Decisions

Experimental. Jev support is new and the questions response format is not yet stable. The request and response shapes documented here may change in a future release without a deprecation period.

Jev is a decision model built by Typesafe. Instead of generating free-form text, it evaluates a piece of content against a set of questions you define and returns a typed answer for each one, together with a probability distribution over the possible outcomes. That makes it a good fit for classification, moderation, sentiment analysis, grading and any other place where you want a calibrated score rather than prose. Through Requesty you call Jev with the same Chat Completions, Messages or Responses API you already use for every other model. The only difference is the response format: you pass your questions in response_format (or its equivalent) and read the answers back as JSON in the assistant message.

Get your API key in the Requesty Console.

Models

Model	Notes
`typesafe/jev-latest`	Always points at the newest Jev release

How it works

A Jev request has two parts:

State: the content to evaluate. Send it as the text of one or more user messages. Several user messages are joined with blank lines.
Questions: a map of question id to question definition, passed as {"type": "questions", "questions": {...}} in the response format field of the API you are using.

The response is a single assistant message whose content is a JSON object with one entry per question id. Every answer carries a type field matching the question type.

Only the following are supported when calling Jev:

user messages with text content. system, assistant and tool messages, images, files and tool definitions are rejected.
Non-streaming requests. stream: true returns a 400.
A questions response format. json_object, json_schema and plain text are not supported by this model.

Question types

Jev supports three question types. You can mix them freely in one request; each question is answered independently.

`noul`: yes / no

A noul question asks whether a statement about the content is true. The answer is a probability between 0.0 and 1.0.

{
  "positive": {
    "type": "noul",
    "instructions": "The review is positive."
  }
}

Answer:

{
  "positive": {
    "type": "noul",
    "noul": 0.98
  }
}

`choice`: pick one option

A choice question picks one of several labelled options. criteria maps each option key to a description of when it applies. The answer contains the winning key in choice, plus probabilities for every option and the overall confidence.

{
  "sentiment": {
    "type": "choice",
    "instructions": "What is the sentiment of the review?",
    "criteria": {
      "positive": "The reviewer liked the book.",
      "negative": "The reviewer disliked the book.",
      "neutral": "The reviewer has no strong opinion."
    }
  }
}

Answer:

{
  "sentiment": {
    "type": "choice",
    "choice": "positive",
    "confidence": 0.97,
    "probabilities": {
      "positive": 0.97,
      "negative": 0.01,
      "neutral": 0.02
    }
  }
}

`score`: position on an ordered scale

A score question places the content on an ordered scale. criteria is a list of labels from lowest to highest. The answer is the zero-based index of the chosen label in score, with probabilities keyed by index, a legend mapping indices back to labels, and the overall confidence.

{
  "rating": {
    "type": "score",
    "instructions": "How much did the reviewer enjoy the book?",
    "criteria": ["Hated it", "Disliked it", "Neutral", "Liked it", "Loved it"]
  }
}

Answer:

{
  "rating": {
    "type": "score",
    "score": 4,
    "confidence": 0.91,
    "legend": {
      "0": "Hated it",
      "1": "Disliked it",
      "2": "Neutral",
      "3": "Liked it",
      "4": "Loved it"
    },
    "probabilities": {
      "0": 0.0,
      "1": 0.0,
      "2": 0.01,
      "3": 0.08,
      "4": 0.91
    }
  }
}

Examples

The examples below ask all three question types about the same book review. Swap in your own content and questions.

Chat Completions API

Pass the questions in response_format.

