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AI/ML API Documentation

Quickstart

Documentation Map

Learn how to get started with the AI/ML API

This documentation portal is designed to help you choose and configure the AI model that best suits your needs—or one of our solutions (ready-to-use tools for specific practical tasks) from our available options and correctly integrate it into your code.

Have suggestions for improvement?

Trending Models

Setting Up

A step-by-step guide to setting up and making a test call to the AI model, including generating an API key, configuring the Base URL, and running the first request.

Here, you'll learn how to start using our API in your code. The following steps must be completed regardless of whether you integrate one of the models we offer or use our ready-made solution:

generating an AIML API Key,
configuring the base URL,
making an API call.

Let's walk through an example of connecting to the model via OpenAI SDK. This guide is suitable even for complete beginners.

Generating an AIML API Key

What is an API Key?

You can find your AIML API key on the .

An AIML API Key is a credential that grants you access to our API from within your code. It is a sensitive string of characters that should be kept confidential. Do not share this API key with anyone else, as it could be misused without your knowledge.

⚠️ 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 API key. Follow these steps:

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

Configuring Base URL

What is a Base URL?

The Base URL is the first part of the URL (including the protocol, domain, and pathname) that determines the server responsible for handling your request. It’s crucial to configure the correct Base URL in your application, especially if you are using SDKs from OpenAI, Azure, or other providers. By default, these SDKs are set to point to their servers, which are not compatible with our API keys and do not support many of the models we offer.

Depending on your environment and application, you will set the base URL differently. Below is a universal string that you can use to access our API. Copy it or return here later when you are ready with your environment or app.

The AI/ML API supports both versioned and non-versioned URLs, providing flexibility in your API requests. You can use either of the following formats:

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

Using versioned URLs can help ensure compatibility with future updates and changes to the API. It is recommended to use versioned URLs for long-term projects to maintain stability.

Making an API Call

Based on your environment, you will call our API differently. Below are two common ways to call our API using two popular programming languages: Python and NodeJS.

In the examples below, we use the . This is possible due to our compatibility with most OpenAI APIs, but this is just one approach. You can use our API without this SDK with raw HTTP queries.

If you don’t want lengthy explanations, here’s the code you can use right away in a Python or Node.js program. You only need to replace <YOUR_AIMLAPI_KEY> with your AIML API Key obtained from your account. However, below, we will still go through these examples step by step in both languages explaining every single line.

Step-by-step example in Python

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

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

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

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

Activate created virtual environment

Install requirement dependencies. In our case we need only OpenAI SDK

Create new file and name it as

Step-by-step example in NodeJS

As in the example from Python, we start from the very beginning too. We assume you already have Node.js installed. If not, here is a .

We need to create a new folder for the example project:

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

Now create a project file:

Install the required dependencies:

Create a file with the source code:

And paste the following content:

You will see a response that looks like this:

Code Explanation

Both examples are written in different programming languages, but despite that, they look very similar. Let's break down the code step by step and see what's going on.

In the examples above, we are using the OpenAI SDK. The OpenAI SDK is a nice module that allows us to use the AI/ML API without dealing with repetitive boilerplate code for handling HTTP requests. Before we can use the OpenAI SDK, it needs to be imported. The import happens in the following places:

Simple as it is. The next step is to initialize variables that our code will use. The two main ones are: the base URL and the API key. We already discussed them at the beginning of the article.

To communicate with LLM models, users use texts. These texts are usually called "Prompts." Inside our code, we have prompts with two roles: the system and the user. The system prompt is designed to be the main source of instruction for LLM generation, while the user prompt is designed to be user input, the subject of the system prompt. Despite that many models can operate differently, this behavior usually applies to chat LLM models, currently one of the most useful and popular ones.

Inside the code, the prompts are called in variables systemPrompt, userPrompt in JS, and system_prompt, user_prompt in Python.

Before we use the API, we need to create an instance of the OpenAI SDK class. It allows us to use all their methods. The instance is created with our imported package, and here we forward two main parameters: the base URL and the API key.

Because of notation, these two parameters are called slightly differently in these different languages (camel case in JS and snake case in Python), but their functionality is the same.

All preparation steps are done. Now we need to write our functionality and create something great. In the examples above, we make the simplest travel agent. Let's break down the steps of how we send a request to the model.

The best practice is to split the code blocks into complete parts with their own logic and not place executable code inside global module code. This rule applies in both languages we discuss. So we create a main function with all our logic. In JS, this function needs to be async, due to Promises and simplicity. In Python, requests run synchronously.

The OpenAI SDK provides us with methods to communicate with chat models. It is placed inside the chat.completions.create function. This function accepts multiple parameters but requires only two: model and messages.

model is a string, the name of the model that you want to use. For the best results, use a model designed for chat, or you can get unpredictable results if the model is not fine-tuned for that purpose. A list of supported models can be found here.

messages is an array of objects with a content field as prompt and a role string that can be one of system, user, tool, assistant. With the role, the model can understand what to do with this prompt: Is this an instruction? Is this a user message? Is this an example of how to answer? Is this the result of code execution? The tool role is used for more complex behavior and will be discussed in another article.

