System One

Create System One decision

curl --request POST \
  --url https://api.zerogpu.ai/v1/systemone \
  --header 'Content-Type: application/json' \
  --header 'x-api-key: <api-key>' \
  --data '
{
  "model": "zlm-v2-iab-classify-edge-enriched",
  "state": "Apple announced a new MacBook powered by its latest M-series processor. Preorders open today.",
  "questions": {
    "category": {
      "type": "choice",
      "instructions": "Which category best describes this content?",
      "criteria": {
        "technology": "Technology and computing",
        "finance": "Personal finance",
        "travel": "Travel"
      }
    },
    "brand_safe": {
      "type": "noul",
      "instructions": "Is this content suitable for brand advertising?"
    },
    "commercial_intent": {
      "type": "score",
      "instructions": "How strong is the commercial intent?",
      "criteria": [
        "None",
        "Low",
        "Medium",
        "High"
      ]
    }
  }
}
'

import requests

url = "https://api.zerogpu.ai/v1/systemone"

payload = {
    "model": "zlm-v2-iab-classify-edge-enriched",
    "state": "Apple announced a new MacBook powered by its latest M-series processor. Preorders open today.",
    "questions": {
        "category": {
            "type": "choice",
            "instructions": "Which category best describes this content?",
            "criteria": {
                "technology": "Technology and computing",
                "finance": "Personal finance",
                "travel": "Travel"
            }
        },
        "brand_safe": {
            "type": "noul",
            "instructions": "Is this content suitable for brand advertising?"
        },
        "commercial_intent": {
            "type": "score",
            "instructions": "How strong is the commercial intent?",
            "criteria": ["None", "Low", "Medium", "High"]
        }
    }
}
headers = {
    "x-api-key": "<api-key>",
    "Content-Type": "application/json"
}

response = requests.post(url, json=payload, headers=headers)

print(response.text)

const options = {
  method: 'POST',
  headers: {'x-api-key': '<api-key>', 'Content-Type': 'application/json'},
  body: JSON.stringify({
    model: 'zlm-v2-iab-classify-edge-enriched',
    state: 'Apple announced a new MacBook powered by its latest M-series processor. Preorders open today.',
    questions: {
      category: {
        type: 'choice',
        instructions: 'Which category best describes this content?',
        criteria: {
          technology: 'Technology and computing',
          finance: 'Personal finance',
          travel: 'Travel'
        }
      },
      brand_safe: {type: 'noul', instructions: 'Is this content suitable for brand advertising?'},
      commercial_intent: {
        type: 'score',
        instructions: 'How strong is the commercial intent?',
        criteria: ['None', 'Low', 'Medium', 'High']
      }
    }
  })
};

fetch('https://api.zerogpu.ai/v1/systemone', options)
  .then(res => res.json())
  .then(res => console.log(res))
  .catch(err => console.error(err));

package main

import (
	"fmt"
	"strings"
	"net/http"
	"io"
)

func main() {

	url := "https://api.zerogpu.ai/v1/systemone"

	payload := strings.NewReader("{\n  \"model\": \"zlm-v2-iab-classify-edge-enriched\",\n  \"state\": \"Apple announced a new MacBook powered by its latest M-series processor. Preorders open today.\",\n  \"questions\": {\n    \"category\": {\n      \"type\": \"choice\",\n      \"instructions\": \"Which category best describes this content?\",\n      \"criteria\": {\n        \"technology\": \"Technology and computing\",\n        \"finance\": \"Personal finance\",\n        \"travel\": \"Travel\"\n      }\n    },\n    \"brand_safe\": {\n      \"type\": \"noul\",\n      \"instructions\": \"Is this content suitable for brand advertising?\"\n    },\n    \"commercial_intent\": {\n      \"type\": \"score\",\n      \"instructions\": \"How strong is the commercial intent?\",\n      \"criteria\": [\n        \"None\",\n        \"Low\",\n        \"Medium\",\n        \"High\"\n      ]\n    }\n  }\n}")

	req, _ := http.NewRequest("POST", url, payload)

	req.Header.Add("x-api-key", "<api-key>")
	req.Header.Add("Content-Type", "application/json")

	res, _ := http.DefaultClient.Do(req)

	defer res.Body.Close()
	body, _ := io.ReadAll(res.Body)

	fmt.Println(string(body))

