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AI/ML API Documentation

Quickstart

Documentation Map

Learn how to get started with the AI/ML API

This page helps you quickly find the right AI model for your task. Open the API reference and copy a working example to integrate it into your code in minutes.

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Select the model by its Task, by its Developer or by the supported Capabilities:

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Text Models (LLM)Image Models Video Models Music Models Voice/Speech Models 3D-Generating Models Vision Models Embedding Models

Alibaba Cloud: Text/Chat Image Video Text-to-Speech Embedding

Anthracite: Text/Chat

Anthropic: Text/Chat Embedding

Assembly AI: Speech-To-Text

Baidu: Text/Chat

ByteDance: Text/Chat Image Video

Cohere:

DeepSeek:

Deepgram:

ElevenLabs:

Flux:

Google:

Gryphe:

Hume AI:

Inworld:

Kling AI:

Krea:

LTXV:

Meta:

Microsoft:

MiniMax:

Mistral AI:

Moonshot:

NousResearch:

NVIDIA:

OpenAI:

Perplexity:

PixVerse:

RecraftAI:

Reve:

Runway:

Stability AI:

Sber AI:

Tencent:

VEED:

xAI:

Zhipu:

Going Deeper

Use more text model capabilities in your project: 📖

📖

Miscellaneous: 🔗

📗

⚠️

❓

Learn more about developer-specific features: 📖

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Quickstart

Access leading AI models (GPT-4o, Gemini, and others) through a single unified API. Initial setup takes just a few minutes.

If you are a manager and simply want to test a model to evaluate its performance, for instance in content generation, the quickest approach is to use our Playground. It offers an intuitive, user-friendly interface—no coding required.

Programmatic API calls are best suited for developers who want to integrate a model into their own apps.

Here, you'll learn how to start using our API in your code. The following steps must be completed regardless of which of our models you plan to call:

generating an AIML API Key,
choosing and preparing your development environment,
making an API call.

Let's walk through an example of connecting to the Gemma 3 model via REST API.

Generating an AIML API Key

What is an API Key?

You can find your AIML API key on the account page.

An AIML API key is a credential that grants you access to our API from your code. It is a sensitive string that is shown only at creation time and should be kept confidential. Do not share this key with anyone, as it could be misused without your knowledge. If you lose it, generate a new key from your dashboard.

⚠️ Note that API keys from third-party organizations cannot be used with our API: you need an AIML API Key.

To use the AIML API, you need to create an account and generate an AIML API key. Follow these steps:

Create an Account: Visit the AI/ML API website and create an account.
Generate an API Key: After logging in, navigate to your account dashboard and generate your API key. Ensure that key is enabled on UI.

Choosing the Development Environment

Each language has recommended environments for running code samples.

cURL

is a web-based REST client that lets you quickly run cURL requests directly in your browser, without installing any tools.
(Windows) or the built-in Terminal (macOS/Linux) allow you to run cURL examples and other command-line tools locally.

Python

is a popular online environment for running Python code and is the fastest option if you do not want to install anything locally.
(VS Code) is a lightweight and widely used code editor that supports both Python and Node.js. It is suitable for running and debugging local examples and for working on real projects.

JavaScript

(VS Code)

In the examples below for cURL, JavaScript and Python, we use the REST API. This approach works with all of our APIs, but it is not the only way to integrate. You can use other supported SDKs.

Making an API Call

Based on your environment, you will call our API differently. Below are three common ways to call our API using two popular languages: cURL (a command-line format for making HTTP requests rather than a programming language), Python, and JavaScript (NodeJS).

If you want to get started really quickly, choose one of the four expandable sections below. Each one contains instructions for calling our model using different tools and environments. The first two options are especially simple and suitable even for beginners.

For completeness, the same example is explained in detail in the Code Step-by-Step section.

curl -L \
  --request POST \
  --url 'https://api.aimlapi.com/v1/chat/completions' \
  --header 'Authorization: Bearer <YOUR_AIMLAPI_KEY>' \
  --header 'Content-Type: application/json' \
  --data '{
    "model": "google/gemma-3-4b-it",
    "messages": [
      {
        "role": "user",
        "content": "Tell me about San Francisco"
      }
    ],
    "temperature": 0.7,
    "max_tokens": 512
  }'

userPrompt = 'Tell me about San Francisco' // insert your request here

async function main() {
  const response = await fetch('https://api.aimlapi.com/v1/chat/completions', {
    method: 'POST',
    headers: {
      // Insert your AIML API Key instead of <YOUR_AIMLAPI_KEY>
      'Authorization': 'Bearer <YOUR_AIMLAPI_KEY>',
      'Content-Type': 'application/json',
    },
    body: JSON.stringify({
      model: 'google/gemma-3-4b-it',
      messages:[
          {
              role:'user',
              content: userPrompt
          }
      ],
      temperature: 0.7,
      max_tokens: 512,
    }),
  });

  const data = await response.json();
  const answer = data.choices[0].message.content;
  
  console.log('User:', userPrompt);
  console.log('AI:', answer);
}

main();

import requests 

user_prompt = "Tell me about San Francisco"  # insert your request here

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>"
    },
    json={
        "model":"google/gemma-3-4b-it",
        "messages":[  
            {
                "role":"user",
                "content": user_prompt
            }
        ],
        "temperature": 0.7,
        "max_tokens": 512,
    }
)

data = response.json()
answer = data["choices"][0]["message"]["content"]

print("User:", user_prompt)
print("AI:", answer)

⭐ How to run a cURL example in a web-based REST client (REQBIN)

Calling the API via cURL through a web service like this is the simplest and fastest method, requiring no additional libraries. However, there is a downside: cURL is not a programming language, which means it has very limited capabilities for adding logic—only API calls, no loops or conditions. You can’t even extract just the specific field with the model’s text response—cURL returns the model’s full output, as you’ll see below.

1. Copy the cURL example above and paste it into a text editor, such as Notepad or Notepad++.

2. Replace the placeholder <YOUR_AIMLAPI_KEY> with your actual AIMLAPI Key.

3. If needed, modify the prompt (the content field).

4. Copy the modified example, go to the website, paste it into the designated field and click Run:

5. After the model processes your request, the model’s full output will be shown directly below the input field.

Pro tip: try experimenting with the three different ways of displaying the model’s output. Some are more readable than others.

⭐ How to run a Python example in an online Jupyter Notebook

The second fastest option, and a much more convenient choice, while offering more flexibility for customizing how the output is displayed in code.

