AI Detection

curl --request POST \
  --url https://text.external-api.pangram.com/task \
  --header 'Content-Type: application/json' \
  --header 'x-api-key: <api-key>' \
  --data '
{
  "text": "<string>"
}
'

import requests

url = "https://text.external-api.pangram.com/task"

payload = { "text": "<string>" }
headers = {
    "x-api-key": "<api-key>",
    "Content-Type": "application/json"
}

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

print(response.text)

{
  "task_id": "<string>",
  "stage": "<string>",
  "text": "<string>",
  "version": "<string>",
  "headline": "<string>",
  "prediction": "<string>",
  "prediction_short": "<string>",
  "fraction_ai": 123,
  "fraction_ai_assisted": 123,
  "fraction_human": 123,
  "num_ai_segments": 123,
  "num_ai_assisted_segments": 123,
  "num_human_segments": 123,
  "dashboard_link": "<string>",
  "windows": [
    {
      "text": "<string>",
      "label": "<string>",
      "ai_assistance_score": 123,
      "confidence": "<string>",
      "start_index": 123,
      "end_index": 123,
      "word_count": 123,
      "token_length": 123,
      "is_humanized": true,
      "humanizer_score": 123
    }
  ]
}

POST

task

AI Detection

curl --request POST \
  --url https://text.external-api.pangram.com/task \
  --header 'Content-Type: application/json' \
  --header 'x-api-key: <api-key>' \
  --data '
{
  "text": "<string>"
}
'

import requests

url = "https://text.external-api.pangram.com/task"

payload = { "text": "<string>" }
headers = {
    "x-api-key": "<api-key>",
    "Content-Type": "application/json"
}

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

print(response.text)

{
  "task_id": "<string>",
  "stage": "<string>",
  "text": "<string>",
  "version": "<string>",
  "headline": "<string>",
  "prediction": "<string>",
  "prediction_short": "<string>",
  "fraction_ai": 123,
  "fraction_ai_assisted": 123,
  "fraction_human": 123,
  "num_ai_segments": 123,
  "num_ai_assisted_segments": 123,
  "num_human_segments": 123,
  "dashboard_link": "<string>",
  "windows": [
    {
      "text": "<string>",
      "label": "<string>",
      "ai_assistance_score": 123,
      "confidence": "<string>",
      "start_index": 123,
      "end_index": 123,
      "word_count": 123,
      "token_length": 123,
      "is_humanized": true,
      "humanizer_score": 123
    }
  ]
}

For more information on billing units and pricing, check our pricing page for developers. Current

Current version

The current AI detection API creates an async task and returns a task ID. Poll the task endpoint until it reaches STAGE_SUCCESS or STAGE_FAILED.

Use the Bulk API when you need to analyze many texts as one asynchronous job. Use GET /models to discover the selectors available to your API key. The REST API temporarily accepts an omitted model for backward compatibility and resolves it to "default". New integrations should always send a selector explicitly.

POST /task

Create an async AI detection task.

POST https://text.external-api.pangram.com/task

Request

string

required

The input text to analyze.

string

default:"default"

A model selector returned by GET /models.

boolean

default:"false"

Whether to include a public dashboard link in the completed response.

Response

string

The ID of the async inference task.

Example

curl -X POST https://text.external-api.pangram.com/task \
  -H "Content-Type: application/json" \
  -H "x-api-key: your_api_key_here" \
  -d '{
    "text": "AI-assisted passage. Human passage.",
    "model": "pangram-4",
    "public_dashboard_link": false
  }'

from pangram import Pangram

pangram_client = Pangram()
result = pangram_client.predict(
    "AI-assisted passage. Human passage.",
    model="pangram-4",
)

Example Response

{
  "task_id": "123e4567-e89b-12d3-a456-426614174000"
}

Model selection errors

Status Code	Description
`403 Forbidden`	The requested model is not enabled for the API key.
`422 Unprocessable Entity`	The model selector is malformed or unknown.
`503 Service Unavailable`	The requested model is temporarily unavailable.

GET /task/

Fetch the current status or completed result for an AI detection task.

GET https://text.external-api.pangram.com/task/{task_id}

Request

string

required

The task ID returned by POST /task.

