Anthropic

Overview

Anthropic is an AI research and product company founded by former OpenAI researchers. The company is best known for its strong emphasis on AI safety, interpretability, and long-term alignment. Anthropic describes its mission as building “reliable, interpretable, and steerable” AI systems that can be safely deployed at scale.

Its product lineup spans lightweight Haiku models for fast inference, Sonnet models aimed at general-purpose production workloads, and Opus models designed for advanced reasoning, coding, and research tasks. Beyond chat assistants, Anthropic is actively expanding into agentic tooling with products such as Claude Code and enterprise integrations focused on automation, software engineering, and organizational workflows.

---

The chat models from this provider have some unique characteristics. Models from Anthropic can be accessed not only via the standard /v1/chat/completions endpoint but also through dedicated endpoints — /messages and /v1/batches. The sections below describe their API schemas, usage specifics, and example requests.

Supported capabilities:

• Text completions: Build advanced chat bots or text processors.

• Function Calling: Utilize tools for specific tasks and API calling.

• Stream mode: Get the text chat model responses as they are generated, rather than waiting for the entire response to be completed.

• Batch Processing: Send multiple independent requests in a single API call.

• Vision Tasks: Process and analyze images.

Text Completions

Ask something and get an answer in a chat-like conversation format.

post

Body

application/json

or

Responses

200Success

application/json

post

/v1/messages

cURLJavaScriptPython

curl -L \
  --request POST \
  --url 'https://api.aimlapi.com/v1/messages' \
  --header 'Authorization: Bearer <YOUR_AIMLAPI_KEY>' \
  --header 'Content-Type: application/json' \
  --data '{
    "model": "claude-sonnet-4-20250514",
    "messages": [
      {
        "role": "user",
        "content": "Hello"
      }
    ]
  }'

200Success

{
  "model": "claude-sonnet-4-20250514",
  "id": "msg_01SUmNmSRFZsoa6h96MxJEHH",
  "type": "message",
  "role": "assistant",
  "content": [
    {
      "type": "text",
      "text": "I'm functioning well, thank you for asking! I'm ready to help you with any questions or tasks you might have. How can I assist you today?"
    }
  ],
  "stop_reason": "end_turn",
  "stop_sequence": "text",
  "stop_details": null,
  "usage": {
    "input_tokens": 27,
    "cache_creation_input_tokens": 0,
    "cache_read_input_tokens": 0,
    "cache_creation": {
      "ephemeral_5m_input_tokens": 0,
      "ephemeral_1h_input_tokens": 0
    },
    "output_tokens": 35,
    "service_tier": "standard",
    "inference_geo": "not_available"
  },
  "meta": {
    "usage": {
      "credits_used": 1576,
      "usd_spent": 0.000788
    }
  }
}

Example: Simple Text Response

Python

import requests
import json   # for getting a structured output with indentation

url = "https://api.aimlapi.com/messages"
headers = {
    # Insert your AIML API Key instead of <YOUR_AIMLAPI_KEY>: 
    "Authorization": "Bearer <YOUR_AIMLAPI_KEY>",
    "Content-Type": "application/json"
}
payload = {
  "model": "claude-sonnet-4-20250514",
  "max_tokens": 1024,
  "system": "You are a robot. You always optimize for clarity, structure, and accuracy.",
  "messages": [
    {
      "role": "user",
      "content": "How are you?"
    }
  ]
}
response = requests.post(url, json=payload, headers=headers)
data = response.json()
print(json.dumps(data, indent=2, ensure_ascii=False))

Response

{
  "model": "claude-sonnet-4-20250514",
  "id": "msg_01SUmNmSRFZsoa6h96MxJEHH",
  "type": "message",
  "role": "assistant",
  "content": [
    {
      "type": "text",
      "text": "I'm functioning well, thank you for asking! I'm ready to help you with any questions or tasks you might have. How can I assist you today?"
    }
  ],
  "stop_reason": "end_turn",
  "stop_sequence": null,
  "stop_details": null,
  "usage": {
    "input_tokens": 27,
    "cache_creation_input_tokens": 0,
    "cache_read_input_tokens": 0,
    "cache_creation": {
      "ephemeral_5m_input_tokens": 0,
      "ephemeral_1h_input_tokens": 0
    },
    "output_tokens": 35,
    "service_tier": "standard",
    "inference_geo": "not_available"
  },
  "meta": {
    "usage": {
      "credits_used": 1576,
      "usd_spent": 0.000788
    }
  }
}