1. When you open Jupyter Notebook for the first time, select “Python 3.13 (XPython)” in the pop-up window to indicate the programming language kernel you will be working with:

In some browsers, the kernel selection may look different:

2. Enter the following command in the first cell to install the requests library:

Click the Run button in the toolbar above the cell to execute it:

3. Paste our example into the second cell, replace the placeholder with your AIMLAPI Key, then click the Run button in the toolbar:

4. After the model processes your request, the result will be shown directly below the cell:

How to run a Python example locally from the command line (without an IDE)

Let's start from very beginning. We assume you already installed Python (with venv), if not, here a guide for the beginners.

Create a new folder for test project, name it as aimlapi-welcome and change to it.

mkdir ./aimlapi-welcome
cd ./aimlapi-welcome

(Optional) If you use IDE then we recommend to open created folder as workspace. On example, in Visual Studio Code you can do it with:

code .

Run a terminal inside created folder and create virtual envorinment with a command:

Activate created virtual environment:

Install requirement dependencies. In our case (REST API SDK) we need only request library:

Create new file and name it as travel.py:

Paste following content inside this travel.py and replace <YOUR_AIMLAPI_KEY> with your API key you got on :

Run the application:

If you done all correct, you will see following output:

How to run a JavaScript example locally from the command line (without an IDE)

We assume you already have Node.js installed. If not, here is a guide for beginners.

Create a new folder for the example project:

mkdir ./aimlapi-welcome
cd ./aimlapi-welcome

Create a project file:

npm init -y

Create a file with the source code:

touch ./index.js

And paste the following content to the file and save it:

async function main() {
  const response = await fetch('https://api.aimlapi.com/v1/chat/completions', {
    method: 'POST',
    headers: {
      // Insert your AIML API Key instead of <YOUR_AIMLAPI_KEY>
      'Authorization': 'Bearer <YOUR_AIMLAPI_KEY>',
      'Content-Type': 'application/json',
    },
    body: JSON.stringify({
      model: 'google/gemma-3-4b-it',
      messages:[
          {
              role:'user',
              content: 'Tell me about San Francisco'  // Insert your prompt here
          }
      ],
      temperature: 0.7,
      max_tokens: 256,
    }),
  });

  const data = await response.json();
  console.log(JSON.stringify(data, null, 2));
}

main();

Run the file:

You will see a response that looks like this:

Code Step-by-Step

Below is a step-by-step explanation of the same API call in three variants: cURL, JavaScript, and Python. All three examples send an identical request to the google/gemma-3-4b-it chat model.

cURL

1. Command start

curl -L \

Runs the cURL HTTP client. The -L flag tells cURL to follow redirects (if any).

2. HTTP method

--request POST \

Specifies that the request uses the POST method.

3. Endpoint

The full endpoint URL used to call chat models.

4. Authorization header

Sends your AIMLAPI key in the Authorization header.

5. Content type

Indicates that the request body is JSON.

6. Request body

This is the payload sent to the API:

model – the model identifier.
messages – the chat history.
- role: "user" – the user message.

These are the input parameters used to tell the endpoint—which in this case generates text answers—what exactly we want it to produce.

With the parameters shown above, we are effectively asking the API to use the google/gemma-3-4b-it model and generate a reasonably vivid and engaging description of San Francisco, limited to roughly 300–350 words— with the temperature and max_tokens parameters controlling the creativity and approximate length of the output, respectively.

7. Response

In the cURL example, you receive the entire JSON response. No fields are extracted — cURL simply prints the raw output.

JavaScript (Node.js)

1. Define the user prompt

userPrompt = 'Tell me about San Francisco'

Stores the text of the user request.

2. Call the API

const response = await fetch(
  'https://api.aimlapi.com/v1/chat/completions',
  { ... }
);

Sends an HTTP request to the endpoint.

3. HTTP method

method: 'POST',

Specifies that the request uses the POST method.

4. Headers

Sends your AIMLAPI key in the Authorization header.
Indicates that the request body is JSON.

5. Request body

This is the payload sent to the API:

model – the model identifier.
messages – the chat history.
- role: "user" – the user message.

These are the input parameters used to tell the endpoint—which in this case generates text answers—what exactly we want it to produce.

6. Parse the response

Converts the API response into a JavaScript object.

7. Extract the model’s text output

Reads the text of the first generated message.

8. Print the result

Output formatting: from the model’s full response, only the generated text is extracted, and it is presented together with the original prompt in a dialogue-style format.

Python

1. Import the HTTP library

import requests

The requests library is used to send HTTP requests.

2. Define the user prompt

user_prompt = "Tell me about San Francisco"

Stores the text of the user query.

3. Call the API

Sends a POST request to the endpoint.

4. Headers

Sends your AIMLAPI key in the Authorization header.
Indicates that the request body is JSON.

5. Request body

This is the payload sent to the API:

model – the model identifier.
messages – the chat history.
- role: "user" – the user message.

These are the input parameters used to tell the endpoint—which in this case generates text answers—what exactly we want it to produce.

6. Parse the response

Converts the JSON response into a Python dictionary.

7. Extract the model’s text output

Reads the text of the first generated message.

8. Print the result

Output formatting: from the model’s full response, only the generated text is extracted, and it is presented together with the original prompt in a dialogue-style format.

Future Steps

OpenClaw AI/ML

About

OpenClaw is an AI platform for building AI agents and assistants. It runs on your own devices and connects to popular messaging platforms (such as WhatsApp, Telegram, Slack, Discord, and others) while preserving full data privacy (all agent data is stored locally in a SQLite database).

Developers use OpenClaw to build multi-channel AI assistants with streaming responses, browser automation, vision, and voice features. It includes a local Gateway service, a CLI for management, and support for 12+ messaging platforms.

Data privacy: OpenClaw stores data locally by default. Nothing is sent externally unless you configure it.

What you get

Multi-channel assistants and routing across 12+ messaging platforms
Streaming responses for faster, more interactive chats
Vision inputs for image understanding and UI analysis
Browser automation via an OpenClaw-managed Chrome instance

Prerequisites

An AIMLAPI key obtained from your
Node.js and npm
pnpm if you build from source

Installation

Option 1: Install via npm (recommended)

openclaw-aimlapi@latest includes two AI/ML API skills:

aimlapi-media-gen for images and video

The onboarding wizard installs the Gateway as a system service. It uses launchd on macOS and systemd on Linux.

Option 2: Build from source

UI walkthrough (screenshots)

Option 3: Install skills from the official repo (ClawHub)

Use this if you want to install or update skills separately from the OpenClaw package.

Install the CLI

Pick one:

For more details, see: .

Install the skills

How it fits into OpenClaw

By default, clawhub installs skills into ./skills under your current directory.
If an OpenClaw workspace is configured, clawhub falls back to that workspace.
Override the install location with --workdir

What these skills do

aiml-image-video — Our media generation models

Generate images and videos via two Python scripts (gen_image.py, gen_video.py).

aiml-llm-reasoning — Our LLMs + Reasoning

Run chat completions via run_chat.py. Use --extra-json for advanced params.