}

require 'uri'
require 'net/http'

url = URI("https://api.zerogpu.ai/v1/systemone")

http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true

request = Net::HTTP::Post.new(url)
request["x-api-key"] = '<api-key>'
request["Content-Type"] = 'application/json'
request.body = "{\n  \"model\": \"zlm-v2-iab-classify-edge-enriched\",\n  \"state\": \"Apple announced a new MacBook powered by its latest M-series processor. Preorders open today.\",\n  \"questions\": {\n    \"category\": {\n      \"type\": \"choice\",\n      \"instructions\": \"Which category best describes this content?\",\n      \"criteria\": {\n        \"technology\": \"Technology and computing\",\n        \"finance\": \"Personal finance\",\n        \"travel\": \"Travel\"\n      }\n    },\n    \"brand_safe\": {\n      \"type\": \"noul\",\n      \"instructions\": \"Is this content suitable for brand advertising?\"\n    },\n    \"commercial_intent\": {\n      \"type\": \"score\",\n      \"instructions\": \"How strong is the commercial intent?\",\n      \"criteria\": [\n        \"None\",\n        \"Low\",\n        \"Medium\",\n        \"High\"\n      ]\n    }\n  }\n}"

response = http.request(request)
puts response.read_body

{
  "model": "zlm-v2-iab-classify-edge-enriched",
  "answers": {
    "category": {
      "type": "choice",
      "choice": "technology",
      "confidence": 0.95,
      "probabilities": {
        "technology": 0.95,
        "finance": 0.03,
        "travel": 0.02
      }
    },
    "brand_safe": {
      "type": "noul",
      "noul": 0.98
    },
    "commercial_intent": {
      "type": "score",
      "score": 2.64,
      "confidence": 0.7,
      "legend": {
        "0": "None",
        "1": "Low",
        "2": "Medium",
        "3": "High"
      },
      "probabilities": {
        "0": 0.01,
        "1": 0.04,
        "2": 0.25,
        "3": 0.7
      }
    }
  },
  "usage": {
    "input_tokens": 120,
    "output_tokens": 0,
    "total_tokens": 120
  }
}

{
  "error": {
    "code": "insufficient_quota",
    "message": "You have insufficient quota to complete this request."
  }
}

{
  "error": {
    "code": "insufficient_quota",
    "message": "You have insufficient quota to complete this request."
  }
}

{
  "error": {
    "code": "insufficient_quota",
    "message": "You have insufficient quota to complete this request."
  }
}

{
  "detail": [
    {
      "loc": [
        "<string>"
      ],
      "msg": "<string>",
      "type": "<string>"
    }
  ]
}

{
  "error": {
    "code": "insufficient_quota",
    "message": "You have insufficient quota to complete this request."
  }
}

POST

systemone

Create System One decision

curl --request POST \
  --url https://api.zerogpu.ai/v1/systemone \
  --header 'Content-Type: application/json' \
  --header 'x-api-key: <api-key>' \
  --data '
{
  "model": "zlm-v2-iab-classify-edge-enriched",
  "state": "Apple announced a new MacBook powered by its latest M-series processor. Preorders open today.",
  "questions": {
    "category": {
      "type": "choice",
      "instructions": "Which category best describes this content?",
      "criteria": {
        "technology": "Technology and computing",
        "finance": "Personal finance",
        "travel": "Travel"
      }
    },
    "brand_safe": {
      "type": "noul",
      "instructions": "Is this content suitable for brand advertising?"
    },
    "commercial_intent": {
      "type": "score",
      "instructions": "How strong is the commercial intent?",
      "criteria": [
        "None",
        "Low",
        "Medium",
        "High"
      ]
    }
  }
}
'

import requests

url = "https://api.zerogpu.ai/v1/systemone"

payload = {
    "model": "zlm-v2-iab-classify-edge-enriched",
    "state": "Apple announced a new MacBook powered by its latest M-series processor. Preorders open today.",
    "questions": {
        "category": {
            "type": "choice",
            "instructions": "Which category best describes this content?",
            "criteria": {
                "technology": "Technology and computing",
                "finance": "Personal finance",
                "travel": "Travel"
            }
        },
        "brand_safe": {
            "type": "noul",
            "instructions": "Is this content suitable for brand advertising?"
        },
        "commercial_intent": {
            "type": "score",
            "instructions": "How strong is the commercial intent?",
            "criteria": ["None", "Low", "Medium", "High"]
        }
    }
}
headers = {
    "x-api-key": "<api-key>",
    "Content-Type": "application/json"
}

response = requests.post(url, json=payload, headers=headers)

print(response.text)

