Claude Fable 5

This documentation is valid for the following list of our models:

• anthropic/claude-fable-5

• claude-fable-5

Try in Playground

Model Overview

Claude Fable 5 is Anthropic's most capable widely released model, built for demanding reasoning and long-horizon agentic work. It supports a 1M-token context window, up to 128k output tokens, vision, function calling, streaming, and adaptive thinking.

Create AI/ML API Key

How to make the first API call

1. Required setup (don't skip this)\

• Create an account: Sign up on the AI/ML API website if you don't have one yet.\

• Generate an API key: In your account dashboard, create an API key and make sure it's enabled in the UI.

2. Copy the code example At the bottom of this page, pick the snippet for your preferred programming language (Python or JavaScript) and copy it into your project.

3. Update the snippet for your use case\

• Insert your API key: Replace <YOUR_AIMLAPI_KEY> with your real AI/ML API key.\

• Select a model: Set the model field to the model you want to call.\

• Provide input: Fill in the messages field shown in the example.

4. (Optional) Tune the request You can add optional parameters to control the output. See the API schema below for the full list.

5. Run your code Run the updated code in your development environment. Response time depends on the model and request size, but simple requests typically return quickly.

For a detailed walkthrough of environment setup and API requests, see the Quickstart guide.

API Schema

post

Body

application/json

modelstring · enumRequiredPossible values:

messagesany ofRequired

A list of messages comprising the conversation so far. Depending on the model you use, different message types (modalities) are supported, like text, documents (txt, pdf), images, and audio.

or

stop_sequencesstring[]Optional

Custom text sequences that will cause the model to stop generating.

streambooleanOptional

If set to True, the model response data will be streamed to the client as it is generated using server-sent events.

Default: false

systemany ofOptional

A system prompt is a way of providing context and instructions to Claude, such as specifying a particular goal or role.

stringOptional

or

tool_choiceany ofOptional

Controls which (if any) tool is called by the model. none means the model will not call any tool and instead generates a message. auto means the model can pick between generating a message or calling one or more tools. required means the model must call one or more tools. Specifying a particular tool via {"type": "function", "function": {"name": "my_function"}} forces the model to call that tool. none is the default when no tools are present. auto is the default if tools are present.

or

or

or

or

or

string · enumOptional

none means the model will not call any tool and instead generates a message. auto means the model can pick between generating a message or calling one or more tools. required means the model must call one or more tools.

Possible values:

or

or

or

or

toolsany ofOptional

Definitions of tools that the model may use. If you include tools in your API request, the model may return tool_use content blocks that represent the model's use of those tools. You can then run those tools using the tool input generated by the model and then optionally return results back to the model using tool_result content blocks. Each tool definition includes: name: Name of the tool. description: Optional, but strongly-recommended description of the tool. input_schema: JSON schema for the tool input shape that the model will produce in tool_use output content blocks.

or

thinkingone ofOptional

Configuration for enabling Claude's extended thinking. When enabled, responses include thinking content blocks showing Claude's thinking process before the final answer. Requires a minimum budget of 1,024 tokens and counts towards your max_tokens limit.

or

or

max_tokensnumberOptional

The maximum number of tokens that can be generated in the chat completion. This value can be used to control costs for text generated via API.

Default: 128000

Responses

200Success

application/jsontext/event-stream

idstringRequired

A unique identifier for the chat completion.

Example: chatcmpl-CQ9FPg3osank0dx0k46Z53LTqtXMl

objectstring · enumRequired

The object type.

Example: chat.completionPossible values:

creatednumberRequired

The Unix timestamp (in seconds) of when the chat completion was created.

Example: 1762343744

modelstringRequired

The model used for the chat completion.

Example: gpt-4o-2024-08-06

post/v1/chat/completions

HTTPcURLJavaScriptPython

POST /v1/chat/completions HTTP/1.1
Host: api.aimlapi.com
Content-Type: application/json
Accept: */*
Content-Length: 697

{
  "model": "anthropic/claude-fable-5",
  "messages": [
    {
      "role": "user",
      "content": "text"
    }
  ],
  "metadata": {
    "ANY_ADDITIONAL_PROPERTY": "text"
  },
  "stop_sequences": [
    "text"
  ],
  "stream": false,
  "system": "text",
  "tool_choice": {
    "type": "auto",
    "disable_parallel_tool_use": true
  },
  "tools": [
    {
      "name": "text",
      "description": "text",
      "input_schema": {
        "type": "object",
        "properties": null,
        "required": [
          "text"
        ],
        "ANY_ADDITIONAL_PROPERTY": null
      },
      "type": "custom",
      "defer_loading": true,
      "eager_input_streaming": true,
      "input_examples": [
        {
          "ANY_ADDITIONAL_PROPERTY": null
        }
      ],
      "strict": true,
      "allowed_callers": [
        "direct"
      ],
      "cache_control": {
        "type": "ephemeral",
        "ttl": "5m"
      }
    }
  ],
  "thinking": {
    "type": "enabled",
    "budget_tokens": 1,
    "display": "summarized"
  },
  "max_tokens": 128000
}

200Success

{
  "id": "chatcmpl-CQ9FPg3osank0dx0k46Z53LTqtXMl",
  "object": "chat.completion",
  "created": 1762343744,
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "Hello! I'm just a program, so I don't have feelings, but I'm here and ready to help you. How can I assist you today?",
        "refusal": null,
        "annotations": null,
        "audio": null,
        "tool_calls": null
      },
      "finish_reason": "stop",
      "logprobs": null
    }
  ],
  "model": "gpt-4o-2024-08-06",
  "usage": {
    "prompt_tokens": 137,
    "completion_tokens": 914,
    "total_tokens": 1051,
    "completion_tokens_details": null,
    "prompt_tokens_details": null
  },
  "meta": {
    "usage": {
      "credits_used": 120000,
      "usd_spent": 0.06
    }
  }
}

Code Example

Python

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

response = requests.post(
    "https://api.aimlapi.com/v1/chat/completions",
    headers={
        # Insert your AIML API Key instead of <YOUR_AIMLAPI_KEY>:
        "Authorization": "Bearer <YOUR_AIMLAPI_KEY>",
        "Content-Type": "application/json"
    },
    json={
        "model": "anthropic/claude-fable-5",
        "messages": [
            {
                "role": "user",
                "content": "How are you?"  # insert your prompt
            }
        ]
    }
)

data = response.json()
print(json.dumps(data, indent=2, ensure_ascii=False))

JavaScript

Response

{
  "id": "chatcmpl-CQ9FPg3osank0dx0k46Z53LTqtXMl",
  "object": "chat.completion",
  "created": 1762343744,
  "model": "anthropic/claude-fable-5",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "I'm doing well, thank you! I'm Claude Fable 5, ready to help with whatever you need. What can I assist you with today?"
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 21,
    "completion_tokens": 31,
    "total_tokens": 52
  },
  "meta": {
    "usage": {
      "credits_used": 52000,
      "usd_spent": 0.00153
    }
  }
}