Paths above assume you run clawhub install ... from your OpenClaw workspace root (so skills land in ./skills). If you install somewhere else, adjust the paths to match your --workdir.

If you installed OpenClaw via openclaw-aimlapi@latest, you may already have AIML-related skills installed. Use ClawHub when you specifically want the skills from the official skills repository.

Configure AI/ML API in OpenClaw

Use the Web UI from onboarding. The default URL is usually .

Select provider

Pick AI/ML API in the providers list.

Use OpenClaw

Use via a chat connector (Telegram example)

1. Message your bot. You will receive a pairing code.

2. Approve the pairing:

Expected output looks like this:

3. Message your bot again. You should get a response.

Use via CLI

Example response

Use Cases

Example: Route Slack + Discord to the same agent

User messages the bot on Slack or Discord.
Gateway receives the message with platform context.

Example: Analyze a web page with vision

User requests a web page analysis.
OpenClaw opens a Chrome instance (CDP-controlled).

Supported models

OpenAI models (, , , , , and others)

Supported SDKs

A description of the software development kits (SDKs) that can be used to interact with the AIML API.

This page describes the SDKs that can be used to call our API.

Key Definitions & Notes

The REST API itself is not an SDK. It is the server-side interface that exposes your models over HTTP. It defines endpoints, HTTP methods (POST/GET), required headers, and the structure of request and response JSON. Essentially, it’s the “contract” the server provides for clients to interact with models programmatically.

An SDK (Software Development Kit) is a client-side library that wraps around the REST API. It handles details like building HTTP requests, serializing/deserializing JSON, error handling, retries, and sometimes additional conveniences.

You can skip the SDK and call the REST API directly via cURL, fetch, requests, etc. The SDK just makes your life easier; the REST API is the “core interface” the SDK talks to.

The following flow shows how a request travels from your code to the model and back. Using an SDK is optional — it simply wraps the REST API for convenience.

Your code → SDK (optional) → REST API → Model → REST API → SDK → Your code

Comparing requests made with raw REST API and different SDKs, pay attention to the following common aspects:

how the Authorization header and the AIML API key are provided,
how the POST method and the endpoint URL are specified,

Also take a look at the section — it covers many third-party services and libraries (workflow platforms, coding assistants, etc.) that allow you to integrate our models in various ways.

REST API

We use the REST API because it’s fast, simple, and easy to understand. Only in Python do you need to import a separate library (requests), while cURL and JavaScript (Node.js) already have built-in support for HTTP requests. Therefore, REST API is used in the documentation examples for all of our models.

Installation

In Python examples, you need to import the requests library. The Node.js and cURL examples do not require any additional imports.

Install the library first:

Import the library in every Python code snippet where you make calls to the REST API.

Authorization

Our API authorization is based on a Bearer token. Include it in the Authorization HTTP header within the request. Example:

Request Example

OpenAI

The OpenAI SDK is a convenient library that simplifies working with our API. It automatically handles JSON responses, includes built-in error handling and retry logic, and provides simple, easy-to-use methods for all API features such as chat, embeddings, and completions.

The AI features that the OpenAI SDK supports

Streaming
Completions

Installation

Python

1. Make sure you have Python 3.7+ and pip installed.

2. Install the OpenAI SDK via terminal or Jupyter Notebook:

In Jupyter Notebook, you can also use:

3. Import the SDK:

JavaScript (Node.js)

1. Make sure you have Node.js 18+ and npm installed.

2. Install the OpenAI SDK in your project:

3. Import the SDK and initialize the client:

Example Code

AI/ML API Python library

We have started developing our own SDK to simplify the use of our service.

If you’d like to contribute to expanding its functionality, feel free to reach out to us on !

Installation

After obtaining your AIML API key, create an .env file and copy the required contents into it.

Copy the code below, paste it into your .env file, and set your API key in AIML_API_KEY="<YOUR_AIMLAPI_KEY>", replacing <YOUR_AIMLAPI_KEY> with your actual key:

Install the package:

Request Example

To execute the script, use:

Next Steps

API REFERENCES

Service Endpoints

Account Balance

[legacy] Get account balance info

This endpoint is considered legacy and is scheduled for future deprecation. Please plan to migrate to the new /v2/billing and /v2/billing/detail endpoints documented below.

You can query your account balance and other billing details through this API. To make a request, you only need your AIMLAPI key obtained from your .

Get balance info

Returns a user's balance.

Get detailed billing info

Returns detailed billing information, balance and auto top-up settings.

API Key Management

POST https://api.aimlapi.com/v1/keys

GET https://api.aimlapi.com/v1/keys

GET https://api.aimlapi.com/v1/key

PATCH https://api.aimlapi.com/v1/keys/{prefix}

Before you start you should create .

Create a new API key

Copy the created key and store it in a secure location. If the key is lost, create a new one.

List API keys

Returns all API keys for your account, including each key’s settings and metadata.

Get the API key

Retrieve parameters of the AIMLAPI key used in the request.

Update an API key

Delete an API key

All Model IDs

A full list of available models.

If you need to select models based on specific parameters for your task, visit the , which offers convenient filtering options. On the selected model’s page, you can find detailed technical and commercial information.

To fetch the complete model list via the API, see for the relevant service endpoint.

Text Models (LLM)

Overview of the capabilities of AIML API text models (LLMs).

Specific Capabilities

There are several capabilities of text models that are worth mentioning separately.

Completion allows the model to analyze a given text fragment and predict how it might continue based on the probabilities of the next possible tokens or characters. Chat Completion extends this functionality, enabling a simulated dialogue between the user and the model based on predefined roles (e.g., "strict language teacher" and "student"). A detailed description and examples can be found in our article.

An evolution of chat completion includes Assistants

qwen-max

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

qwen-max
qwen-max-2025-01-25

Model Overview

The large-scale Mixture-of-Experts (MoE) language model. Excels in language understanding and task performance. Supports 29 languages, including Chinese, English, and Arabic.

How to make the first API call

1️⃣ 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.

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

3️ 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

API Schema

Code Example

Response

qwen-plus

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

qwen-plus

Model Overview

An advanced large language model. Multilingual support, including Chinese and English. Enhanced reasoning capabilities for complex tasks. Improved instruction-following abilities.

How to make the first API call

1️ 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.

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

3️ 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:

API Schema

Code Example

Response

qwen-turbo

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

qwen-turbo

Model Overview

This model is designed to enhance both the performance and efficiency of AI agents developed on the Alibaba Cloud Model Studio platform. Optimized for speed and precision in generative AI application development. Improves AI agent comprehension and adaptation to enterprise data, especially when integrated with Retrieval-Augmented Generation (RAG) architectures. Large context window (1,000,000 tokens).

