# AKI.IO Documentation

> Full documentation index: https://aki.io/llms.txt


---

## Getting Started


### [Get Your API Key](https://aki.io/docs/getting-started/get-your-api-key/)

AKI.IO is the European AI inference provider for companies and coders that need control over models, costs, and compliance. The simple API follows OpenAI and Anthropic-standards to access leading open-weight AI models. Enjoy seamless integration and scalable deployment on a sovereign European AI cloud infrastructure, hosted on high-performance GPU servers in ISO 27001-certified data centers without hyperscalers 

To get started, create a free user account at [https://aki.io/signup](https://aki.io/signup) and get a free trial with millions of tokens in order to evaluate the API capabilities.


Once you have registered your user account, [sign in to your user profile](https://aki.io/admin/user) in order to access your API key.


![image](https://aki.io/media/original_images/aki_docs_screenshot_api_key.png)


> The API key is required for all API requests. The key is bound exclusive to your user account and should not be passed on. Make sure to keep it safe and not expose it by embedding it directly in your public HTML code.


The default setting of your API key provides access to all available AI models. Select dedicated endpoints in order to restrict access to certain models.

A company account is required to use the API in a production environment. This company account regulates all legal matters, data protection agreements, as well as pricing and billing for the API usage.

When you create a company account from within your user account, you automatically become the company administrator. 

This enables:
- Creating Additional Users and API Keys
- Individual tracking of API requests for each use case
- Simple key management with revocation or renewal of individual keys
- Monthly budgets (defined in euros) by key.
- Limiting the Available Endpoints by key.


### [Do Your First Request](https://aki.io/docs/getting-started/do-you-first-request/)

The platform features a model centric API. Every available AI model is exposed as an endpoint, service of similar use cases (Large Language Models, Image Generation) have a similar and comparable structure.

However, since different AI models often have their own capabilities and functions, there are differing or specialized parameters and inputs. This is where a model-specific API has major advantages, as features can be exposed directly as additional parameters. There is no need to adapt the entire API specification at any time.

AKI.IO also offers an OpenAI  and Anthropic API interfaces for connecting to it with a service-centered interface. This allows the service to be used as a drop-in replacement for existing OpenAI API or Anthropic API based tools. More information can be found here: [AKI.IO OpenAI API Interface ]and [AKI.IO Anthropic API Interface](/docs/compatibility/anthropic-api-compatibility/).

The API is designed as a bidirectional, real-time streaming interface, but it can also be used in a traditional blocking request–response mode.

All communication is performed exclusively via JSON messages. This includes binary data, which must be embedded directly within the JSON payload (for example, as Base64-encoded content). No additional transport channels or side channels are required.

Authentication is handled within the JSON payload itself. No HTTP headers are required for authentication. This design makes API calls straightforward, reliable, and proxy-safe.

The following example demonstrates a basic request using the curl command-line tool:


```bash
curl -X POST -H 'Content-Type: application/json' -d \
'{ "key":"fc3a8c50-b12b-4d6a-ba07-c9f6a6c32c37", "prompt_input":"Tell a joke", "wait_for_result": true}' \
https://aki.io/api/call/llama3_8b_chat
```


This example does a simple blocking call to the LLama3 8B LLM model endpoint (llama3_8b_chat), with a simple input prompt to “Tell a joke”, due the option “wait_for_result”, returning the complete response as JSON data:


```json
{
"text":"A man walked into a library and asked the librarian, \"Do you have any books on Pavlov's dogs and Schrödinger's cat?\" \n\nThe librarian replied, \"It rings a bell, but I'm not sure if it's here or not.\"",
"model_name":"Llama-3.1-8BInstruct",
"max_seq_len":65536,
"prompt_length":38,
"num_generated_tokens":55,
"current_context_length":93,
"success":true,
"total_duration":0.721,
"compute_duration":0.7
}
```


In addition to the response result, the API returns relevant metadata such as the number of input tokens, generated tokens, and the total compute duration.

That’s it — you’ve made your first request.

To use the full capabilities of the AKI.IO API, including real-time streaming, official client libraries are available for [Python](/docs/client-interfaces/python/) (via PyPI) and [Javascript](/docs/client-interfaces/javascript/). Additional SDKs and interfaces are currently under development and will be released soon.

If you need assistance integrating the AKI.IO API into your preferred programming language or application framework, please contact us at [support@aki.io](mailto:clients@aki.io).


**Advanced Agent Setup**: For a complete machine-readable configuration guide, see the [AI Coding Agents Integration Guide](/docs/compatibility/for-ai-coding-agents/).


### [Available Models](https://aki.io/docs/getting-started/available-models/)

> Overview of all models available through AKI.IO: LLMs, embedding models, and image generation models. All models use the same API key, and model IDs are identical across OpenAI-compatible and Anthropic-compatible endpoints. For the live list, call GET /v1/models.