Python (OpenAI SDK)
TypeScript
cURL

import json
from openai import OpenAI

client = OpenAI(
    api_key="YOUR_REQUESTY_API_KEY",
    base_url="https://router.requesty.ai/v1",
)

review = (
    "This book was an absolute delight, I could not put it down "
    "and finished it in one sitting."
)

questions = {
    "positive": {
        "type": "noul",
        "instructions": "The review is positive.",
    },
    "sentiment": {
        "type": "choice",
        "instructions": "What is the sentiment of the review?",
        "criteria": {
            "positive": "The reviewer liked the book.",
            "negative": "The reviewer disliked the book.",
            "neutral": "The reviewer has no strong opinion.",
        },
    },
    "rating": {
        "type": "score",
        "instructions": "How much did the reviewer enjoy the book?",
        "criteria": ["Hated it", "Disliked it", "Neutral", "Liked it", "Loved it"],
    },
}

completion = client.chat.completions.create(
    model="typesafe/jev-latest",
    messages=[{"role": "user", "content": review}],
    response_format={"type": "questions", "questions": questions},
)

answers = json.loads(completion.choices[0].message.content)
print(answers["positive"]["noul"])       # 0.98
print(answers["sentiment"]["choice"])    # "positive"
print(answers["rating"]["score"])        # 4

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: "YOUR_REQUESTY_API_KEY",
  baseURL: "https://router.requesty.ai/v1",
});

const review =
  "This book was an absolute delight, I could not put it down and finished it in one sitting.";

const questions = {
  positive: {
    type: "noul",
    instructions: "The review is positive.",
  },
  sentiment: {
    type: "choice",
    instructions: "What is the sentiment of the review?",
    criteria: {
      positive: "The reviewer liked the book.",
      negative: "The reviewer disliked the book.",
      neutral: "The reviewer has no strong opinion.",
    },
  },
  rating: {
    type: "score",
    instructions: "How much did the reviewer enjoy the book?",
    criteria: ["Hated it", "Disliked it", "Neutral", "Liked it", "Loved it"],
  },
};

const completion = await client.chat.completions.create({
  model: "typesafe/jev-latest",
  messages: [{ role: "user", content: review }],
  // The SDK types do not know about "questions" yet, hence the cast
  response_format: { type: "questions", questions } as any,
});

const answers = JSON.parse(completion.choices[0].message.content!);
console.log(answers.positive.noul);     // 0.98
console.log(answers.sentiment.choice);  // "positive"
console.log(answers.rating.score);      // 4

curl https://router.requesty.ai/v1/chat/completions \
  -H "Authorization: Bearer YOUR_REQUESTY_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "typesafe/jev-latest",
    "messages": [
      {
        "role": "user",
        "content": "This book was an absolute delight, I could not put it down and finished it in one sitting."
      }
    ],
    "response_format": {
      "type": "questions",
      "questions": {
        "positive": {
          "type": "noul",
          "instructions": "The review is positive."
        },
        "sentiment": {
          "type": "choice",
          "instructions": "What is the sentiment of the review?",
          "criteria": {
            "positive": "The reviewer liked the book.",
            "negative": "The reviewer disliked the book.",
            "neutral": "The reviewer has no strong opinion."
          }
        },
        "rating": {
          "type": "score",
          "instructions": "How much did the reviewer enjoy the book?",
          "criteria": ["Hated it", "Disliked it", "Neutral", "Liked it", "Loved it"]
        }
      }
    }
  }'

The answers come back as the JSON content of the assistant message:

{
  "id": "req_...",
  "object": "chat.completion",
  "model": "typesafe/jev-latest",
  "choices": [
    {
      "index": 0,
      "finish_reason": "stop",
      "message": {
        "role": "assistant",
        "content": "{\"positive\":{\"type\":\"noul\",\"noul\":0.98},\"sentiment\":{\"type\":\"choice\",\"choice\":\"positive\",\"confidence\":0.97,\"probabilities\":{\"positive\":0.97,\"negative\":0.01,\"neutral\":0.02}},\"rating\":{\"type\":\"score\",\"score\":4,\"confidence\":0.91,\"legend\":{\"0\":\"Hated it\",\"1\":\"Disliked it\",\"2\":\"Neutral\",\"3\":\"Liked it\",\"4\":\"Loved it\"},\"probabilities\":{\"0\":0.0,\"1\":0.0,\"2\":0.01,\"3\":0.08,\"4\":0.91}}}"
      }
    }
  ],
  "usage": {
    "prompt_tokens": 448,
    "completion_tokens": 55,
    "total_tokens": 503
  }
}

Messages API

Pass the questions in output_config.format.