Response

string

The ID of the async inference task. Present while the task is in progress.

string

Current task stage. Terminal stages are STAGE_SUCCESS and STAGE_FAILED.

string

The analyzed text returned by the model. Pangram 4 may normalize the submitted text before inference; window offsets refer to this returned value. Present on success.

string

The API version identifier (e.g., "4.0" for Pangram 4). Present on success.

string

Classification headline summarizing the result. Present on success.

string

Long-form prediction string describing the classification. Present on success.

string

Short-form prediction string. Pangram 4 returns "AI", "Human", or "Mixed". Present on success.

float

Fraction of text classified as AI-written (0.0–1.0). Present on success.

float

Fraction of text classified as AI-assisted (0.0–1.0). Present on success.

float

Fraction of text classified as human-written (0.0–1.0). Present on success.

integer

Number of text segments classified as AI. Present on success.

integer

Number of text segments classified as AI-assisted. Present on success.

integer

Number of text segments classified as human. Present on success.

string

A link to the dashboard page containing the full classification result. Present on success when public_dashboard_link is true.

array

List of text segments () analyzed individually. Present on success.

Show Window object properties

string

The window text.

string

Descriptive classification label. Pangram 4 returns "AI-Generated", "AI-Assisted", or "Human Written".

float

detailing the level of AI assistance (0.0–1.0).

string

for the classification ("High", "Medium", "Low").

integer

Starting character index in the returned top-level text.

integer

End-exclusive character index in the returned top-level text.

integer

Number of words in the window.

integer

Token length of the window.

boolean

Whether Pangram 4’s humanizer head classified the window as humanized. Present on every Pangram 4 window.

float

Pangram 4 humanizer-head score from 0.0–1.0. Present on every Pangram 4 window.

Example

cURL

curl -X GET https://text.external-api.pangram.com/task/123e4567-e89b-12d3-a456-426614174000 \
  -H "x-api-key: your_api_key_here"

In-Progress Response

{
  "task_id": "123e4567-e89b-12d3-a456-426614174000",
  "stage": "STAGE_PREPROCESSING"
}

Success Response This example shows a Pangram 4 result. Pangram 4 returns version: "4.0", uses "AI-Assisted" instead of lightly or moderately assisted window labels, and keeps confidence as "Low", "Medium", or "High".

{
  "stage": "STAGE_SUCCESS",
  "text": "AI-assisted passage. Human passage.",
  "version": "4.0",
  "headline": "AI Assisted",
  "prediction": "We believe that this text is a mix of AI-assisted and human-written content.",
  "prediction_short": "Mixed",
  "fraction_ai": 0.0,
  "fraction_ai_assisted": 0.6,
  "fraction_human": 0.4,
  "num_ai_segments": 0,
  "num_ai_assisted_segments": 1,
  "num_human_segments": 1,
  "windows": [
    {
      "text": "AI-assisted passage. ",
      "label": "AI-Assisted",
      "ai_assistance_score": 0.55,
      "confidence": "High",
      "start_index": 0,
      "end_index": 21,
      "word_count": 2,
      "token_length": 5,
      "is_humanized": true,
      "humanizer_score": 0.91
    },
    {
      "text": "Human passage.",
      "label": "Human Written",
      "ai_assistance_score": 0.02,
      "confidence": "Medium",
      "start_index": 21,
      "end_index": 35,
      "word_count": 2,
      "token_length": 4,
      "is_humanized": false,
      "humanizer_score": 0.0
    }
  ]
}

Failed Response

{
  "stage": "STAGE_FAILED",
  "text": "",
  "version": "",
  "headline": "preprocessing: Input text contains no valid text after preprocessing",
  "prediction": "",
  "prediction_short": "",
  "fraction_ai": 0.0,
  "fraction_ai_assisted": 0.0,
  "fraction_human": 0.0,
  "num_ai_segments": 0,
  "num_ai_assisted_segments": 0,
  "num_human_segments": 0,
  "windows": []
}

​POST /task

​Request

​Response

​Example

​Model selection errors

​GET /task/

​Request

​Response

​Example

POST /task

Request

Response

Example

Model selection errors

GET /task/

Request

Response

Example