## Available Models


All AKI.IO models are accessible with a single API key. The model IDs listed below work identically across OpenAI-compatible and Anthropic-compatible endpoints, so you can use the same value for the model parameter no matter which SDK format you prefer.


You can always retrieve the current live list via [GET https://aki.io/openai/v1/models](https://aki.io/openai/v1/models) (requires an API key).


### LLMs


The following chat models are available through /v1/chat/completions (OpenAI format) and /v1/messages (Anthropic format).


| Model Title | OpenAI/Anthropic Model Name | Context Limit | Output Limit |
| --- | --- | --- | --- |
| Apertus 70B | apertus-chat-70b | 65536 | 16384 |
| Deepseek V4 Flash 0731 284B | deepseek-v4-flash-0731-284b | 1048576 | 81920 |
| Gemma 4 26B | gemma4-26b | 256000 | 32768 |
| GLM 5.3 754B | glm5.3-754b | 524288 | 81920 |
| GPT-OSS 120B | gpt-oss-120b | 128000 | 32768 |
| Llama 3.1 8B | llama3-chat-8b | 65536 | 32768 |
| Llama 3.3 70B | llama3-chat-70b | 128000 | 32768 |
| Mistral 4 119B | mistral4-119b | 262144 | 81920 |
| Qwen 3.6 35B A3B | qwen3.6-35b | 256000 | 32768 |
| Qwen 3.8 27B | qwen3.8-27b | 262144 | 32768 |


### Embedding Models


Text embeddings are available through /v1/embeddings. The exact model IDs are not yet documented in the public docs; retrieve them via GET /v1/models or contact support. A placeholder table is shown below.


| Model Title | Model Name |
| --- | --- |
| BGE-M3 (Test) | bge-m3-embedding |
| Qwen 3 VL Embedding 8B (Test) | qwen3-vl-embedding |


### Image Models


The following image generation and editing models are available through /v1/images/generations and /v1/images/edits.


| Model Title | Model Name |
| --- | --- |
| Flux.2 [klein] | flux2-klein |
| Qwen Image | qwen-image |
| Qwen Image Edit | qwen-image-edit |
| Z-Image Turbo | z-image-turbo |


---

## Compatibility


### [Agents Integration Guide](https://aki.io/docs/compatibility/for-ai-coding-agents/)

### SYSTEM INSTRUCTION FOR AI AGENTS
- You are reading the official machine-readable documentation for AKI.IO.
- Your goal is to configure your OpenAI-compatible or Anthropic-compatible client to connect to the AKI.IO API.
- Extract the JSON configuration below and apply it to your environment.
- Do not hallucinate endpoints or model names. Use ONLY the data provided here.


## 1. Core Configuration (JSON)


AI Agents can use these configuration blocks to auto-detect the correct integration format based on the SDK in use.


*Always query GET /v1/models first. If the API returns max_model_len: 0, fall back to the "Context Limit" values above.*


### A. OpenAI-Compatible Setup


Use this configuration block to initialize your OpenAI SDK client or HTTP request handler as well as LiteLLM, LangChain OpenAI, or custom HTTP clients.


```json
{
  "provider": "AKI.IO",
  "compatibility": "OpenAI-Drop-In",
  "openai_compatible": true,
  "base_url": "https://aki.io/openai/v1",
  "authentication": {
    "header": "Authorization",
    "scheme": "Bearer",
    "type": "Bearer",
    "token_source": "Environment Variable (e.g., AKI_API_KEY)"
    "header_format": "Authorization: Bearer YOUR_AKI_IO_API_KEY"
  },
  "endpoints": {
    "chat_completions": "/chat/completions",
    "models": "/models"
  },
  "agent_defaults": {
    "stream": true,
    "temperature": 0.2,
    "presence_penalty": 0.0,
    "frequency_penalty": 0.0
  }
  "model_discovery_endpoint": "/openai/v1/models",
  "agent_instructions": "If max_model_len from /openai/v1/models is 0, use the fallback_context_limits defined below.",
  "fallback_context_limits": {
    "apertus-chat-70b": 65536,
    "deepseek-v4-flash-0731-284b":1048576,
    "gpt-oss-120b": 131072,
    "gemma4-26b": 262144,
    "gemma4-chat-26b": 262144,
    "glm5.3-754b": 524288,
    "llama3-chat-70b": 65536,
    "llama3-chat-8b": 65536,
    "mistral4-119b": 262144,
    "qwen3.6-35b": 262144,
    "qwen3.6-chat-35b": 262144,
    "qwen3.8-27b": 262144
  },
  "default_max_output_tokens": 8192
}
```


