jev — API, Pricing & Context Window | Vivgrid

jev is TypeSafe's System One model, and it does not write text. You send it the current state plus the questions you need answered, and it returns typed answers — a picked option, a score on your rubric, or a yes/no probability — each with a calibrated confidence value.

That makes it the right tool for the decisions an agent makes around the model call: routing a ticket, grading a retrieval hit, deciding whether a step is done, gating a retry. Those are classification problems, and running them through a frontier chat model means paying for generated tokens and parsing prose back into a type. Jev answers in 70–500 ms, typically 40–200× faster.

Specifications

Pricing

Pricing in USD per 1M tokens.

Question types

• Choice — pick one option from a map of option → rubric description
• Score — rate the state against an ordered array of levels, returning a continuous score plus its distribution
• Noul — a yes/no question, returning the probability the statement is true

A single request may carry many questions; state plus the longest question must fit in 32K tokens, and the whole request in 64K.

API surfaces

The Vivgrid Console catalogs jev on one surface, all behind the same API key. The endpoint shown is the one the Console previews for that surface.

Quick start

Get an API key from the Vivgrid Console, then call jev directly.

curl https://api.vivgrid.com/v1/systemone \
  -H "Authorization: Bearer $VIVGRID_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "jev",
    "state": "The website has been down for 2 hours and customers cannot complete purchases.",
    "questions": {
      "routing": {
        "type": "choice",
        "instructions": "Which team should handle this?",
        "criteria": {
          "billing": "Payments, invoicing, refunds",
          "technical": "Bugs, outages, integrations",
          "sales": "Pricing, upgrades, new accounts"
        }
      },
      "severity": {
        "type": "score",
        "instructions": "How severe is this issue?",
        "criteria": ["Minor", "Moderate", "Critical"]
      }
    }
  }'

Ideal use cases

• Routing, triage, and intent classification in agent pipelines
• Guardrails and gating decisions between tool calls
• Scoring retrieval results or model output against a rubric
• High-volume labeling where per-call latency and cost dominate

Related models

• decision-1 — Microsoft's decision model on the same /systemone API
• gemini-3.8-flash — a fast general-purpose model when you need generated text
• glm-5.3-flash — low-cost chat completions for lightweight classification