How to make the first API call

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

3️ 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:

API Schema

Code Example

Response

qwen3-32b

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

alibaba/qwen3-32b

Model Overview

A world-class model with comparable quality to DeepSeek R1 while outperforming and . Optimized for both complex reasoning and efficient dialogue.

How to make the first API call

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

3️ 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:

API Schema

Code Example #1: Without Thinking and Streaming

enable_thinking must be set to false for non-streaming calls.

Response

Code Example #2: Enable Thinking and Streaming

Response

The example above prints the raw output of the model. The text is typically split into multiple chunks. While this is helpful for debugging, if your goal is to evaluate the model's reasoning and get a clean, human-readable response, you should aggregate both the reasoning and the final answer in a loop — for example:

Example with response parsing

After running such code, you'll receive only the model's textual output in a clear and structured format:

Response

qwen3-coder-480b-a35b-instruct

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

alibaba/qwen3-coder-480b-a35b-instruct

Model Overview

The most powerful model in the Qwen3 Coder series — a 480B-parameter MoE architecture with 35B active parameters. It natively supports a 256K token context and can handle up to 1M tokens using extrapolation techniques, delivering outstanding performance in both coding and agentic tasks.

How to make the first API call

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

3️ 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:

API Schema

Code Example

Response

qwen3-next-80b-a3b-instruct

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

alibaba/qwen3-next-80b-a3b-instruct

Model Overview

An instruction-tuned chat model optimized for fast, stable replies without reasoning traces, designed for complex tasks in reasoning, coding, knowledge QA, and multilingual use, with strong alignment and formatting.

How to make the first API call

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

3️ 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:

API Schema

Code Example

Response

qwen3-next-80b-a3b-thinking

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

alibaba/qwen3-next-80b-a3b-thinking

Model Overview

The model may take longer to generate reasoning content than its predecessor. Alibaba Cloud strongly recommends its use for highly complex reasoning tasks.

How to make the first API call

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

3️ 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:

API Schema

Code Example

Response

qwen3-max-preview

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

alibaba/qwen3-max-preview

Model Overview

The preview version of .

How to make the first API call

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

3️ 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:

API Schema

Code Example

Response

qwen3-max-instruct

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

alibaba/qwen3-max-instruct

Model Overview

This model offers improved accuracy in math, coding, logic, and science, handles complex instructions in Chinese and English more reliably, reduces hallucinations, supports 100+ languages with stronger translation and commonsense reasoning, and is optimized for RAG and tool use, though it lacks a dedicated ‘thinking’ mode.

How to make the first API call

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

3️ 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:

API Schema

Code Example

Response

qwen3-vl-32b-instruct

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

alibaba/qwen3-vl-32b-instruct

Model Overview

The most advanced vision-language model in the Qwen series as of October 2025 — a non-thinking-capable version of the model. Optimized for instruction-following in image description, visual dialogue, and content-generation tasks.

How to make the first API call

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

3️ Update the snippet for your use case ▪ Insert your API key: replace <YOUR_AIMLAPI_KEY> with your real AI/ML API key. ▪

API Schema

Code Example

Response

qwen3.5-plus

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

alibaba/qwen3.5-plus-20260218

Model Overview

A commercial large language model designed for long-context text generation and enterprise-grade conversational AI. Supports up to 1M tokens per request with production-ready API stability.

How to make the first API call

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

3️ 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

API Schema

Code Example

Response

qwen3.6-27b

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

alibaba/qwen3.6-27b

Model Overview

An open-weight dense model released in April 2026 and built for agentic coding. It delivers high performance, matches on Terminal-Bench 2.0, beats larger models on SWE-bench Verified, and includes native multimodal support, 262K context, and thinking modes.

How to make the first API call

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

3️ 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

API Schema

Code Example

Response

qwen3.5-omni-plus

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

alibaba/qwen3.5-omni-plus

Model Overview

A premium multimodal model with support for text, image, audio, and video inputs. Designed for complex tasks requiring advanced reasoning, speech generation, and high-quality outputs.

How to make the first API call

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

3️ 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

API Schema

Code example #1: Chat

Response

Code example #2: Video analysis

Response

qwen3.5-omni-flash

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

alibaba/qwen3.5-omni-flash

Model Overview

A fast and cost-efficient multimodal model supporting text, image, audio, and video inputs. A lighter and faster version of , built for low-latency workloads that need strong performance at scale.

How to make the first API call

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

3️ 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

API Schema

Code Example

Response

Code example #2: Video analysis

Response

Anthropic

The chat models from this provider have some unique characteristics. In addition to the standard v1/chat/completions endpoint, you can also call Anthropic models via the /messages endpoint. This capability is described in more detail in the Capabilities section.

Claude 4 Opus

This documentation is valid for the following model:

anthropic/claude-opus-4

As of February 13, 2026, the streaming response format for Anthropic models has changed.

Model Overview

The leading coding model globally, consistently excelling at complex, long-duration tasks and agent-based workflows.

How to Make a Call

Step-by-Step Instructions

1️ Setup You Can’t Skip

▪️ : Visit the AI/ML API website and create an account (if you don’t have one yet). ▪️ : After logging in, navigate to your account dashboard and generate your API key. Ensure that key is enabled on UI.

2️ Copy the code example

At the bottom of this page, you'll find that shows how to structure the request. Choose the code snippet in your preferred programming language and copy it into your development environment.

API Schema

Code Example #1

Response

Code Example #2: Streaming Mode

As of February 13, 2026, the streaming response format for Anthropic models has changed. Specifically, the usage fields were renamed as follows:

the state structure is no longer used,
input_tokens → prompt_tokens,
output_tokens

Response

Claude 4 Sonnet

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

anthropic/claude-sonnet-4
claude-sonnet-4
claude-sonnet-4-20250514

As of February 13, 2026, the streaming response format for Anthropic models has changed.

Model Overview

A major improvement over , offering better coding abilities, stronger reasoning, and more accurate responses to your instructions.

How to Make a Call

Step-by-Step Instructions

1️ Setup You Can’t Skip

2️ Copy the code example

At the bottom of this page, you'll find that shows how to structure the request. Choose the code snippet in your preferred programming language and copy it into your development environment.

API Schema

Code Example #1

Response

Code Example #2: Streaming Mode

As of February 13, 2026, the streaming response format for Anthropic models has changed. Specifically, the usage fields were renamed as follows:

the state structure is no longer used,
input_tokens → prompt_tokens,
output_tokens

Response

Claude 4.1 Opus

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

anthropic/claude-opus-4.1
claude-opus-4-1
claude-opus-4-1-20250805

All three IDs listed above refer to the same model; we support them for backward compatibility.