Python (Anthropic SDK)
TypeScript
cURL

import json
from anthropic import Anthropic

client = Anthropic(
    api_key="YOUR_REQUESTY_API_KEY",
    base_url="https://router.requesty.ai",
)

review = (
    "This book was an absolute delight, I could not put it down "
    "and finished it in one sitting."
)

questions = {
    "positive": {
        "type": "noul",
        "instructions": "The review is positive.",
    },
    "sentiment": {
        "type": "choice",
        "instructions": "What is the sentiment of the review?",
        "criteria": {
            "positive": "The reviewer liked the book.",
            "negative": "The reviewer disliked the book.",
            "neutral": "The reviewer has no strong opinion.",
        },
    },
    "rating": {
        "type": "score",
        "instructions": "How much did the reviewer enjoy the book?",
        "criteria": ["Hated it", "Disliked it", "Neutral", "Liked it", "Loved it"],
    },
}

message = client.messages.create(
    model="typesafe/jev-latest",
    max_tokens=1024,
    messages=[{"role": "user", "content": review}],
    output_config={
        "format": {"type": "questions", "questions": questions},
    },
)

answers = json.loads(message.content[0].text)
print(answers["sentiment"]["choice"])  # "positive"

import Anthropic from "@anthropic-ai/sdk";

const client = new Anthropic({
  apiKey: "YOUR_REQUESTY_API_KEY",
  baseURL: "https://router.requesty.ai",
});

const review =
  "This book was an absolute delight, I could not put it down and finished it in one sitting.";

const questions = {
  positive: {
    type: "noul",
    instructions: "The review is positive.",
  },
  sentiment: {
    type: "choice",
    instructions: "What is the sentiment of the review?",
    criteria: {
      positive: "The reviewer liked the book.",
      negative: "The reviewer disliked the book.",
      neutral: "The reviewer has no strong opinion.",
    },
  },
  rating: {
    type: "score",
    instructions: "How much did the reviewer enjoy the book?",
    criteria: ["Hated it", "Disliked it", "Neutral", "Liked it", "Loved it"],
  },
};

const message = await client.messages.create({
  model: "typesafe/jev-latest",
  max_tokens: 1024,
  messages: [{ role: "user", content: review }],
  // The SDK types do not know about "questions" yet, hence the cast
  output_config: { format: { type: "questions", questions } },
} as any);

const block = message.content[0];
const answers = JSON.parse(block.type === "text" ? block.text : "{}");
console.log(answers.sentiment.choice);  // "positive"

curl https://router.requesty.ai/v1/messages \
  -H "x-api-key: YOUR_REQUESTY_API_KEY" \
  -H "anthropic-version: 2023-06-01" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "typesafe/jev-latest",
    "max_tokens": 1024,
    "messages": [
      {
        "role": "user",
        "content": "This book was an absolute delight, I could not put it down and finished it in one sitting."
      }
    ],
    "output_config": {
      "format": {
        "type": "questions",
        "questions": {
          "positive": {
            "type": "noul",
            "instructions": "The review is positive."
          },
          "sentiment": {
            "type": "choice",
            "instructions": "What is the sentiment of the review?",
            "criteria": {
              "positive": "The reviewer liked the book.",
              "negative": "The reviewer disliked the book.",
              "neutral": "The reviewer has no strong opinion."
            }
          },
          "rating": {
            "type": "score",
            "instructions": "How much did the reviewer enjoy the book?",
            "criteria": ["Hated it", "Disliked it", "Neutral", "Liked it", "Loved it"]
          }
        }
      }
    }
  }'

Responses API

Pass the questions in text.format.