In our example, we also use max_tokens and temperature.

With that knowledge, we can now send our request like the following:

The response from the function chat.completions.create contains a . Completion is a fundamental part of LLM models' logic. Every LLM model is some sort of word autocomplete engine, trained by huge amounts of data. The chat models are designed to autocomplete large chunks of messages with prompts and certain roles, but other models can have their own custom logic without even roles.

Inside this completion, we are interested in the text of the generation. We can get it by getting the result from the completion variable:

In certain cases, completion can have multiple results. These results are called choices. Every choice has a message, the product of generation. The string content is placed inside the content variable, which we placed inside our response variable above.

In the next steps, we can finally see the results. In both examples, we print the user prompt and response like it was a conversation:

Voila! Using AI/ML API models is the simplest and most productive way to get into the world of Machine Learning and Artificial Intelligence.

Future Steps

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.

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.

API REFERENCES

Service Endpoints

Account Balance

Get account balance info

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 account dashboard.

Alibaba Cloud

qwen-max

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

qwen-max

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 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.

3️ Modify the code example

API Schema

Code Example

Response

qwen-turbo

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

qwen-turbo

Qwen2.5-7B-Instruct-Turbo

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

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

Qwen2.5-72B-Instruct-Turbo

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

Qwen/Qwen2.5-72B-Instruct-Turbo

Model Overview

A state-of-the-art large language model designed for a variety of natural language processing tasks, including instruction following, coding assistance, and mathematical problem-solving.

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.

3️ Modify the code example

API Schema

Code Example

Response

Qwen2.5-Coder-32B-Instruct

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

Qwen/Qwen2.5-Coder-32B-Instruct

Model Overview

The 32B variant of the latest code-focused model series (formerly CodeQwen). The most capable, with strong performance in coding, math, and general tasks.

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.

3️ Modify the code example

API Schema

Code Example

Response

Qwen3-235B-A22B

This documentation is valid for the following model:

Qwen/Qwen3-235B-A22B-fp8-tput

qwen3-235b-a22b-thinking-2507

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

alibaba/qwen3-235b-a22b-thinking-2507

qwen3-next-80b-a3b-instruct

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

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

qwen3-max-instruct

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

alibaba/qwen3-max-instruct

qwen3-omni-30b-a3b-captioner

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

alibaba/qwen3-omni-30b-a3b-captioner

Anthracite

Anthropic

Claude 3 Haiku

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

anthropic/claude-3-haiku

Claude 3 Opus

Model Overview

A highly capable multimodal model designed to process both text and image data. It excels in tasks requiring complex reasoning, mathematical problem-solving, coding, and multilingual text understanding.

Claude 4 Opus

Model Overview

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

Claude 4.5 Sonnet

Model Overview

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

Claude 4.5 Haiku

Model Overview

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

Cohere

DeepSeek

DeepSeek V3

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

deepseek-chat
deepseek/deepseek-chat

DeepSeek Chat V3.1

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

deepseek/deepseek-chat-v3.1

DeepSeek Reasoner V3.1

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

deepseek/deepseek-reasoner-v3.1

Deepseek Reasoner V3.1 Terminus

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

deepseek/deepseek-reasoner-v3.1-terminus

DeepSeek V3.2 Exp Non-thinking

Model Overview

September 2025 update of the non-reasoning model.

Google

gemini-2.0-flash-exp

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.

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.

gemma-3

Model Overview

This page describes four variants of Google’s latest open AI model, Gemma 3. All variants share the same set of parameters but differ in speed and reasoning capabilities.

Llama-3-chat-hf

Model Overview

This model is optimized for dialogue use cases and outperform many existing open-source chat models on common industry benchmarks.

You can also view on our main website.

Llama-3-8B-Instruct-Lite

Model Overview

A generative text model optimized for dialogue and instruction-following use cases. It leverages a refined transformer architecture to deliver high performance in text generation tasks.

Llama-3.1-8B-Instruct-Turbo

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

meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo

Model Overview

An advanced language model designed for high-quality text generation, optimized for professional and industry applications requiring extensive GPU resources.

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.

3️ Modify the code example

API Schema

Code Example

Response

Llama-3.1-70B-Instruct-Turbo

Model Overview

A state-of-the-art instruction-tuned language model designed for multilingual dialogue use cases. It excels in natural language generation and understanding tasks, outperforming many existing models in the industry benchmarks.

Llama-3.2-3B-Instruct-Turbo

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

meta-llama/Llama-3.2-3B-Instruct-Turbo

Model Overview

A large language model (LLM) optimized for instruction-following tasks, striking a balance between computational efficiency and high-quality performance. It excels in multilingual tasks, offering a lightweight solution without compromising on quality.