As of February 13, 2026, the streaming response format for Anthropic models has changed.

Model Overview

An upgrade to on agentic tasks, real-world coding, and thinking.

How to Make a Call

Step-by-Step Instructions

1️ Setup You Can’t Skip

2️ Copy the code example

At the bottom of this page, you'll find that show how to structure the request. Choose the code snippet in your preferred programming language and copy it into your development environment.

API Schema

Code Example #1: Without Thinking

Response

Code Example #2: Thinking Enabled

Response

Code Example #3: Streaming Mode

As of February 13, 2026, the streaming response format for Anthropic models has changed. Specifically, the usage fields were renamed as follows:

the state structure is no longer used,
input_tokens → prompt_tokens,
output_tokens

Response

Claude 4.5 Haiku

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

claude-haiku-4-5
anthropic/claude-haiku-4.5
claude-haiku-4-5-20251001

As of February 13, 2026, the streaming response format for Anthropic models has changed.

Model Overview

The model offers coding performance comparable to , but at one-third the cost and more than twice the speed.

How to Make a Call

Step-by-Step Instructions

1️ Setup You Can’t Skip

2️ Copy the code example

At the bottom of this page, you'll find that shows how to structure the request. Choose the code snippet in your preferred programming language and copy it into your development environment.

API Schema

Code Example #1

Response

Code Example #2: Streaming Mode

As of February 13, 2026, the streaming response format for Anthropic models has changed. Specifically, the usage fields were renamed as follows:

the state structure is no longer used,
input_tokens → prompt_tokens,
output_tokens

Response

Claude 4.5 Opus

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

anthropic/claude-opus-4-5
claude-opus-4-5
claude-opus-4-5-20251101

As of February 13, 2026, the streaming response format for Anthropic models has changed.

Model Overview

A high-performance chat model that delivers state-of-the-art results on real-world software engineering benchmarks.

How to Make a Call

How to make the first API call

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

3️ 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

API Schema

Code Example #1

Response

Code Example #2: Streaming Mode

As of February 13, 2026, the streaming response format for Anthropic models has changed. Specifically, the usage fields were renamed as follows:

the state structure is no longer used,
input_tokens → prompt_tokens,
output_tokens

Response

Claude 4.6 Opus

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

anthropic/claude-opus-4-6

Claude 4.6 Sonnet

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

anthropic/claude-sonnet-4.6
anthropic/claude-sonnet-4-6-20260218

Model Overview

A general-purpose LLM with an optimal balance of intelligence, cost, and speed. It’s great for chatbots, assistants, and production text generation workflows, and it supports prompt caching for efficient repeated contexts.

How to make the first API call

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

3️ 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

API Schema

Code Example

Response

Baidu

ernie-4.5-0.3b

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

baidu/ernie-4.5-0.3b

Model Overview

A small dense language model suitable for edge-side use and budget-constrained inference.

How to make the first API call

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

3️ 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

API Schema

Code Example

Response

ernie-4.5-21b-a3b-thinking

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

baidu/ernie-4.5-21b-a3b-thinking

Model Overview

A post-trained LLM with 21B total parameters and 3B activated parameters per token. Reasoning variant.

How to make the first API call

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

3️ 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

API Schema

Code Example

Response

ernie-4.5-vl-28b-a3b

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

baidu/ernie-4.5-vl-28b-a3b

Model Overview

A post-trained LLM with 28B total parameters and 3B activated parameters per token. A non-reasoning variant with image and PDF input support.

How to make the first API call

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

3️ 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

API Schema

Code Example

Response

ernie-4.5-300b-a47b

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

baidu/ernie-4.5-300b-a47b

Model Overview

A post-trained LLM with 300B total parameters and 47B activated parameters per token. Non-reasoning variant.

How to make the first API call

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

3️ 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

API Schema

Code Example

Response

ernie-4.5-turbo-128k

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

baidu/ernie-4-5-turbo-128k

Model Overview

A model from the ERNIE 4.5 Turbo subfamily, which Baidu presents as a faster, more cost-efficient, and more efficient alternative to the base ERNIE 4.5. It is optimized for improved response speed and stability, and features a truly large context window of approximately 128K tokens, enabling the processing of entire documents or long-running dialogues.

How to make the first API call

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

3️ 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

API Schema

Code Example

Response

ernie-4.5-turbo-vl-32k

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

baidu/ernie-4-5-turbo-vl-32k

Model Overview

A model from the ERNIE 4.5 Turbo subfamily with multimodal support (text and images), offering a balanced trade-off between performance and computational cost.

How to make the first API call

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

3️ 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

API Schema

Code Example

Response

ernie-5.0-thinking-preview

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

baidu/ernie-5-0-thinking-preview

Model Overview

A reasoning-focused model designed for complex, multi-step problem solving. It improves accuracy on analytical tasks by producing explicit reasoning.

How to make the first API call

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

3️ 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

API Schema

Code Example

Response

ernie-5.0-thinking-latest

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

baidu/ernie-5-0-thinking-latest

Model Overview

How to make the first API call

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

3️ 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

API Schema

Code Example

Response

ernie-x1-turbo-32k

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

baidu/ernie-x1-turbo-32k

Model Overview

How to make the first API call

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

3️ 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

API Schema

Code Example

Response

ernie-x1.1-preview

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

baidu/ernie-x1-1-preview

Model Overview

How to make the first API call

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

3️ 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

API Schema

Code Example

Response

ByteDance

Seed 1.8

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

bytedance/seed-1-8

Model Overview

A general-purpose agentic model optimized for efficient and accurate execution of complex tasks in real-world scenarios.

How to make the first API call

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

3️ 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

API Schema

Code Example

Response

Dola Seed 2.0 Mini

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

bytedance/dola-seed-2-0-mini

Model Overview

A fast and cost-efficient multimodal model for lightweight tasks. Supports text, image, and video inputs with reasoning and agent workflows, handling up to ~256K context.

How to make the first API call

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

3️ 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

API Schema

Code Example

Response

Dola Seed 2.0 Pro

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

bytedance/dola-seed-2-0-pro

Model Overview

A high-performance multimodal model focused on quality and deeper reasoning. Supports text, image, and video inputs with reasoning and agent workflows, handling up to ~256K context.

How to make the first API call

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

3️ 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

API Schema

Code Example

Response

Dola Seed 2.0 Code

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

bytedance/dola-seed-2-0-code

Model Overview

A multimodal model optimized for programming and technical tasks. Supports text, image, and video inputs with reasoning and agent workflows, handling up to ~256K context.

How to make the first API call

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

3️ 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

API Schema

Code Example

Response

Cohere

command-a

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

cohere/command-a

Model Overview

A powerful LLM with advanced capabilities for enterprise applications.

