> ## Documentation Index
> Fetch the complete documentation index at: https://io-migration.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Getting Started with AI Models API

> IO Intelligence API (Application Programming Interface) serves as a bridge to powerful, open-source machine learning models, deployed on IO Net hardware, allowing you to integrate cutting-edge AI capabilities into your projects with relative ease. In simpler terms, the API is like a helper that lets you use smart programs in your projects. To make the integration easy for your application, we fully support the API contract presented by OpenAI, being fully OpenAI API compatible for Chat Completions and .

[Get Started with the IO Intelligence API](https://www.youtube.com/embed/6kVSfjt1P8s)

### Important Note on Usage Limits

The IO Intelligence API provides the following **free daily limits** (measured in LLM tokens) **per account, per day, per model.**.

#### Column Definitions:

* **LLM Model Name:** The name of the large language model (LLM) available for use.
* **Daily Chat Quota:** The maximum number of tokens you can use in chat-based interactions with this model per day.
* **Daily API Quota:** The maximum number of tokens allowed for API-based interactions per day.
* **Daily Embeddings Quota:** The maximum number of tokens available for embedding operations per day.
* **Context Length:** The maximum number of tokens the model can process in a single request (including both input and output).

Please refer to the table below for model-specific limits:

| LLM Model Name | Daily Chat quote | Daily API quote | Daily Embeddings quote | Context Length |
| - | - | - | - | - |
| meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8 | 1,000,000 tk | 500,000 tk | N/A | 430,000 tk |
| openai/gpt-oss-120b | 1,000,000 tk | 500,000 tk | N/A | 131,072 tk |
| deepseek-ai/DeepSeek-R1-0528 | 1,000,000 tk | 500,000 tk | N/A | 128,000 tk |
| Qwen3-235B-A22B-Thinking-2507 | 1,000,000 tk | 500,000 tk | N/A | 262,144 tk |
| Intel/Qwen3-Coder-480B-A35B-Instruct-int4-mixed-ar | 1,000,000 tk | 500,000 tk | N/A | 106,000 tk |
| meta-llama/Llama-3.3-70B-Instruct | 1,000,000 tk | 500,000 tk | N/A | 128,000 tk |
| mistralai/Magistral-Small-2506 | 1,000,000 tk | 500,000 tk | N/A | 128,000 tk |
| mistralai/Devstral-Small-2505 | 1,000,000 tk | 500,000 tk | N/A | 128,000 tk |
| mistralai/Mistral-Large-Instruct-2411 | 1,000,000 tk | 500,000 tk | N/A | 128,000 tk |
| CohereForAI/aya-expanse-32b | 1,000,000 tk | 500,000 tk | N/A | 8,000 tk |
| Qwen/Qwen2.5-VL-32B-Instruct | 0 tk | 500,000 tk | N/A | 32,000 tk |
| meta-llama/Llama-3.2-90B-Vision-Instruct | 0 tk | 500,000 tk | N/A | 16,000 tk |
| BAAI/bge-multilingual-gemma2 | N/A | N/A | 50,000,000 tk | 4,096 tk |

This limit is designed to ensure fair and balanced usage for all users. If you anticipate needing a higher request limit, please consider optimizing your implementation or reach out to us for assistance.

## Introduction

You can interact with the API using HTTP requests from any programming language or by using the official Python and Node.js libraries.

To install the official Python library, run the following command:

```
pip install openai
```

To install the official Node.js library, run this command in your Node.js project directory:

```
npm install openai
```

### Example: Using the IO Intelligence API with Python

Here’s an example of how you can use the `openai Python` library to interact with the IO Intelligence API:

```python openai Python theme={null}
import openai

client = openai.OpenAI(
    api_key="$IOINTELLIGENCE_API_KEY",
    base_url="https://api.intelligence.io.solutions/api/v1/",
)

response = client.chat.completions.create(
    model="meta-llama/Llama-3.3-70B-Instruct",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Hi, I am doing a project using IO Intelligence."},
    ],
    temperature=0.7,
    stream=False,
    max_completion_tokens=50
)

print(response.choices[0].message.content)
```

This snippet demonstrates how to configure the client, send a chat completion request using the `Llama-3.3-70B-Instruct` model, and retrieve a response.