const options = {
  method: 'POST',
  headers: {'x-api-key': '<api-key>', 'Content-Type': 'application/json'},
  body: JSON.stringify({
    model: 'zlm-v2-iab-classify-edge-enriched',
    state: 'Apple announced a new MacBook powered by its latest M-series processor. Preorders open today.',
    questions: {
      category: {
        type: 'choice',
        instructions: 'Which category best describes this content?',
        criteria: {
          technology: 'Technology and computing',
          finance: 'Personal finance',
          travel: 'Travel'
        }
      },
      brand_safe: {type: 'noul', instructions: 'Is this content suitable for brand advertising?'},
      commercial_intent: {
        type: 'score',
        instructions: 'How strong is the commercial intent?',
        criteria: ['None', 'Low', 'Medium', 'High']
      }
    }
  })
};

fetch('https://api.zerogpu.ai/v1/systemone', options)
  .then(res => res.json())
  .then(res => console.log(res))
  .catch(err => console.error(err));

package main

import (
	"fmt"
	"strings"
	"net/http"
	"io"
)

func main() {

	url := "https://api.zerogpu.ai/v1/systemone"

	payload := strings.NewReader("{\n  \"model\": \"zlm-v2-iab-classify-edge-enriched\",\n  \"state\": \"Apple announced a new MacBook powered by its latest M-series processor. Preorders open today.\",\n  \"questions\": {\n    \"category\": {\n      \"type\": \"choice\",\n      \"instructions\": \"Which category best describes this content?\",\n      \"criteria\": {\n        \"technology\": \"Technology and computing\",\n        \"finance\": \"Personal finance\",\n        \"travel\": \"Travel\"\n      }\n    },\n    \"brand_safe\": {\n      \"type\": \"noul\",\n      \"instructions\": \"Is this content suitable for brand advertising?\"\n    },\n    \"commercial_intent\": {\n      \"type\": \"score\",\n      \"instructions\": \"How strong is the commercial intent?\",\n      \"criteria\": [\n        \"None\",\n        \"Low\",\n        \"Medium\",\n        \"High\"\n      ]\n    }\n  }\n}")

	req, _ := http.NewRequest("POST", url, payload)

	req.Header.Add("x-api-key", "<api-key>")
	req.Header.Add("Content-Type", "application/json")

	res, _ := http.DefaultClient.Do(req)

	defer res.Body.Close()
	body, _ := io.ReadAll(res.Body)

	fmt.Println(string(body))

}

require 'uri'
require 'net/http'

url = URI("https://api.zerogpu.ai/v1/systemone")

http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true

request = Net::HTTP::Post.new(url)
request["x-api-key"] = '<api-key>'
request["Content-Type"] = 'application/json'
request.body = "{\n  \"model\": \"zlm-v2-iab-classify-edge-enriched\",\n  \"state\": \"Apple announced a new MacBook powered by its latest M-series processor. Preorders open today.\",\n  \"questions\": {\n    \"category\": {\n      \"type\": \"choice\",\n      \"instructions\": \"Which category best describes this content?\",\n      \"criteria\": {\n        \"technology\": \"Technology and computing\",\n        \"finance\": \"Personal finance\",\n        \"travel\": \"Travel\"\n      }\n    },\n    \"brand_safe\": {\n      \"type\": \"noul\",\n      \"instructions\": \"Is this content suitable for brand advertising?\"\n    },\n    \"commercial_intent\": {\n      \"type\": \"score\",\n      \"instructions\": \"How strong is the commercial intent?\",\n      \"criteria\": [\n        \"None\",\n        \"Low\",\n        \"Medium\",\n        \"High\"\n      ]\n    }\n  }\n}"

response = http.request(request)
puts response.read_body

{
  "model": "zlm-v2-iab-classify-edge-enriched",
  "answers": {
    "category": {
      "type": "choice",
      "choice": "technology",
      "confidence": 0.95,
      "probabilities": {
        "technology": 0.95,
        "finance": 0.03,
        "travel": 0.02
      }
    },
    "brand_safe": {
      "type": "noul",
      "noul": 0.98
    },
    "commercial_intent": {
      "type": "score",
      "score": 2.64,
      "confidence": 0.7,
      "legend": {
        "0": "None",
        "1": "Low",
        "2": "Medium",
        "3": "High"
      },
      "probabilities": {
        "0": 0.01,
        "1": 0.04,
        "2": 0.25,
        "3": 0.7
      }
    }
  },
  "usage": {
    "input_tokens": 120,
    "output_tokens": 0,
    "total_tokens": 120
  }
}

{
  "error": {
    "code": "insufficient_quota",
    "message": "You have insufficient quota to complete this request."
  }
}

{
  "error": {
    "code": "insufficient_quota",
    "message": "You have insufficient quota to complete this request."
  }
}

{
  "error": {
    "code": "insufficient_quota",
    "message": "You have insufficient quota to complete this request."
  }
}