How to make the first API call

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

3️ 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

API Schema

Code Example

Response

DeepSeek V3

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

deepseek-chat
deepseek/deepseek-chat
deepseek/deepseek-chat-v3-0324

We provide the latest version of this model from Mar 24, 2025. All three IDs listed above refer to the same model; we support them for backward compatibility.

Model Overview

DeepSeek V3 (or deepseek-chat) is an advanced conversational AI designed to deliver highly engaging and context-aware dialogues. This model excels in understanding and generating human-like text, making it an ideal solution for creating responsive and intelligent chatbots.

How to make the first API call

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

3️ 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

API Schema

Code Example

Response

DeepSeek R1

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

deepseek/deepseek-r1
deepseek-reasoner

Both IDs listed above refer to the same model; we support them for backward compatibility.

Model Overview

DeepSeek R1 is a cutting-edge reasoning model developed by DeepSeek AI, designed to excel in complex problem-solving, mathematical reasoning, and programming assistance.

How to make the first API call

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

3️ 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

API Schema

Code Example

Response

DeepSeek Chat V3.1

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

deepseek/deepseek-chat-v3.1

Model Overview

August 2025 update of the non-reasoning model.

How to make the first API call

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

3️ 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

API Schema

Code Example

Response

DeepSeek V3.2 Speciale

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

deepseek/deepseek-v3.2-speciale

Model Overview

A high-compute variant of DeepSeek-V3.2 that outperforms GPT-5 and matches Gemini-3.0-Pro in reasoning benchmarks, achieving gold-medal-level results at the 2025 International Mathematical Olympiad (IMO) and the International Olympiad in Informatics (IOI).

How to make the first API call

If you need a more detailed walkthrough for setting up your development environment and making a request step by step — feel free to use our .

API Schema

Code Example

Response

DeepSeek V4 Pro

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

deepseek/deepseek-v4-pro

Model Overview

A high-performance reasoning model as of late April 2026, designed for complex tasks, coding, and logic-heavy workflows. It supports up to 1M context length and includes an advanced thinking mode for deeper analysis.

How to make the first API call

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

3️ 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

API Schema

Code Example

Response

DeepSeek V4 Flash

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

deepseek/deepseek-v4-flash

Model Overview

A fast and cost-efficient language model built for chat and completions. A lighter and faster version of , it supports up to 1M context length and offers both thinking and non-thinking modes for scalable, low-latency workloads.

How to make the first API call

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

3️ 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

API Schema

Code Example

Response

Google

gemini-2.0-flash

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

google/gemini-2.0-flash

Model Overview

A cutting-edge multimodal AI model developed by Google DeepMind, designed to power agentic experiences. This model is capable of processing text and images.

How to make the first API call

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

3️ 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

API Schema

Code Example

Response

gemini-2.5-flash

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

google/gemini-2.5-flash

Model Overview

Gemini 2.5 models are capable of reasoning through their thoughts before responding, resulting in enhanced performance and improved accuracy.

How to make the first API call

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

3️ 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

API Schema

Code Example

A common issue when using reasoning-capable models via API is receiving an empty string in the content field—meaning the model did not return the expected text, yet no error was thrown.

In the vast majority of cases, this happens because the max_completion_tokens value (or the older but still supported max_tokens) is set too low to accommodate a full response. Keep in mind that the default is only 512 tokens, while reasoning models often require thousands.

Response

gemini-2.5-pro

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

google/gemini-2.5-pro

Model Overview

Gemini 2.5 models are capable of reasoning through their thoughts before responding, resulting in enhanced performance and improved accuracy.

How to make the first API call

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

3️ 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

API Schema

Code Example

A common issue when using reasoning-capable models via API is receiving an empty string in the content field—meaning the model did not return the expected text, yet no error was thrown.

Response

gemma-3 (27B)

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

google/gemma-3-27b-it

Model Overview

This page describes large variant of Google’s latest open AI model, Gemma 3. In addition to the capabilities of , this version also supports system and developer roles, enabling you to pass behavior-shaping instructions for the model.

How to make the first API call

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

3️ 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

API Schema

Code Example

Response

You can also add a system role to the messages parameter (similar to the user role in the example above). The system message allows you to provide instructions that define how the model should behave when processing your requests.

Response #2

gemma-3n-4b

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

google/gemma-3n-e4b-it

Model Overview

The first open model built on Google’s next-generation, mobile-first architecture—designed for fast, private, and multimodal AI directly on-device. With Gemma 3n, developers get early access to the same technology that will power on-device AI experiences across Android and Chrome later this year, enabling them to start building for the future today.

How to make the first API call

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

3️ 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

API Schema

Code Example

Note that the system role is not supported in this model. In the messages parameter, only user and assistant roles are available.

Response

gemini-3-flash-preview

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

google/gemini-3-flash-preview

Model Overview

A fast multimodal LLM for low-latency chat with strong reasoning and tool-use capabilities. Supports text input and optional image understanding for vision-based prompts.

How to make the first API call

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

3️ 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

API Schema

Code Example

Response

gemma-4-31b-it

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

google/gemma-4-31b-it

Model Overview

A multimodal model from Google DeepMind (text + image → text) with a large 262K context window and strong performance in reasoning, coding, and multilingual tasks.

How to make the first API call

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

3️ 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

API Schema

Code Example

Response

Gryphe

MythoMax L2 (13B)

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

gryphe/mythomax-l2-13b

Model Overview

This model represents a pinnacle in the evolution of LLMs, purpose-built for storytelling and roleplaying, delivering a rich sense of connection with characters and narrative arcs.

How to make the first API call

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

3️ 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

API Schema

Code Example

Response

Llama-3.3-70B-Versatile

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

meta-llama/llama-3.3-70b-versatile

Model Overview

An advanced multilingual large language model with 70 billion parameters, optimized for diverse NLP tasks. It delivers high performance across benchmarks while remaining efficient for a wide range of applications.

How to make the first API call

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

3️ 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

API Schema

Code Example

Response

MiniMax

Text-01

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

MiniMax-Text-01

Model Overview

A powerful language model developed by MiniMax AI, designed to excel in tasks requiring extensive context processing and reasoning capabilities. With a total of 456 billion parameters, of which 45.9 billion are activated per token, this model utilizes a hybrid architecture that combines various attention mechanisms to optimize performance across a wide array of applications.

How to make the first API call

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

3️ 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

API Schema

Code Example

Response

post

Body

modelstring · enumRequiredPossible values:

rolestring · enumRequired

The role of the author of the message — in this case, the user

Possible values:

contentany ofRequired

The contents of the user message.

stringOptional

typestring · enumRequired

The type of the content part.