---

Function Calling

To process text and use function calling, follow the examples below:

Example: Get Weather Information

import requests

url = "https://api.aimlapi.com/messages"
headers = {
    "Authorization": "Bearer YOUR_AIMLAPI_KEY",
    "Content-Type": "application/json"
}
payload = {
  "model": "anthropic/claude-sonnet-4.5",
  "max_tokens": 1024,
  "tools": [
    {
      "name": "get_weather",
      "description": "Get the current weather in a given location",
      "input_schema": {
        "type": "object",
        "properties": {
          "location": {
            "type": "string",
            "description": "The city and state, e.g. San Francisco, CA"
          }
        }
      }
    }
  ],
  "messages": [
    {
      "role": "user",
      "content": "What is the weather like in San Francisco?"
    }
  ]
}
response = requests.post(url, json=payload, headers=headers)
print(response.json())

---

Streaming Mode

To enable streaming of responses, set stream=True in your request payload.

import requests

url = "https://api.aimlapi.com/messages"
headers = {
    "Authorization": "Bearer YOUR_AIMLAPI_KEY",
    "Content-Type": "application/json"
}
payload = {
  "model": "anthropic/claude-sonnet-4.5",
  "max_tokens": 1024,
  "tools": [
    {
      "name": "get_weather",
      "description": "Get the current weather in a given location",
      "input_schema": {
        "type": "object",
        "properties": {
          "location": {
            "type": "string",
            "description": "The city and state, e.g. San Francisco, CA"
          }
        }
      }
    }
  ],
  "messages": [
    {
      "role": "user",
      "content": "What is the weather like in San Francisco?"
    }
  ]

---

Batch Processing

Due to the complexity of its description, this capability has been placed on a separate page.

---

Vision

Note: API only support Base64 string as image input.

Possible media types:

• image/jpeg

• image/png

• image/gif

• image/webp

import httpx
import base64
from openai import OpenAI

client = OpenAI(
    base_url='https://api.aimlapi.com',
    api_key='<YOUR_AIMLAPI_KEY>'    
)  

image_url = "https://upload.wikimedia.org/wikipedia/commons/a/a7/Camponotus_flavomarginatus_ant.jpg"
image_media_type = "image/jpeg"
image_data = base64.standard_b64encode(httpx.get(image_url).content).decode("utf-8")

response = client.chat.completions.create(
    model="anthropic/claude-sonnet-4.5",
    messages=[
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "source": {
                    "type": "base64",
                    "media_type": image_media_type,
                    "data": imag1_data,
                },
            },
            {
                "type": "text",
                "text": "Describe this image."
            }
        ],
    }
],
)
print(response)

---

Response Format

The responses from the AI/ML API for Anthropic models will typically include the generated text or results from the tool called. Here is an example response for a weather query:

{
  "model": "claude-sonnet-4-20250514",
  "id": "msg_014iMvypzB9GafRthc8CQHsR",
  "type": "message",
  "role": "assistant",
  "content": [
    {
      "type": "text",
      "text": "I'm doing well, thank you for asking! I'm here and ready to help with whatever you'd like to discuss or work on. How are you doing today?"
    }
  ],
  "stop_reason": "end_turn",
  "stop_sequence": null,
  "stop_details": null,
  "usage": {
    "input_tokens": 11,
    "cache_creation_input_tokens": 0,
    "cache_read_input_tokens": 0,
    "cache_creation": {
      "ephemeral_5m_input_tokens": 0,
      "ephemeral_1h_input_tokens": 0
    },
    "output_tokens": 37,
    "service_tier": "standard",
    "inference_geo": "not_available"
  },
  "meta": {
    "usage": {
      "credits_used": 1529,
      "usd_spent": 0.0007645
    }
  }
}