Quickstart

Set up your API key

Create a Fastino account
Get your API key from Settings

Set your key as an environment variable so the examples below work as-is:

export FASTINO_API_KEY="your_api_key_here"

setx FASTINO_API_KEY "your_api_key_here"

Run your first inference call

Call POST /v1/systemone with a model, a state describing the situation, and one or more typed questions. GLiDE returns calibrated probabilities and labels directly - there’s no generated text to parse. The example below asks a single yes/no (noul) question. GLiDE costs $0.15 per 1 million input tokens. Output tokens are free ($0). See Pricing for the current inference rates.

cURL
Python
JavaScript

To run a GLiDE decision call:

curl -X POST "https://api.fastino.ai/v1/systemone" \
  -H "X-API-Key: $FASTINO_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "fastino/GLiDE",
    "state": "Refund request: the receipt is attached, the purchase was 10 days ago, and refunds are allowed within 30 days.",
    "questions": {
      "refund_allowed": {
        "type": "noul",
        "instructions": "Does this request qualify for a refund?",
        "criteria": { "true": "Qualifies", "false": "Does not qualify" }
      }
    }
  }'

To run a GLiDE decision call:

import os
import requests

response = requests.post(
    "https://api.fastino.ai/v1/systemone",
    headers={
        "X-API-Key": os.environ["FASTINO_API_KEY"],
        "Content-Type": "application/json",
    },
    json={
        "model": "fastino/GLiDE",
        "state": "Refund request: the receipt is attached, the purchase was 10 days ago, and refunds are allowed within 30 days.",
        "questions": {
            "refund_allowed": {
                "type": "noul",
                "instructions": "Does this request qualify for a refund?",
                "criteria": {"true": "Qualifies", "false": "Does not qualify"},
            }
        },
    },
)

response.raise_for_status()
print(response.json())

To run a GLiDE decision call:

const response = await fetch(
  "https://api.fastino.ai/v1/systemone",
  {
    method: "POST",
    headers: {
      "X-API-Key": process.env.FASTINO_API_KEY,
      "Content-Type": "application/json",
    },
    body: JSON.stringify({
      model: "fastino/GLiDE",
      state: "Refund request: the receipt is attached, the purchase was 10 days ago, and refunds are allowed within 30 days.",
      questions: {
        refund_allowed: {
          type: "noul",
          instructions: "Does this request qualify for a refund?",
          criteria: { true: "Qualifies", false: "Does not qualify" },
        },
      },
    }),
  }
);

if (!response.ok) {
  throw new Error(`Inference failed: ${response.status} ${await response.text()}`);
}

const result = await response.json();
console.log(JSON.stringify(result, null, 2));

Success looks like this:

{
  "model": "glide",
  "answers": {
    "refund_allowed": {
      "type": "noul",
      "noul": 0.99767683794902,
      "confidence": 0.9953536758980399
    }
  },
  "usage": { "input_tokens": 89, "output_tokens": 1 },
  "token_usage": 90
}

answers.refund_allowed.noul is the probability the answer is “yes” - 0.999 is a strong qualifying signal. Continue with the Noul, Choice, and Score references, or use the routing guide to turn confidence into an action.

Extracting entities instead? Use GLiNER 2.5

If you need span-level extraction (NER, classification, structured records) rather than a decision, call POST /v1/chat/completions with a model, a message, and a schema that defines what you want to extract:

curl -X POST "https://api.fastino.ai/v1/chat/completions" \
  -H "Authorization: Bearer $FASTINO_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "fastino/gliner2.5-multi-v1",
    "messages": [
      {
        "role": "user",
        "content": "Apple announced the MacBook Pro at WWDC in Cupertino."
      }
    ],
    "schema": {
      "entities": [
        { "name": "organization", "description": "business or institution name" },
        { "name": "product", "description": "named commercial product" },
        { "name": "event", "description": "named conference or event" },
        { "name": "location", "description": "city, region, or place" }
      ]
    },
    "threshold": 0.5
  }'

For encoder models (GLiNER), the schema object defines the task and can include entities, classifications, structures, and relations. For decoder models (LLMs), omit schema and send a normal chat message. See the Inference API for the full contract, Python/JavaScript examples, and error codes.

Next steps

Decision Models

Understand decision models and continue with either GLiDE or GLiNER 2.5 Decide.

GLiDE agent skill

Use this skill to help your coding agent classify, route, and score decisions with GLiDE.

GLiDE inference

Review the complete /v1/systemone request, response, limits, retries, and errors.

Training API

Learn how to use our training API to fine-tune a GLiNER model end-to-end.

Get Started

Decision Models

Entity & Classification

Inference API

Training API

Troubleshooting

SDK Integrations

Models

Account

Set up your API key

Run your first inference call

Next steps

Decision Models

GLiDE agent skill

GLiDE inference

Training API

​Set up your API key

​Run your first inference call

​Next steps

Decision Models

GLiDE agent skill

GLiDE inference

Training API

Set up your API key

Run your first inference call

Next steps