### B. Anthropic-Compatible Setup


Use this for Anthropic SDK, LiteLLM Anthropic, or applications expecting the Messages API format.


```json
{
  "provider": "AKI.IO",
  "compatibility": "Anthropic-Drop-In",
  "base_url": "https://aki.io/anthropic",
  "authentication": {
    "header": "x-api-key",
    "scheme": "direct",
    "note": "NO 'Bearer' prefix. Pass API key as raw value.",
    "token_source": "Environment Variable (e.g., AKI_API_KEY)"
  },
  "required_headers": {
    "anthropic-version": "2023-06-01",
    "content-type": "application/json"
  },
  "endpoints": {
    "messages": "/v1/messages",
    "model_discovery": "/v1/models"
  },
  "required_parameters": ["model", "messages", "max_tokens"],
  "system_prompt_handling": "Pass system prompt as top-level 'system' parameter, NOT as a message with role 'system'.",
  "agent_defaults": {
    "max_tokens": 8192,
    "temperature": 0.2,
    "stream": true
  },
  "agent_instructions": "Model discovery uses the OPENAI-compatible endpoint /v1/models (not Anthropic). If max_model_len is 0, use fallback_context_limits below.",
  "fallback_context_limits": {
    "apertus-chat-70b": 65536,
    "deepseek-v4-flash-0731-284b":1048576,
    "llama3-chat-70b": 131072,
    "llama3-chat-8b": 65536,
    "mistral4-119b": 262144,
    "gpt-oss-120b": 128000,
    "gemma4-26b": 262144,
    "gemma4-chat-26b": 262144,
    "glm5.3-754b": 524288,
    "qwen3.6-35b": 262144,
    "qwen3.6-chat-35b": 262144,
    "qwen3.8-27b": 262144
  },
  "default_max_output_tokens": 8192
}
```


## 2. Dynamic Model Discovery & Routing


AI Agents should not rely on hardcoded context limits. AKI.IO provides a machine-readable endpoint to discover the exact specifications of all available models at runtime.


### Fetching Live Limits (Recommended)


Query the models endpoint to get the real-time context windows and output limits.


```bash
curl -s https://aki.io/openai/v1/models \
  -H "Authorization: Bearer YOUR_AKI_IO_API_KEY"
```


You can use the following API key to access the API and query the list of models. Access is limited to a small number of calls per IP address.


```bash
curl -s https://aki.io/openai/v1/models/ \
  -H "Authorization: Bearer fc3a8c50-b12b-4d6a-ba07-c9f6a6c32c37"
```


### B. Semantic Routing (Static Table)


While the API provides *technical limits*, it does not provide *use-case recommendations*. Use this table to route tasks to the most capable model, then apply the limits fetched from the API above.


| API model ID | Best For | Tool Calling / JSON Mode |
| --- | --- | --- |
| apertus-chat-70b | Secure Code Review, GDPR-compliant tasks | ⚠️ Basic Support |
| deepseek-v4-flash-0731-284b | Massive Codebases, Cross-File Analysis, Long-Horizon Coding, Agent Swarm Mode, Multimodal | ✅ Supported |
| gpt-oss-120b | Instruction Following, Agentic Loops | ✅ Supported |
| gemma4-chat-26b | Lightweight Tasks, Fast IDE Autocomplete, Multimodal | ✅ Supported |
| kimi-k2.7-code-1100b | Massive Codebases, Cross-File Analysis, Long-Horizon Coding, Agent Swarm Mode, Multimodal | ✅ Supported |
| llama3-chat-8b | Edge-Case Routing, Quick Classifications | ⚠️ Basic Support |
| llama3-chat-70b | General Coding, Fast Chat, Summarization | ⚠️ Basic Support |
| minimax-m2.5-230b | Massive Codebases, Cross-File Analysis, Concise Answers | ✅ Supported |
| mistral4-119b | Edge-Case Routing, Quick Classifications | ✅ Supported |
| qwen3.6-chat-35b | Complex Code Reasoning, Refactoring, Architecture, Multimodal | ✅ Supported |
| qwen3.8-chat-27b | Complex Code Reasoning, Refactoring, Architecture, Multimodal | ✅ Supported |


## 3. Agent-Specific Instructions


### A. Streaming is Mandatory


Always set "stream": true in your POST /v1/chat/completions payload. The AKI.IO API supports Server-Sent Events (SSE). Parse the data: [DONE] marker to close the connection cleanly.


### B. Tool Calling (Function Calling)


AKI.IO supports OpenAI-standard tool calling.
- Pass your tools in the tools array.
- Use "tool_choice": "auto" to let the model decide.
- **Important:** When executing a tool, append the tool result as a message with role: "tool" and the matching tool_call_id.


### C. JSON Mode (Structured Output)


If you need to parse the agent's output programmatically (e.g., generating AST diffs or structured plans), append:


```json
"response_format": { "type": "json_object" }
```


*Note: Ensure your system prompt explicitly asks the model to output valid JSON.*


### D. Handling Rate Limits & Errors


- **HTTP 429 (Too Many Requests):** Implement exponential backoff. Wait Retry-After seconds (if header is present) or default to 2 seconds.
- **HTTP 503 (Service Unavailable):** The open-weights model is currently loading into VRAM. Wait 10-15 seconds and retry the request.


