Migrate from TypeSafe or OpenAI

POST https://api.mouse.dev/v1/decisions (primary) and POST https://api.mouse.dev/v1/systemone (alias) are wire-compatible with TypeSafe's System One API (jev-latest / jev-1.13.0), and also accept OpenAI's Decisions API (POST /v1/decisions on gpt-6-luna) field names directly.

Side-by-side comparison: Mouse vs TypeSafe Jev vs OpenAI Decisions

Feature / Field	Mouse (mouse-1)	TypeSafe Jev (jev-1.13.0)	OpenAI Decisions (gpt-6-luna)
Endpoint	POST https://api.mouse.dev/v1/decisions (alias /v1/systemone)	POST https://api.typesafe.ai/v1/systemone	POST https://api.openai.com/v1/decisions
Model name	"mouse-1" (snapshot mouse-preview-2026-10-13)	"jev-latest" (jev-1.13.0)	"gpt-6-luna"
Input / context field	state (string, JSON object, or array)	state (string, JSON object, or array)	input (string or user message array)
Questions container	questions object map ({ "<id>": Question })	questions object map ({ "<id>": Question })	questions array ([{ "name": "<id>", ... }])
Binary yes/no primitive	type: "noul" → answer.noul (0..1)	type: "noul" → answer.noul (0..1)	type: "predicate" → answer.probability (0..1)
Multi-option primitive	type: "choice", criteria: { key: desc } (up to 1,024 options)	type: "choice", criteria: { key: desc } (up to 255 options)	type: "choice", choices: [{ value, description }]
Choice probabilities shape	Object map: { "billing": 0.91, "shipping": 0.09 }	Object map: { "billing": 0.91, "shipping": 0.09 }	Array: [{ "value": "billing", "probability": 0.91 }, ...]
Ordinal rubric primitive	type: "score", criteria: [lvl0, lvl1, ...] (2–10 levels)	type: "score", criteria: [lvl0, lvl1, ...] (2–10 levels)	type: "score", levels: [{ label, description }]
Score probabilities shape	Object map: { "0": 0.1, "1": 0.7, "2": 0.2 } + legend	Object map: { "0": 0.1, "1": 0.7, "2": 0.2 } + legend	Array: [{ "value": 0, "label": "...", "probability": 0.1 }, ...]
Confidence formulas	System One choice & score formulas + mouse.p_top	System One choice & score formulas	Undocumented internal confidence scalar
Refusal answer type	Never refuses (choice, noul, or score always returned)	Never refuses	Can return {"type": "refusal", "name": "..."}
First-party calibration	POST /v1/calibrations → signed calp1. profile + mouse.abstain	None (manual thresholding only)	None (manual thresholding only)
Max state / context	65,536 state tokens (422 on overflow, never truncated)	32k state+longest question / 64k request	Not published
Rate limits	200 RPS (burst 400), 20M TPM published on every key	80 RPS / 100K TPS ("adjusting dynamically")	Tier-based RPM
Price per 1M tokens	$0.10 input · $0.02 cached (5m) · $0 output	$0.042 input · no cache tier · $0 output	$0.10 input (+10% regional) · no cache discount · $0 output
Itemized usage & cost_usd	input_tokens, state_tokens, question_tokens, framing_tokens, cached_tokens, cost_usd	input_tokens, output_tokens	Token counts only
Zero Data Retention (ZDR)	Default for every customer (Mouse-Data-Retention: none)	Enterprise plan via sales only	Eligible customers with prior approval

---

1. Migrating from TypeSafe Jev (POST /v1/systemone)

Because Mouse implements the exact /v1/systemone request, answer, and confidence specification, migrating from Jev requires changing only the base URL and model name:

const res = await fetch("https://api.typesafe.ai/v1/systemone", {
const res = await fetch("https://api.mouse.dev/v1/systemone", {
  method: "POST",
  headers: {
    Authorization: `Bearer ${process.env.TYPESAFE_API_KEY}`,
    Authorization: `Bearer ${process.env.MOUSE_API_KEY}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    model: "jev-latest",
    model: "mouse-1",
    state,
    questions,
  }),
});