Possible values:

textstringRequired

The text content.

namestringOptional

An optional name for the participant. Provides the model information to differentiate between participants of the same role.

contentany ofRequired

The contents of the developer message.

stringOptional

typestring · enumRequired

The type of the content part.

Possible values:

textstringRequired

The text content.

rolestring · enumRequired

The role of the author of the message — in this case, the developer.

Possible values:

namestringOptional

An optional name for the participant. Provides the model information to differentiate between participants of the same role.

rolestring · enumRequired

The role of the author of the message — in this case, the system.

Possible values:

contentany ofRequired

The contents of the system message.

stringOptional

typestring · enumRequired

The type of the content part.

Possible values:

textstringRequired

The text content.

namestringOptional

An optional name for the participant. Provides the model information to differentiate between participants of the same role.

rolestring · enumRequired

The role of the author of the message — in this case, the tool.

Possible values:

contentstringRequired

The contents of the tool message.

tool_call_idstringRequired

Tool call that this message is responding to.

namestring · nullableOptional

An optional name for the participant. Provides the model information to differentiate between participants of the same role.

rolestring · enumRequired

The role of the author of the message — in this case, the Assistant.

Possible values:

contentany ofOptional

The contents of the Assistant message. Required unless tool_calls or function_call is specified.

stringOptional

The contents of the Assistant message.

itemsany ofOptional

typestring · enumRequired

The type of the content part.

Possible values:

textstringRequired

The text content.

refusalstringRequired

The refusal message generated by the model.

typestring · enumRequired

The type of the content part.

Possible values:

namestringOptional

An optional name for the participant. Provides the model information to differentiate between participants of the same role.

idstringRequired

The ID of the tool call.

typestring · enumRequired

The type of the tool. Currently, only function is supported.

Possible values:

namestringRequired

The name of the function to call.

argumentsstringRequired

The arguments to call the function with, as generated by the model in JSON format. Note that the model does not always generate valid JSON, and may hallucinate parameters not defined by your function schema. Validate the arguments in your code before calling your function.

refusalstring · nullableOptional

The refusal message by the Assistant.

max_tokensnumber · min: 1Optional

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.

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

include_usagebooleanRequired

typestring · enumRequired

The type of the tool. Currently, only function is supported.

Possible values:

descriptionstringOptional

A description of what the function does, used by the model to choose when and how to call the function.

namestringRequired

The name of the function to be called. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.

Other propertiesany · nullableOptional

The parameters the functions accepts, described as a JSON Schema object.

strictboolean · nullableOptional

Whether to enable strict schema adherence when generating the function call. If set to True, the model will follow the exact schema defined in the parameters field. Only a subset of JSON Schema is supported when strict is True.

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.

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:

typestring · enumRequired

The type of the tool. Currently, only function is supported.

Possible values:

namestringRequired

The name of the function to call.

parallel_tool_callsbooleanOptional

Whether to enable parallel function calling during tool use.

response_formatone ofOptional

An object specifying the format that the model must output.

typestring · enumRequired

The type of response format being defined. Always text.

Possible values:

typestring · enumRequired

The type of response format being defined. Always json_object.

Possible values:

typestring · enumRequired

The type of response format being defined. Always json_schema.

Possible values:

namestringRequired

The name of the response format. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.

Other propertiesany · nullableOptional

strictboolean · nullableOptional

Whether to enable strict schema adherence when generating the output. If set to True, the model will always follow the exact schema defined in the schema field. Only a subset of JSON Schema is supported when strict is True.

descriptionstringOptional

A description of what the response format is for, used by the model to determine how to respond in the format.

echobooleanOptional

If True, the response will contain the prompt. Can be used with logprobs to return prompt logprobs.

temperaturenumber · max: 2Optional

What sampling temperature to use. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic. We generally recommend altering this or top_p but not both.

top_pnumber · min: 0.01 · max: 1Optional

An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered. We generally recommend altering this or temperature but not both.

ninteger · min: 1 · nullableOptional

How many chat completion choices to generate for each input message. Note that you will be charged based on the number of generated tokens across all of the choices. Keep n as 1 to minimize costs.

stopany ofOptional

Up to 4 sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence.

stringOptional

string[]Optional

any · nullableOptional

logprobsboolean · nullableOptional

Whether to return log probabilities of the output tokens or not. If True, returns the log probabilities of each output token returned in the content of message.

top_logprobsnumber · max: 20 · nullableOptional

An integer between 0 and 20 specifying the number of most likely tokens to return at each token position, each with an associated log probability. logprobs must be set to True if this parameter is used.

Other propertiesnumber · min: -100 · max: 100Optional

frequency_penaltynumber · min: -2 · max: 2 · nullableOptional

Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim.

typestring · enumRequired

The type of the predicted content you want to provide.

Possible values:

contentany ofRequired

The content that should be matched when generating a model response. If generated tokens would match this content, the entire model response can be returned much more quickly.

stringOptional

The content used for a Predicted Output. This is often the text of a file you are regenerating with minor changes.

typestring · enumRequired

The type of the content part.

Possible values:

textstringRequired

The text content.

presence_penaltynumber · min: -2 · max: 2 · nullableOptional

Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics.

seedinteger · min: 1Optional

This feature is in Beta. If specified, our system will make a best effort to sample deterministically, such that repeated requests with the same seed and parameters should return the same result.

min_pnumber · min: 0.001 · max: 0.999Optional

A number between 0.001 and 0.999 that can be used as an alternative to top_p and top_k.

top_knumberOptional

Only sample from the top K options for each subsequent token. Used to remove "long tail" low probability responses. Recommended for advanced use cases only. You usually only need to use temperature.

repetition_penaltynumber · nullableOptional

A number that controls the diversity of generated text by reducing the likelihood of repeated sequences. Higher values decrease repetition.

Responses

200Success

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

indexnumberRequired

The index of the choice in the list of choices.

Example: 0

rolestringRequired

The role of the author of this message.

Example: assistant

contentstringRequired

The contents of the message.

Example: 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?

refusalstring · nullableOptional

The refusal message generated by the model.

typestring · enumRequired

The type of the URL citation. Always url_citation.

Possible values:

end_indexintegerRequired

The index of the last character of the URL citation in the message.

start_indexintegerRequired

The index of the first character of the URL citation in the message.

titlestringRequired

The title of the web resource.

urlstringRequired

The URL of the web resource.

idstringRequired

Unique identifier for this audio response.

datastringRequired

Base64 encoded audio bytes generated by the model, in the format specified in the request.

transcriptstringRequired

Transcript of the audio generated by the model.

expires_atintegerRequired

The Unix timestamp (in seconds) for when this audio response will no longer be accessible on the server for use in multi-turn conversations.

idstringRequired

The ID of the tool call.

typestring · enumRequired

The type of the tool.