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.

3️ Modify the code example

API Schema

Code Example

Response

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.

MiniMax

m1

Model Overview

The world's first open-weight, large-scale hybrid-attention reasoning model.

Mistral AI

mistral-nemo

Model Overview

A state-of-the-art large language model designed for advanced natural language processing tasks, including text generation, summarization, translation, and sentiment analysis.

Moonshot

NousResearch

NVIDIA

OpenAI

Setting Up

A step-by-step guide to setting up and making a test call to the AI model, including generating an API key, configuring the Base URL, and running the first request.

Here, you'll learn how to start using our API in your code. The following steps must be completed regardless of whether you integrate one of the models we offer or use our ready-made solution:

generating an AIML API Key,
configuring the base URL,
making an API call.

Let's walk through an example of connecting to the model via OpenAI SDK. This guide is suitable even for complete beginners.

Generating an AIML API Key

What is an API Key?

You can find your AIML API key on the .

⚠️ 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 API key. Follow these steps:

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

Configuring Base URL

What is a Base URL?

The AI/ML API supports both versioned and non-versioned URLs, providing flexibility in your API requests. You can use either of the following formats:

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

Using versioned URLs can help ensure compatibility with future updates and changes to the API. It is recommended to use versioned URLs for long-term projects to maintain stability.

Making an API Call

Based on your environment, you will call our API differently. Below are two common ways to call our API using two popular programming languages: Python and NodeJS.

In the examples below, we use the . This is possible due to our compatibility with most OpenAI APIs, but this is just one approach. You can use our API without this SDK with raw HTTP queries.

Step-by-step example in Python

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

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

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

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

Activate created virtual environment

Install requirement dependencies. In our case we need only OpenAI SDK

Create new file and name it as

Step-by-step example in NodeJS

As in the example from Python, we start from the very beginning too. We assume you already have Node.js installed. If not, here is a .

We need to create a new folder for the example project:

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

Now create a project file:

Install the required dependencies:

Create a file with the source code:

And paste the following content:

You will see a response that looks like this:

Code Explanation

Both examples are written in different programming languages, but despite that, they look very similar. Let's break down the code step by step and see what's going on.

Simple as it is. The next step is to initialize variables that our code will use. The two main ones are: the base URL and the API key. We already discussed them at the beginning of the article.

Inside the code, the prompts are called in variables systemPrompt, userPrompt in JS, and system_prompt, user_prompt in Python.

Because of notation, these two parameters are called slightly differently in these different languages (camel case in JS and snake case in Python), but their functionality is the same.

In our example, we also use max_tokens and temperature.

With that knowledge, we can now send our request like the following:

Inside this completion, we are interested in the text of the generation. We can get it by getting the result from the completion variable:

In the next steps, we can finally see the results. In both examples, we print the user prompt and response like it was a conversation:

Voila! Using AI/ML API models is the simplest and most productive way to get into the world of Machine Learning and Artificial Intelligence.

AI/ML API Documentation

Quickstart

Documentation Map

Setting Up

Generating an AIML API Key

Configuring Base URL

Making an API Call

Code Explanation

Future Steps

Supported SDKs

API REFERENCES

Service Endpoints

Account Balance

Get account balance info

Alibaba Cloud

qwen-max

qwen-plus

Model Overview

How to Make a Call

API Schema

Code Example

qwen-turbo

Qwen2.5-7B-Instruct-Turbo

Qwen2.5-72B-Instruct-Turbo

Model Overview

How to Make a Call

API Schema

Code Example

Qwen2.5-Coder-32B-Instruct

Model Overview

How to Make a Call

API Schema

Code Example

Qwen3-235B-A22B

qwen3-235b-a22b-thinking-2507

qwen3-next-80b-a3b-instruct

qwen3-max-instruct

qwen3-omni-30b-a3b-captioner

Anthracite

Anthropic

Claude 3 Haiku

Claude 3 Opus

Model Overview

Claude 4 Opus

Model Overview

Claude 4.5 Sonnet

Model Overview

Claude 4.5 Haiku

Model Overview

Cohere

DeepSeek

DeepSeek V3

DeepSeek Chat V3.1

DeepSeek Reasoner V3.1

Deepseek Reasoner V3.1 Terminus

DeepSeek V3.2 Exp Non-thinking

Model Overview

Google

gemini-2.0-flash-exp

Model Overview

gemini-2.0-flash

Model Overview

gemma-3

Model Overview

Meta

Llama-3-chat-hf

Model Overview

Llama-3-8B-Instruct-Lite

Model Overview

Llama-3.1-8B-Instruct-Turbo

Model Overview

How to Make a Call

API Schema

Code Example

Llama-3.1-70B-Instruct-Turbo

Model Overview

Llama-3.2-3B-Instruct-Turbo

Model Overview

How to Make a Call

API Schema