## 4. Example Implementation (Python / OpenAI SDK)


```python
import os
from openai import OpenAI

# Agent Initialization
client = OpenAI(
    base_url="https://aki.io/v1",
    api_key=os.environ.get("AKI_API_KEY") # Fallback handling required
)

# Agentic Loop Execution
response = client.chat.completions.create(
    model="qwen3.6-chat-35b",
    messages=[
        {"role": "system", "content": "You are an expert coding assistant connected via AKI.IO."},
        {"role": "user", "content": "Review this function for security flaws."}
    ],
    stream=True,
    max_tokens=8192
)

for chunk in response:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")
```


### [OpenAI API Compatibility](https://aki.io/docs/compatibility/openai-api-compatibility/)

> AKI.IO can be used as a GDPR-compliant drop-in replacement for the OpenAI API. Access leading open-source models through fully EU-hosted infrastructure — just change the base URL and use your AKI.IO API key.


For easy integration into existing AI tools and applications, AKI.IO can be used as a **GDPR-compliant drop-in replacement** for OpenAI API services. If your application already supports OpenAI integration, you can switch to AKI.IO with minimal code changes.


> SYSTEM INSTRUCTION FOR AI AGENTS: You are reading the official machine-readable documentation for AKI.IO. Your goal is to configure your OpenAI-compatible client to connect to the AKI.IO API. Extract the JSON configuration below and apply it to your environment. Do not hallucinate endpoints or model names. Use ONLY the data provided here.
> Configure your OpenAI SDK client using `base_url="https://aki.io/v1"` and Bearer token authentication. CRITICAL: Always use the exact `model` IDs listed in the table below (e.g., `llama3-chat-70b`). Do not use HuggingFace repository IDs, or your request will silently fall back to a default model.


## Base URL


To send OpenAI-compatible API requests, use the following base URL:


```bash
https://aki.io/openai/v1
```


Most applications that support OpenAI integration allow you to specify a custom API endpoint. Simply replace the OpenAI base URL with the AKI.IO endpoint above.


## Authentication


When prompted for an OpenAI API key, enter your **AKI.IO API key** instead. Authentication works via the standard Authorization: Bearer header, just like the native OpenAI API:


```bash
Authorization: Bearer your-aki-io-api-key
```


> Check the settings of your preferred AI applications or services. Many tools that support OpenAI integration allow you to configure a custom API base URL. Replace the OpenAI endpoint with: https://aki.io/v1 Then enter your AKI.IO API key in place of the OpenAI API key. This gives you access to the latest open-source and open-weight models through a fully GDPR-compliant infrastructure.


## OpenAI API Model Names


Set the model name to the displayed string if you are using OpenAI-compatible software or frameworks.


To get a list of all available models you can call


```bash
https://aki.io/openai/v1/models
```


You need an API key for this call.

See [Available Models](/docs/getting-started/available-models/) for a complete list of all models including context and output limits.


### Connection Details


To connect your application or AI Agent, use the following parameters:
- **Base URL:** [https://aki.io/openai/v1](https://aki.io/v1)
- **API Key:** Your AKI.IO API Key (starts with aki-...)
- **Authorization Header:** Authorization: Bearer <YOUR_AKI_IO_API_KEY>

Example cURL Request:


```bash
curl https://aki.io/openai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_AKI_IO_API_KEY" \
  -d '{
    "model": "llama3-chat-70b",
    "messages": [{"role": "user", "content": "Hello!"}],
    "max_tokens": 8192
  }'
```


## Supported OpenAI Endpoints


### /v1/models


Returns a list of all available AKI.IO language-model resources for your API key. The response is a JSON payload containing an array of model objects with id, object, created, and owned_by fields.


```bash
curl https://aki.io/openai/v1/models \
  -H "Authorization: Bearer your-aki-io-api-key"
```


### /v1/chat/completions


Generates conversational replies. Instead of OpenAI's GPT models, any available LLM from AKI.IO can be selected as model. Send a POST request with a list of message objects — each with a role (system, user, or assistant) and content — plus optional parameters. Response streaming is supported via the stream parameter.


### /v1/embeddings


The embeddings API endpoint converts text, and depending on the embedding model also images and audio, into a numerical vector a so called embedding. The embedding encodes meaning of the input as a numerical representation. Input data with similar semantic meaning will have a numerical close representation with a short vector distance. The use case for embeddings are:
- **Semantic search** — find documents related to a query even if they don’t share the same keywords. By comparing all occurrences with a distance below a certain threshold.
- **Clustering** — group similar items automatically.
- **Recommendations** — suggest content similar to what a user likes.
- **Classification** — feed embeddings into a machine-learning model.
- **Anomaly detection** — spot outliers based on meaning.


### /v1/images/generations


Creates images from textual prompts using the latest diffusion models. Send a POST request with a prompt and optional parameters such as n (number of images) and size (resolution like 256x256, 512x512, 1024x1024). The response contains a data array with base-64-encoded images.