Python (OpenAI SDK)
TypeScript
cURL

import json
from openai import OpenAI

client = OpenAI(
    api_key="YOUR_REQUESTY_API_KEY",
    base_url="https://router.requesty.ai/v1",
)

review = (
    "This book was an absolute delight, I could not put it down "
    "and finished it in one sitting."
)

questions = {
    "positive": {
        "type": "noul",
        "instructions": "The review is positive.",
    },
    "sentiment": {
        "type": "choice",
        "instructions": "What is the sentiment of the review?",
        "criteria": {
            "positive": "The reviewer liked the book.",
            "negative": "The reviewer disliked the book.",
            "neutral": "The reviewer has no strong opinion.",
        },
    },
    "rating": {
        "type": "score",
        "instructions": "How much did the reviewer enjoy the book?",
        "criteria": ["Hated it", "Disliked it", "Neutral", "Liked it", "Loved it"],
    },
}

response = client.responses.create(
    model="typesafe/jev-latest",
    input=review,
    text={"format": {"type": "questions", "questions": questions}},
)

answers = json.loads(response.output_text)
print(answers["rating"]["score"])  # 4

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: "YOUR_REQUESTY_API_KEY",
  baseURL: "https://router.requesty.ai/v1",
});

const review =
  "This book was an absolute delight, I could not put it down and finished it in one sitting.";

const questions = {
  positive: {
    type: "noul",
    instructions: "The review is positive.",
  },
  sentiment: {
    type: "choice",
    instructions: "What is the sentiment of the review?",
    criteria: {
      positive: "The reviewer liked the book.",
      negative: "The reviewer disliked the book.",
      neutral: "The reviewer has no strong opinion.",
    },
  },
  rating: {
    type: "score",
    instructions: "How much did the reviewer enjoy the book?",
    criteria: ["Hated it", "Disliked it", "Neutral", "Liked it", "Loved it"],
  },
};

const response = await client.responses.create({
  model: "typesafe/jev-latest",
  input: review,
  // The SDK types do not know about "questions" yet, hence the cast
  text: { format: { type: "questions", questions } as any },
});

const answers = JSON.parse(response.output_text);
console.log(answers.rating.score);  // 4

curl https://router.requesty.ai/v1/responses \
  -H "Authorization: Bearer YOUR_REQUESTY_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "typesafe/jev-latest",
    "input": "This book was an absolute delight, I could not put it down and finished it in one sitting.",
    "text": {
      "format": {
        "type": "questions",
        "questions": {
          "positive": {
            "type": "noul",
            "instructions": "The review is positive."
          },
          "sentiment": {
            "type": "choice",
            "instructions": "What is the sentiment of the review?",
            "criteria": {
              "positive": "The reviewer liked the book.",
              "negative": "The reviewer disliked the book.",
              "neutral": "The reviewer has no strong opinion."
            }
          },
          "rating": {
            "type": "score",
            "instructions": "How much did the reviewer enjoy the book?",
            "criteria": ["Hated it", "Disliked it", "Neutral", "Liked it", "Loved it"]
          }
        }
      }
    }
  }'

Usage and billing

Jev responses report input_tokens and output_tokens in the usual usage block, and requests are priced and logged like any other model. See Cost Tracking and Logs.

Errors

Status	Cause
`400`	Missing or empty `questions`, a non-`questions` response format, `stream: true`, a non-user message, or non-text content
`429`	Upstream rate limit. Retry after the `Retry-After` interval

Structured Outputs for schema-constrained JSON from general-purpose models
Supported Models

Getting Started

LLM Gateway

Model Capabilities

Analytics & Monitoring

Access Control

Organization

MCP Gateway

Models

How it works

Question types

`noul`: yes / no

`choice`: pick one option

`score`: position on an ordered scale

Examples

Chat Completions API

Messages API

Responses API

Usage and billing

Errors

​Models

​How it works

​Question types

​noul: yes / no

​choice: pick one option

​score: position on an ordered scale

​Examples

​Chat Completions API

​Messages API

​Responses API

​Usage and billing

​Errors

​Related

Models

How it works

Question types

`noul`: yes / no

`choice`: pick one option

`score`: position on an ordered scale

Examples

Chat Completions API

Messages API

Responses API

Usage and billing

Errors

Related