{
  "detail": [
    {
      "loc": [
        "<string>"
      ],
      "msg": "<string>",
      "type": "<string>"
    }
  ]
}

{
  "error": {
    "code": "insufficient_quota",
    "message": "You have insufficient quota to complete this request."
  }
}

Send the content as state and ask up to 16 named questions about it in one call. Each answer comes back under the question’s name, with calibrated probabilities. The request and response follow the TypeSafe System One decision schema, so an existing System One client works by changing the base URL and API key. System One is available on four models, in addition to their existing endpoints:

Model	Also served on	Good for
`zlm-v1-moderation-edge`	`/moderations`	Is this unsafe? Which moderation category applies? How severe is it?
`zlm-v1-iab-classify-edge`	`/chat/completions`, `/responses`	Which IAB category matches? Does it belong to category X? How strongly?
`zlm-v1-iab-domain-classifier`	`/chat/completions`, `/responses`	The same questions about a domain. `state` must be a URL or domain.
`zlm-v2-iab-classify-edge-enriched`	`/chat/completions`, `/responses`	Mixed category, brand-safety and commercial-intent questions in one request.

Question types

noul - a yes/no decision. The answer’s noul is the probability of yes: near 1 favors yes, near 0 favors no. Optional criteria.true and criteria.false describe what counts as each.
choice - pick one option. criteria maps each choice name to a description. The answer gives the choice, its confidence and the probabilities of every option.
score - rate against an ordered rubric. criteria is a list of levels, scored from 0. The answer’s score is the probability-weighted level and may fall between integers; legend maps each level number back to its text.

How answers are produced

Each model scores the content against its own labels: the 13 OpenAI moderation categories plus an overall unsafe score, or the IAB Content 2.2 and Audience 1.1 taxonomies. A question whose criteria name those labels is answered directly from the model’s scores. A category name covers its whole branch, so Technology and computing also counts Laptops. Brand-safety questions on the IAB models read the Sensitive Topics branch, and commercial-intent questions read the Purchase Intent audiences. A question whose criteria are not among the model’s labels is answered by zero-shot classification instead. You can mix both kinds in one request.

Limits and billing

Up to 16 questions per request, and up to 32 criteria per question.
state may be a string, an object or an array. An object or array is read as its JSON.
You are billed for input tokens only (the state plus the questions); output_tokens is always 0.
Request validation errors return 422 with System One’s { "detail": [{ "loc", "msg", "type" }] } body. Other errors use the standard error format.

Authorizations

x-api-key

string

header

required

Your ZeroGPU API key. Create one in the dashboard under API keys. Send it on every request.

Body

application/json

model

string

required

A model that supports System One: zlm-v1-moderation-edge, zlm-v1-iab-classify-edge, zlm-v1-iab-domain-classifier or zlm-v2-iab-classify-edge-enriched.

Example:

"zlm-v1-iab-classify-edge"

state

required

The content every question refers to: a string, object or array. An object or array is read as its JSON. For zlm-v1-iab-domain-classifier it must be a URL or domain.

questions

object

required

Named questions (up to 16). Keys are names you choose; each answer comes back under the same key.

Hide child attributes

questions.{key}

object

A yes/no (true/false) decision.

Option 1
Option 2
Option 3

Hide child attributes

questions.{key}.type

string

required

Allowed value: "noul"

questions.{key}.instructions

The statement or question to evaluate.

questions.{key}.criteria

object | null

Optional definitions of what counts as true and false.

Hide child attributes

questions.{key}.criteria.true

Free-form text: a string, or an object or array that is read as its JSON.

questions.{key}.criteria.false

Free-form text: a string, or an object or array that is read as its JSON.

Response

Success

model

string

required

The model that answered.

answers

object

required

Keyed by your question names, in request order.

Hide child attributes

answers.{key}

object

Option 1
Option 2
Option 3

Hide child attributes

answers.{key}.type

string

required

Allowed value: "noul"

answers.{key}.noul

number

required

Probability of true. Near 1 favors true, near 0 favors false, near 0.5 is uncertain.

Required range: 0 <= x <= 1

usage

object

required

Hide child attributes

usage.input_tokens

integer

required

Tokens billed: the state plus the questions.

usage.output_tokens

integer

required

Always 0; System One output is not billed.

usage.total_tokens

integer

required

Endpoints

Audio Models

ZeroClick

Batch API

Question types

How answers are produced

Limits and billing

Authorizations

Body

Response

​Question types

​How answers are produced

​Limits and billing

Authorizations

Body

Response

Question types

How answers are produced

Limits and billing