### /v1/images/edits


Modifies an existing image according to a textual instruction. Replace colors or objects, transform the image style, exchange the background, or show the image from a different angle.


## Chat Completions Parameters


### model


**Type:** string — The model identifier to use. Use any available AKI.IO model name (e.g., llama3-chat-70b, qwen3.6-35b). Retrieve the full list via /v1/models.


### messages


**Type:** array of objects — Conversation history as message objects. Each message has a role (system, user, or assistant) and content (the message text).


### temperature


**Type:** float (0.0 – 2.0) — Controls randomness. Lower values produce more deterministic outputs; higher values produce more creative responses. Defaults to 1.0.


### max_tokens


**Type:** integer — Maximum number of tokens to generate in the response. Controls output length.


### stream


**Type:** boolean — Set to true to receive server-sent events (SSE) with incremental token deltas, enabling real-time streaming of the response.


### stop


**Type:** string or array of strings — Up to 4 sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence.


## Example Requests


### Basic Chat Completion


```bash
curl https://aki.io/openai/v1/chat/completions \
  -H "Authorization: Bearer your-aki-io-api-key" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gemma4-26b",
    "messages": [
      {"role": "system", "content": "You are a helpful assistant."},
      {"role": "user", "content": "Explain quantum computing in simple terms."}
    ],
    "temperature": 0.7,
    "max_tokens": 1024
  }'
```


### Streaming Chat Completion


```bash
curl https://aki.io/openai/v1/chat/completions \
  -H "Authorization: Bearer your-aki-io-api-key" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gemma4-26b",
    "stream": true,
    "messages": [
      {"role": "user", "content": "Write a haiku about programming."}
    ]
  }'
```


### Image Generation


```bash
curl https://aki.io/openai/v1/images/generations \
  -H "Authorization: Bearer your-aki-io-api-key" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "z-image-turbo",
    "prompt": "A sunset over the Alps in watercolor style",
    "n": 1,
    "size": "1024x1024"
  }'
```


### Using the OpenAI Python SDK


Use the official OpenAI Python SDK with AKI.IO by changing the base_url and providing your AKI.IO API key:


```python
from openai import OpenAI

client = OpenAI(
    base_url="https://aki.io/openai/v1",
    api_key="your-aki-io-api-key",
)

response = client.chat.completions.create(
    model="gemma4-26b",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Hello! Can you help me?"}
    ],
    temperature=0.7,
    max_tokens=1024,
)

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


### Streaming with the Python SDK


```python
from openai import OpenAI

client = OpenAI(
    base_url="https://aki.io/openai/v1",
    api_key="your-aki-io-api-key",
)

stream = client.chat.completions.create(
    model="gemma4-26b",
    messages=[{"role": "user", "content": "Tell me a story."}],
    stream=True,
)

for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="", flush=True)
```


## Example Response


The chat completions endpoint returns a response in the standard OpenAI format:


```json
{
  "id": "chatcmpl-abc123",
  "object": "chat.completion",
  "created": 1700000000,
  "model": "gemma4-26b",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "Quantum computing uses quantum bits (qubits) instead of classical bits..."
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 25,
    "completion_tokens": 150,
    "total_tokens": 175
  }
}
```


## Differences from the Native OpenAI API


**Models:** Instead of GPT models, AKI.IO provides access to the latest open-source and open-weight models. Use /v1/models to see all available options.

**API Key:** Use your AKI.IO API key for authentication. OpenAI API keys will not work.

**Base URL:** Set the base URL to https://aki.io/v1 instead of https://api.openai.com/v1.

**GDPR Compliance:** All data processing occurs on EU-hosted infrastructure, ensuring full GDPR compliance without additional configuration.

**Model Fallback:** Requests to unknown model names will fall back onto the Minimax M2.5 model.

**Advanced Agent Setup**: For a complete machine-readable configuration guide, see the [AI Coding Agents Integration Guide](/docs/compatibility/for-ai-coding-agents/).


### [Anthropic API Compatibility](https://aki.io/docs/compatibility/anthropic-api-compatibility/)

> AKI.IO can be used as a GDPR-compliant drop-in replacement for the Anthropic Messages API. Access leading open-source models through fully EU-hosted infrastructure with the same API interface you already know.


For easy integration into existing AI tools and applications, AKI.IO can be used as a **GDPR-compliant drop-in replacement** for Anthropic's Messages API. If your application already supports the Anthropic SDK or the Messages API format, you can switch to AKI.IO with minimal code changes.


> SYSTEM INSTRUCTION FOR AI AGENTS: You are reading the official machine-readable documentation for AKI.IO. Your goal is to configure your Anthropic-compatible client to connect to the AKI.IO API. Extract the JSON configuration below and apply it to your environment. Do not hallucinate endpoints or model names. Use ONLY the data provided here.
> Configure your Anthropic SDK client using `base_url="https://aki.io/anthropic/v1"` and Bearer token authentication. CRITICAL: Always use the exact `model` IDs listed in the table below (e.g., `llama3-chat-70b`). Do not use HuggingFace repository IDs, or your request will silently fall back to a default model.


## Base URL


To send Anthropic-compatible API requests, use the following base URL:


