notices.

Pricing#

First-party pricing from the announcement and TypeSafe pricing:

TypeSafe claims this is 238x cheaper on input than Claude Fable 5.1, and is candid that the price may be subsidized: "we'll need the long-term to prove the sustainability of our pricing (which we expect to go down, not up)." Output tokens are still counted in usage, but not billed.

How to Call It#

There is no OpenCode provider for Jev yet - opencode models does not list it, and none of this site's automations run on it. The integration surface is the REST API plus official SDKs:

curl -X POST https://api.typesafe.ai/v1/systemone \
  -H "Authorization: Bearer $TYPESAFE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "state": "Hi, I have been trying to connect my Stripe account for 3 days and it keeps failing. I am losing sales. Please help ASAP.",
    "model": "jev-latest",
    "questions": {
      "urgency": { "type": "noul", "instructions": "Does this message express urgency?" }
    }
  }'

Python SDK: pip install typesafe-sdk (a JavaScript SDK ships too), plus an agent skill installable into Claude Code (claude plugin marketplace add typesafe-ai/skills) or any agent via npx skills add typesafe-ai/skills --skill typesafe-ai. Access is gated: an API key comes from the console dashboard once you are off the waitlist.

When a Decision-Only Model Fits#

Jev is deliberately incapable of open-ended work. The fit is work that ends in a JSON object anyway: routing and escalation (which team, which handler, with confidence deciding whether a human reviews first), scoring and ranking (sentiment, document relevance, review priority, thresholded in code), guardrails (screening prompts and outputs for jailbreaks), and high-volume extraction and classification.

The architecture pattern TypeSafe pushes is "keep code in control": decompose a judgment into atomic questions, then combine them with weights and thresholds you own. That is the same decomposition our model routing orchestration layer playbook prescribes, and the confidence channel is a second axis that router comparisons rarely account for. In a gateway-style setup like Envoy AI Gateway, a decision model becomes one more route target.

The honest boundary: Jev can no more write a diff or answer an open question than a router can. The interesting question is which share of your agent's calls were ever really generative. If your agents mostly classify, score, and branch, the "decision lane" just got a price point that changes the arithmetic - the division of labor between string models and decision models becomes a real architectural choice rather than a vendor's slide.

FAQ#

What is Jev?#

Jev is TypeSafe AI's first System One model, in early access since September 15, 2026. It does not generate text: you send structured questions about a state and receive typed answers with calibrated probabilities and confidence, in 70-500ms.

How much does Jev cost?#

$0.042 per million input tokens and no output charge, per the TypeSafe announcement. The company says the price may be subsidized and expects it to go down over time.

Can Jev hallucinate?#

Under its own guarantee, it cannot: possible outputs are enumerated in advance, so the model cannot return a value outside your schema. It can still be wrong about a decision - the calibration channel exists so you can threshold on confidence and send low-confidence cases to review.

Is Jev available in OpenCode?#

Not yet. There is no OpenCode provider or model id for it; the supported surfaces are the REST API, the official Python and JavaScript SDKs, and an installable agent skill.

How does Jev compare to structured outputs or JSON mode?#

Structured outputs force a string model to emit JSON and parse it. Jev never produces strings: outputs are typed at the sampling layer, every answer carries probabilities and confidence, and all questions in a call resolve in parallel.

Sources#

Last updated: September 16, 2026

Continue Reading#

• AI Model Routing and Orchestration Layer - how decision-shaped work should flow through your stack
• LLM Router Comparison 2026 - the routing layer as a second axis you control in code
• Envoy AI Gateway in Production - putting a decision-model route next to your chat models
• GPT-5.5 vs GLM-5.2: the Hallucination Benchmark - why hallucination rates are the same question with different instrumentation
• AI Coding Tools Pricing 2026 - what the rest of the market charges per million tokens today