Batch

Batch API Quickstart

This quickstart walks you through generating your first batch completions with Infron.

Create a message batch

Create a batch of messages for asynchronous processing. All usage is charged at 50% of the standard API prices.

A Batch is composed of a list of requests. The shape of an individual request is comprised of:

• A unique custom_id for identifying the Messages request

• A params object with the standard Messages API parameters

You can create a batch by passing this list into the requests parameter:

Python

import requests
import json

headers = {
    "Authorization": "Bearer <<API_KEY>>",
    "Content-Type": "application/json"
}

data = {
  "requests": [
    {
      "custom_id": "my-request-01",
      "params": {
        "model": "openai/gpt-4o-mini-batch",
        "max_tokens": 1024,
        "messages": [
          {
            "role": "user",
            "content": "How to learn nestjs?"
          }
        ],
        "metadata": {
          "ANY_ADDITIONAL_PROPERTY": "text"
        },
        "stop_sequences": [
          "text"
        ],
        "system": "text",
        "temperature": 1,
        "tool_choice": None,
        "tools": [],
        "top_k": 1,
        "top_p": 1,
        "thinking": {
          "budget_tokens": 1024,
          "type": "enabled"
        }
      }
    },
    {
      "custom_id": "my-request-02",
      "params": {
        "model": "openai/gpt-4o-mini-batch",
        "max_tokens": 1024,
        "messages": [
          {
            "role": "user",
            "content": "How to learn Reactjs?"
          }
        ],
        "metadata": {
          "ANY_ADDITIONAL_PROPERTY": "text"
        },
        "stop_sequences": [
          "text"
        ],
        "system": "text",
        "temperature": 1,
        "tool_choice": None,
        "tools": [],
        "top_k": 1,
        "top_p": 1,
        "thinking": {
          "budget_tokens": 1024,
          "type": "enabled"
        }
      }
    },
    {
      "custom_id": "my-request-03",
      "params": {
        "model": "openai/gpt-4o-mini-batch",
        "max_tokens": 1024,
        "messages": [
          {
            "role": "user",
            "content": "How to learn Nextjs?"
          }
        ],
        "metadata": {
          "ANY_ADDITIONAL_PROPERTY": "text"
        },
        "stop_sequences": [
          "text"
        ],
        "system": "text",
        "temperature": 1,
        "tool_choice": None,
        "tools": [],
        "top_k": 1,
        "top_p": 1,
        "thinking": {
          "budget_tokens": 1024,
          "type": "enabled"
        }
      }
    }
  ]
}

response = requests.post("https://llm.onerouter.pro/v1/batches", headers=headers, data=json.dumps(data))

data = response.json()
print("Batch created:", json.dumps(data, indent=2, ensure_ascii=False))

In this example, three separate requests are batched together for asynchronous processing. Each request has a unique custom_id and contains the standard parameters you'd use for a Messages API call.

{
  'batch': {
    'cancelled_at': None,
    'cancelling_at': None,
    'completed_at': None,
    'completion_window': '24h',
    'created_at': 1765972352,
    'endpoint': '',
    'error_file_id': '',
    'errors': None,
    'expired_at': None,
    'expires_at': 1766058749,
    'failed_at': None,
    'finalizing_at': None,
    'id': 'batch_a34c321b-ed4b-4e91-ae29-7f02939d8962',
    'in_progress_at': None,
    'input_file_id': 'file-142b17fbff7d4a06a88ec9205ae143c9',
    'metadata': None,
    'object': 'batch',
    'output_file_id': '',
    'request_counts': {
      'completed': 0,
      'failed': 0,
      'total': 0
    },
    'status': 'validating'
  },
  'batch_id': 'batch_a34c321b-ed4b-4e91-ae29-7f02939d8962',
  'file': {
    'bytes': 802,
    'created_at': 1765972347,
    'filename': 'batch.jsonl',
    'id': 'file-142b17fbff7d4a06a88ec9205ae143c9',
    'object': 'file',
    'purpose': 'batch',
    'status': 'processed'
  },
  'file_id': 'file-142b17fbff7d4a06a88ec9205ae143c9',
  'task_id': 2,
  'task_status': 'NOT_START'
}

Get status or results of a specific message batch

Get batch status if in progress, or stream results if completed in JSONL format.

Python

import requests
import json

# Insert your batch_id here
batch_id = "batch_a34c321b-ed4b-4e91-ae29-7f02939d8962"

headers = {
    "Authorization": "Bearer <<API_KEY>>",
    "Content-Type": "application/json"
}

response = requests.get("https://llm.onerouter.pro/v1/batches/{batch_id}", headers=headers)

print("Raw response:\n", response.text[:500])  

try:
    data = [json.loads(line) for line in response.text.splitlines() if line.strip()]
    print("\n✅ Parsed JSONL:")
    print(json.dumps(data, indent=2))
except json.JSONDecodeError:
    try:
        data = response.json()
        print("\n✅ Parsed JSON:")
        print(json.dumps(data, indent=2))
    except Exception as e:
        print("\n⚠️ Could not parse response:", e)

Cancel a specific batch

You can cancel a Batch that is currently processing using the cancel endpoint. Immediately after cancellation, a batch's processing_status will be canceling. Canceled batches end up with a status of ended and may contain partial results for requests that were processed before cancellation.

Python

import requests 
import json

batch_id = "batch_a34c321b-ed4b-4e91-ae29-7f02939d8962"
headers = { 
    "Authorization": "Bearer <<API_KEY>>", 
    "Content-Type": "application/json" 
}

response = requests.post(
    f"https://llm.onerouter.pro/v1/batches/{batch_id}/cancel", 
    headers=headers
)
if response.status_code == 200: 
    print("Batch canceled successfully:") 
    data = response.json() 
    print(json.dumps(data, indent=2, ensure_ascii=False)) 
else: 
    print(f"Failed to cancel batch ({response.status_code}):") 
    data = response.json() 
    print(json.dumps(data, indent=2, ensure_ascii=False))

Next steps

Overview