Or with @typesafe-ai/sdk / typesafe-sdk:

import { TypeSafeClient } from "@typesafe-ai/sdk";

const client = new TypeSafeClient({
  apiKey: process.env.MOUSE_API_KEY,
  baseURL: "https://api.mouse.dev",
  defaultModel: "mouse-1",
});

What stays identical vs what improves

• Identical: questions (choice, noul, score), structured instructions and criteria, answers.<id>.choice, answers.<id>.noul, answers.<id>.score, probabilities, legend, and the exact confidence formulas.
• Additional fields (ignored by existing SDKs): Each answer includes logits and mouse: { calibrated, p_top, abstain, confidence_winning, confidence_normalized } (noul answers also gain probabilities), and usage includes state_tokens, question_tokens, framing_tokens, cached_tokens, and cost_usd.
• Re-calibrating thresholds & shadow replay: Even though confidence uses the same formula over probabilities, mouse-1 and jev-1.13.0 have different underlying model weights. A threshold tuned on Jev does not transfer. Replay your existing Jev or OpenAI JSONL logs with apps/decide/scripts/shadow_migrate.py (MOUSE_API_KEY=sk_mouse_... python scripts/shadow_migrate.py logs.jsonl --calibrate) to inspect top-1 agreement rate, confidence shift, 10-bin ECE, and fit a temperature and an out-of-fold cutoff for mouse-1 via POST /v1/calibrations, then branch on mouse.abstain.
• Option order: If you saw answers flip when options were reordered, pass order_invariant: true and Mouse averages two passes server-side.

---

2. Migrating from OpenAI Decisions (POST /v1/decisions)

OpenAI's Decisions API uses the same three conceptual primitives under slightly different field names:

1. Change the base URL to https://api.mouse.dev (POST /v1/decisions is served) and "model": "gpt-6-luna" to "model": "mouse-1".
2. Send the request body unchanged. Mouse reads the OpenAI field names as aliases (input → state; the questions array keyed by name; predicate → noul; choices: [{ value, description }] and levels: [{ label, description }] → criteria). The canonical map form below is what the request becomes; you can convert at your own pace.
3. Read the Mouse response shape: answers is a map keyed by your question names, a predicate comes back as answers.<name>.noul (not answers[i].probability), and choice probabilities are a map (answers.<name>.probabilities[value]).
4. Remove answer.type === "refusal" branches—mouse-1 never refuses a valid schema and always returns a probability distribution over your options.

Before (OpenAI POST /v1/decisions)

{
  "model": "gpt-6-luna",
  "input": "I was charged twice for my order.",
  "questions": [
    {
      "type": "choice",
      "name": "department",
      "instructions": "Which department should handle this complaint?",
      "choices": [
        { "value": "billing", "description": "Payments, invoices, and refunds." },
        { "value": "technical", "description": "Problems using the product." },
        { "value": "shipping", "description": "Delivery and tracking." },
        { "value": "other", "description": "Requests outside these categories." }
      ]
    },
    {
      "type": "predicate",
      "name": "refund_requested",
      "instructions": "Does the customer ask for a refund or report a duplicate charge?"
    }
  ]
}

After (Mouse POST /v1/decisions)

{
  "model": "mouse-1",
  "state": "I was charged twice for my order.",
  "questions": {
    "department": {
      "type": "choice",
      "instructions": "Which department should handle this complaint?",
      "criteria": {
        "billing": "Payments, invoices, and refunds.",
        "technical": "Problems using the product.",
        "shipping": "Delivery and tracking.",
        "other": "Requests outside these categories."
      }
    },
    "refund_requested": {
      "type": "noul",
      "instructions": "Does the customer ask for a refund or report a duplicate charge?"
    }
  }
}

---

3. Migrating from generative LLM classifiers (chat/completions / responses)

If you currently prompt a chat model (claude-haiku-4-5, gpt-5.6, gemini-flash) with a JSON schema or forced tool call to classify intents, gate actions, or score outputs:

1. Move your dynamic prompt variables into state as a clean JSON object (no prompt template boilerplate needed).
2. Turn each boolean schema field into a noul question, each enum field into a choice question, and each 1–5 rating field into a score question with concrete level descriptions.
3. Delete your JSON parser and retry-on-malformed-output loop: mouse-1 evaluates all questions in one forward pass (~40–100 ms) with $0 output token cost and returns calibrated probabilities for every option.