Possible values:

argumentsstringRequired

namestringRequired

The name of the function to call.

idstringRequired

The ID of the tool call.

typestring · enumRequired

The type of the tool.

Possible values:

inputstringRequired

The input for the custom tool call generated by the model.

namestringRequired

The name of the custom tool to call.

finish_reasonstring · enumRequired

The reason the model stopped generating tokens. This will be stop if the model hit a natural stop point or a provided stop sequence, length if the maximum number of tokens specified in the request was reached, content_filter if content was omitted due to a flag from our content filters, tool_calls if the model called a tool

Possible values:

bytesinteger[]Required

A list of integers representing the UTF-8 bytes representation of the token. Useful in instances where characters are represented by multiple tokens and their byte representations must be combined to generate the correct text representation. Can be null if there is no bytes representation for the token.

logprobnumberRequired

The log probability of this token, if it is within the top 20 most likely tokens. Otherwise, the value -9999.0 is used to signify that the token is very unlikely.

tokenstringRequired

The token.

bytesinteger[] · nullableOptional

logprobnumberRequired

The log probability of this token, if it is within the top 20 most likely tokens. Otherwise, the value -9999.0 is used to signify that the token is very unlikely.

tokenstringRequired

The token.

bytesinteger[]Required

logprobnumberRequired

The log probability of this token, if it is within the top 20 most likely tokens. Otherwise, the value -9999.0 is used to signify that the token is very unlikely.

tokenstringRequired

The token.

bytesinteger[] · nullableOptional

logprobnumberRequired

The log probability of this token, if it is within the top 20 most likely tokens. Otherwise, the value -9999.0 is used to signify that the token is very unlikely.

tokenstringRequired

The token.

modelstringRequired

The model used for the chat completion.

Example: Qwen/Qwen2.5-7B-Instruct-Turbo

prompt_tokensnumberRequired

Number of tokens in the prompt.

Example: 137

completion_tokensnumberRequired

Number of tokens in the generated completion.

Example: 914

total_tokensnumberRequired

Total number of tokens used in the request (prompt + completion).

Example: 1051

accepted_prediction_tokensinteger · nullableOptional

When using Predicted Outputs, the number of tokens in the prediction that appeared in the completion.

audio_tokensinteger · nullableOptional

Audio input tokens generated by the model.

reasoning_tokensinteger · nullableOptional

Tokens generated by the model for reasoning.

rejected_prediction_tokensinteger · nullableOptional

When using Predicted Outputs, the number of tokens in the prediction that did not appear in the completion. However, like reasoning tokens, these tokens are still counted in the total completion tokens for purposes of billing, output, and context window limits.

audio_tokensinteger · nullableOptional

Audio input tokens present in the prompt.

cached_tokensinteger · nullableOptional

Cached tokens present in the prompt.

credits_usednumberRequired

The number of tokens consumed during generation.

Example: 120000

usd_spentnumberRequired

The total amount of money spent by the user in USD.

Example: 0.06

post

/v1/chat/completions

200Success

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AI/ML API Documentation

Quickstart

Documentation Map

hashtagBrowse Models

hashtagGoing Deeper

hashtagHave a Minute? Help Make the Docs Better!

Quickstart

hashtagGenerating an AIML API Key

hashtagChoosing the Development Environment

hashtagMaking an API Call

hashtagCode Step-by-Step

hashtagFuture Steps

OpenClaw AI/ML

hashtagAbout

hashtagWhat you get

hashtagPrerequisites

hashtagInstallation

hashtagOption 1: Install via npm (recommended)

hashtagOption 2: Build from source

hashtagOption 3: Install skills from the official repo (ClawHub)

hashtagInstall the CLI

hashtagInstall the skills

hashtagHow it fits into OpenClaw

hashtagWhat these skills do

hashtagConfigure AI/ML API in OpenClaw

hashtagSelect provider

hashtagUse OpenClaw

hashtagUse via a chat connector (Telegram example)

hashtagUse via CLI

hashtagUse Cases

hashtagSupported models

hashtagMore

Supported SDKs

hashtagREST API

hashtagInstallation

hashtagAuthorization

hashtagRequest Example

hashtagOpenAI

hashtagInstallation

hashtagExample Code

hashtagAI/ML API Python library

hashtagInstallation

hashtagRequest Example

hashtagNext Steps

API REFERENCES

Service Endpoints

Account Balance

hashtag[legacy] Get account balance info

hashtagGet balance info

hashtagGet detailed billing info

API Key Management

hashtagCreate a new API key

hashtagList API keys

hashtagGet the API key

hashtagUpdate an API key

hashtagDelete an API key

All Model IDs

Text Models (LLM)

qwen-max

hashtagModel Overview

hashtagAPI Schema

hashtagCode Example

qwen-plus

hashtagModel Overview

hashtagAPI Schema

hashtagCode Example

qwen-turbo

hashtagModel Overview

hashtagAPI Schema

hashtagCode Example

qwen3-32b

hashtagModel Overview

hashtagAPI Schema

hashtagCode Example #1: Without Thinking and Streaming

hashtagCode Example #2: Enable Thinking and Streaming

qwen3-coder-480b-a35b-instruct

hashtagModel Overview

hashtagAPI Schema

hashtagCode Example

qwen3-next-80b-a3b-instruct

Browse Models

Going Deeper

Have a Minute? Help Make the Docs Better!

Generating an AIML API Key

Choosing the Development Environment

Making an API Call

Code Step-by-Step

Future Steps

About

What you get

Prerequisites

Installation

Option 1: Install via npm (recommended)

Option 2: Build from source

Option 3: Install skills from the official repo (ClawHub)

Install the CLI

Install the skills

How it fits into OpenClaw

What these skills do

Configure AI/ML API in OpenClaw

Select provider

Use OpenClaw

Use via a chat connector (Telegram example)

Use via CLI

Use Cases

Supported models

More

REST API

Installation

Authorization

Request Example

OpenAI

Installation

Example Code

AI/ML API Python library

Installation

Request Example

Next Steps

[legacy] Get account balance info

Get balance info

Get detailed billing info

Create a new API key

List API keys

Get the API key

Update an API key

Delete an API key

Model Overview

API Schema

Code Example

Model Overview

API Schema

Code Example

Model Overview

API Schema

Code Example

Model Overview

API Schema

Code Example #1: Without Thinking and Streaming

Code Example #2: Enable Thinking and Streaming

Model Overview

API Schema

Code Example

Model Overview

API Schema

Code Example

Model Overview

API Schema

Code Example

Model Overview

API Schema

Code Example

Model Overview

API Schema

Code Example

Model Overview

API Schema

Code Example

Model Overview

API Schema

Code Example