## Authentication

### API keys

IO Intelligence APIs authenticate requests using API keys. You can [generate API keys from your user](https://ai.io.net/ai/api-keys) account:

> [!WARNING]
>
> Always treat your API key as a secret! Do not share it or expose it in client-side code (e.g., browsers or mobile apps). Instead, store it securely in an environment variable or a key management service on your backend server.

Include the API key in an `Authorization` HTTP header for all API requests:

```
Authorization: Bearer \$IOINTELLIGENCE_API_KEY
```

### Example: List Available Models

Here's an example `curl` command to list all models available in IO Intelligence:

```curl curl theme={null}
curl https://api.intelligence.io.solutions/api/v1/models /
  -H "Authorization: Bearer \$IOINTELLIGENCE_API_KEY" 
```

![](https://mintcdn.com/io-migration/gNTVwsExumjDr_tJ/images/reference/e3e8c7c183fa588fc355c7bd42d70b93735d2c229da84c09310b5df20e27ca3f-chatetest-1.jpg?fit=max\&auto=format\&n=gNTVwsExumjDr_tJ\&q=85\&s=d9b5a87d0eb1e2a070c55508c47b1e63)

This request should return a response like this:

```json json theme={null}
{
  "object": "list",
  "data": [
    {
      "id": "meta-llama/Llama-3.3-70B-Instruct",
      "object": "model",
      "created": 1736168795,
      "owned_by": "io-intelligence",
      "root": null,
      "parent": null,
      "max_model_len": null,
      "permission": [
        {
          "id": "modelperm-30ac078e67ab456a9279d53cf83155bb",
          "object": "model_permission",
          "created": 1736755239,
          "allow_create_engine": false,
          "allow_sampling": true,
          "allow_logprobs": true,
          "allow_search_indices": false,
          "allow_view": true,
          "allow_fine_tuning": false,
          "organization": "*",
          "group": null,
          "is_blocking": false
        }
      ]
    },
    ...
  ]
}
```

## Making requests

To test the API, use the following `curl` command. Replace `$IOINTELLIGENCE_API_KEY` with your actual **API key.**

```curl curl theme={null}
curl https://api.intelligence.io.solutions/api/v1/chat/completions /
  -H "Content-Type: application/json" /
  -H "Authorization: Bearer \$IOINTELLIGENCE_API_KEY" /
  -d '{
     "model": "meta-llama/Llama-3.3-70B-Instruct",
     "messages": [{"role": "user", "content": "Say this is a test!"}],
     "reasoning_content": true,
     "temperature": 0.7
   }'
```

![](https://mintcdn.com/io-migration/gNTVwsExumjDr_tJ/images/reference/24379dbcfebcf3e58eb79022144a5a88a4f41a2d59df9b06c0991675ccccb86c-chatetest-2.jpg?fit=max\&auto=format\&n=gNTVwsExumjDr_tJ\&q=85\&s=ea2c30737a1dbffce8c408af2de41c3b)

This command queries the `meta-llama/Llama-3.3-70B-Instruct` model to generate a chat completion for the input: "*Say this is a test!*".:

### Example Response

The API should return a response like this:

```json json theme={null}
{
  "id": "01945ea6-1d9f-9d46-efbc-2608dcc78169",
  "object": "chat.completion",
  "created": 1736754732,
  "model": "meta-llama/Llama-3.3-70B-Instruct",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "This is a test!"
      },
      "logprobs": null,
      "finish_reason": "stop",
      "stop_reason": null
    }
  ],
  "usage": {
    "prompt_tokens": 12,
    "total_tokens": 18,
    "completion_tokens": 6,
    "prompt_tokens_details": null
  },
  "prompt_logprobs": null
}
```

### Key Details in the Response

* **finish\_reason**: Indicates why the generation stopped (e.g., "stop").
* **choices**: Contains the generated response(s). Adjust the n parameter to generate multiple response choices.

With these steps, you've successfully made your first request to the IO Intelligence API.


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