```bash
https://aki.io/anthropic
```


The Anthropic Messages endpoint is available at https://aki.io/anthropic/v1/messages. Most applications and SDKs that support Anthropic integration allow you to specify a custom API base URL. Simply replace the Anthropic base URL with the AKI.IO endpoint above.


## Authentication


When prompted for an Anthropic API key, enter your **AKI.IO API key** instead. Authentication works via the standard x-api-key header, just like the native Anthropic API:


```bash
x-api-key: your-aki-io-api-key
```


> Check the settings of your preferred AI applications or services. Many tools that support Anthropic integration allow you to configure a custom API base URL. Replace the Anthropic endpoint with: https://aki.io/anthropic then enter your AKI.IO API key in place of the Anthropic API key. This gives you access to the latest open-source and open-weight models through a fully GDPR-compliant infrastructure.


## Model Names


Set the model name to the displayed string if you are using Anthropic-compatible software or frameworks.


You need an API key for this call.

See [Available Models](/docs/getting-started/available-models/) for a complete list of all models including context and output limits.


To get a list of all available models you can call


```bash
https://aki.io/anthropic/v1/models
```


## Supported Anthropic Endpoints


### /messages


The **/v1/messages** endpoint is the primary API for generating conversational replies. Instead of Anthropic's Claude models, any available LLM from AKI.IO can be selected via the model parameter. You send a POST request with a list of message objects and optional parameters such as temperature, max_tokens, top_p, top_k, and stop_sequences. The API returns a JSON response containing the generated message with role assistant. Response streaming is supported via the stream parameter.


## Request Parameters


### model


**Type:** string — The model identifier to use for generation. Use any of the available AKI.IO model names (e.g., llama3-chat-70b, qwen3.6-35b). Retrieve the full list via the Anthropic-compatible /v1/models endpoint.


### messages


**Type:** array of objects — An array of message objects representing the conversation history. Each message has a role (user or assistant) and content (the message text). The API processes the full conversation context to generate the next assistant response.


### max_tokens


**Type:** integer — The maximum number of tokens to generate in the response. This parameter is **required** by the Anthropic Messages API. Set this to control the length of the model's output.


### temperature


**Type:** float (0.0 – 1.0) — Controls randomness in the output. Lower values produce more focused, deterministic responses. Higher values produce more creative, varied outputs. Defaults to 1.0.


### top_p, top_k


**top_p** (float, 0.0–1.0): Nucleus sampling — the model considers only tokens comprising the top top_p probability mass. Use top_p or temperature, but not both.

**top_k** (integer): Only sample from the top K options for each subsequent token. Limits the model's choices to the highest-probability tokens, which can help reduce nonsensical outputs.


### stop_sequences


**Type:** array of strings — A list of strings that will cause the model to stop generating. If the model outputs any of these strings, generation stops at that point. Useful for controlling the format or length of responses.


### stream


**Type:** boolean — Set to true to enable streaming responses. When enabled, the API returns server-sent events (SSE) with incremental message deltas, allowing you to process partial results in real time.


### system


**Type:** string — A system prompt that sets the behavior and context for the assistant. This is equivalent to the system role message in OpenAI's API but is passed as a top-level parameter in the Anthropic Messages API format. Use this to define the assistant's personality, constraints, or task instructions.


## Example Requests


### Basic Messages Request


```bash
curl https://aki.io/anthropic/v1/messages \
  -H "x-api-key: your-aki-io-api-key" \
  -H "content-type: application/json" \
  -H "anthropic-version: 2023-06-01" \
  -d '{
    "model": "gemma4-26b",
    "max_tokens": 1024,
    "messages": [
      {"role": "user", "content": "Explain quantum computing in simple terms."}
    ]
  }'
```


### Request with System Prompt


```bash
curl https://aki.io/anthropic/v1/messages \
  -H "x-api-key: your-aki-io-api-key" \
  -H "content-type: application/json" \
  -H "anthropic-version: 2023-06-01" \
  -d '{
    "model": "gemma4-26b",
    "max_tokens": 2048,
    "system": "You are a helpful technical assistant. Answer concisely.",
    "temperature": 0.7,
    "messages": [
      {"role": "user", "content": "What is the difference between REST and GraphQL?"},
      {"role": "assistant", "content": "REST uses fixed endpoints with defined data structures..."},
      {"role": "user", "content": "Which one should I use for a new project?"}
    ]
  }'
```


### Using the Anthropic Python SDK


You can use the official Anthropic Python SDK with AKI.IO by changing the base_url and providing your AKI.IO API key:


```python
import anthropic

client = anthropic.Anthropic(
    base_url="https://aki.io/anthropic",
    api_key="your-aki-io-api-key",
)

message = client.messages.create(
    model="gemma4-26b",
    max_tokens=1024,
    system="You are a helpful assistant.",
    messages=[
        {"role": "user", "content": "Hello! Can you help me?"}
    ],
)

print(message.content)
```


### Streaming with the Python SDK


```python
import anthropic

client = anthropic.Anthropic(
    base_url="https://aki.io/anthropic",
    api_key="your-aki-io-api-key",
)

with client.messages.stream(
    model="gemma4-26b",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Tell me a story."}
    ],
) as stream:
    for text in stream.text_stream:
        print(text, end="", flush=True)
```


The Anthropic Python SDK often expects only the host portion as the `base_url` and constructs the rest itself.

AKI.IO API internally appends `/v1/messages`, so we recommend using `https://aki.io/anthropic` for the base_url instead.


## Example Response


The API returns a response in the standard Anthropic Messages format:


```json
{
  "id": "msg_01XFDUDYJgAACzvnptvVoYEL",
  "type": "message",
  "role": "assistant",
  "content": [
    {
      "type": "text",
      "text": "Quantum computing uses quantum bits (qubits) instead of classical bits..."
    }
  ],
  "model": "gemma4-26b",
  "stop_reason": "end_turn",
  "stop_sequence": null,
  "usage": {
    "input_tokens": 25,
    "output_tokens": 150
  }
}
```


## Differences from the Native Anthropic API


**Models:** Instead of Claude models, AKI.IO provides access to the latest open-source and open-weight models. Use the /v1/models endpoint to see all available options.

**API Key:** Use your AKI.IO API key for authentication. Anthropic API keys will not work.

**Base URL:** Set the base URL to https://aki.io/anthropic instead of https://api.anthropic.com.

**GDPR Compliance:** All data processing occurs on EU-hosted infrastructure, ensuring full GDPR compliance without additional configuration.

**anthropic-version header:** The anthropic-version header is accepted but not required. AKI.IO will respond regardless of the version string provided.

**Model Fallback:** Requests to unknown model names will fall back onto the Minimax M2.5 model.

**Advanced Agent Setup**: For a complete machine-readable configuration guide, see the [AI Coding Agents Integration Guide](/docs/compatibility/for-ai-coding-agents/).


---

## Client Interfaces


### [Javascript](https://aki.io/docs/client-interfaces/javascript/)

> AKI.IO Javascript Interface | AKI.IO Docs


Alle required calls to the AKI.IO API are contained in the [aki-io.js](https://github.com/aki-io-labs/aki-io/blob/main/js/aki_io.js) Javascript file.

It has all required methods for authentication, doing API calls and handling streaming.

For in-house or local web services **aki-io.js** can directly included in your HTML files with following line:


```html
<script src="/static/js/aki_io.js"></script>
```


> Bear in mind that this should never be done on public websites, as your API key will get exposed and can easily be misused with out your permission. Please keep your API key always secret!


It is more useful to include the **aki-io.js** Javascript in your backend, which acts as a proxy to your clients, and can be used like a npm package by including it with the following line, and having a local copy of [aki-io.js](https://github.com/aki-io-labs/aki-io/blob/main/js/aki_io.js) in the specified path.


```javascript
const { Aki, doAPIRequest } = require('/js/aki_io');
```


> An official Javascript npm package is in the works and will soon be available.


### Simple LLM Chat Example


A complete node Javascript example to do a blocking AKI.IO LLM request. To run the node [llm_simple_example.js](https://github.com/aki-io-labs/aki-io/blob/main/js/examples/llm_simple_example.js) example execute:


```bash
node llm_simple_example.js
```


```javascript
const { Aki, doAPIRequest } = require('../aki_io');

const ENDPOINT = 'llama3_8b_chat';
const API_KEY = 'fc3a8c50-b12b-4d6a-ba07-c9f6a6c32c37';

const chatContext = [
    {role: 'system', content: 'You are a helpful assistant named AKI.' },
    {role: 'assistant', content: 'How can I help you today?' },
    {role: 'user', content: 'Tell me a joke' }
];

const params = {
    chat_context: JSON.stringify(chatContext),
    top_k: 40,
    top_p: 0.9,
    temperature: 0.8,
    max_gen_tokens: 1000
};

doAPIRequest(
    ENDPOINT,
    API_KEY,
    params,
    (result) => {
        if (result.success) {
            console.log('\nAPI JSON response:', result);
            console.log('\nChat response:\n', result.text);
            console.log('\nGenerated Tokens:', result.num_generated_tokens);
        }
        else {
            console.error('API Error:', result.error_code, '-', result.error);
        }
    }
);
```


### Streaming LLM Chat Example


A complete node Javascript example to do a streaming AKI.IO LLM request. To run the node [llm_stream_example.js](https://github.com/aki-io-labs/aki-io/blob/main/js/examples/llm_stream_example.js) example execute:


```bash
node llm_stream_example.js
```


```javascript
const { Aki, doAPIRequest } = require('../aki_io');

const aki = new Aki('llama3_8b_chat', 'fc3a8c50-b12b-4d6a-ba07-c9f6a6c32c37')

const chatContext = [
    {role: 'system', content: 'You are a helpful assistant named AKI.' },
    {role: 'assistant', content: 'How can I help you today?' },
    {role: 'user', content: 'Tell me a funny story with more than 100 words' }
];

const params = {
    chat_context: JSON.stringify(chatContext),
    top_k: 40,
    top_p: 0.9,
    temperature: 0.8,
    max_gen_tokens: 1000
};

output_position = 0

console.log('\n🤖 Assistant: ');

// Make the API request
aki.doAPIRequest(
    params,
    (result) => {
        if (!result || result.success === false) {
            console.error('❌ API Error:', result.error || 'Unknown error');
            return;
        }

        process.stdout.write(result.text.slice(output_position) + '\n');

        console.log('\n📊 Stats:', {
            'Generated Tokens': result.num_generated_tokens,
            'Compute Duration': `${result.compute_duration.toFixed(2)}s`,
            'Total Duration': `${result.total_duration.toFixed(2)}s`
        });

        console.log('\n✨ Chat completed!');
    },
    (progress, progress_data) => {
        if(progress_data) {
            text = progress_data.text
            process.stdout.write(text.slice(output_position));
            output_position = text.length 
        }
    }
);
```


More Javascript examples to use AKI.IO for various use cases and model types can be found [here](https://github.com/aki-io-labs/aki-io/tree/main/js/examples)


### [Python](https://aki.io/docs/client-interfaces/python/)

> AKI.IO Python Interface | AKI.IO Docs


We provide an official AKI.IO Python Client Pip package that wraps all available AKI.IO models and streaming functionality in an easy to use but powerful Python interface.


### Install the aki-io PIP


Installation of the aki-io PIP package is done by following command line.


```bash
pip install aki-io
```


The PIP package requires Python version 3.8 or higher. The aki-io pip only depends on the **requests** and **aiohttp** pips for doing synchronous or respectively asynchronous HTTPS requests depending on the interface you prefer. They additional pips will be automatically installed in case they are not already available in your Python environment.


### Simple LLM Chat Example


This is the simplest way to call the AKI.IO API. Fill in the input parameters in a Dict object, call the API and get the results. The example show a chat request with an instruct chat context to the LLama3 8b Chat endpoint.


```python
from aki_io import Aki

aki = Aki('llama3_8b_chat', 'fc3a8c50-b12b-4d6a-ba07-c9f6a6c32c37')

chat_context = [
    {"role": "system", "content": "You are a helpful assistant named AKI."},
    {"role": "assistant", "content": "How can I help you today?"},
    {"role": "user", "content": "Tell me a joke"},
]

params = {
    "chat_context": chat_context,
    "top_k": 40,
    "top_p": 0.9,
    "temperature": 0.8,
    "max_gen_tokens": 1000,
}

    
result = aki.do_api_request(params) # Do the API call and wait for result
if result['success']:
    print("API JSON response:\n", result)
    print("\nChat response:\n", result['text'])
    print("\nGenerated Tokens:", result['num_generated_tokens'], )
else:
    print("API Error:", result.get('error_code'), "-", result.get('error'))
```


### LLM Streaming Chat Example with Callbacks


To use the streaming function a simple way is to use **aki-io** PIP callback mechanism. The progress_callback(...) function is called as soon as new chat output was received. The main aki.do_api_request(...) call blocks until all data was received and the function returns with the final result. This is an easy way to provide progress updates to the user without having to use the Python Async framework.


```python
from aki_io import Aki

aki = Aki('llama3_8b_chat', 'fc3a8c50-b12b-4d6a-ba07-c9f6a6c32c37')

chat_context = [
    {"role": "system", "content": "You are a helpful assistant named AKI."},
    {"role": "assistant", "content": "How can I help you today?"},
    {"role": "user", "content": "Tell me a funny store with more than 100 words"},
]

params = {
    "chat_context": chat_context,
    "top_k": 40,
    "top_p": 0.9,
    "temperature": 0.8,
    "max_gen_tokens": 1000,
}

output_position = 0

def progress_callback(progress, progress_data):
    global output_position
    if progress_data and 'text' in progress_data:
        text = progress_data.get('text')
        print(text[output_position:], end='', flush=True)
        output_position = len(text)

result = aki.do_api_request(params, progress_callback) # Do the API call and stream the output
if result['success']:
    print("\nAPI JSON response:\n", result)
    print("\nFinal Chat response:\n", result['text'])
    print("\nGenerated Tokens:", result['num_generated_tokens'], )
else:
    print("API Error:", result.get('error_code'), "-", result.get('error'))
```


## LLM Chat with Async Callbacks


To run a chat stream in the background while continue to process other requests or to run multiple chat requests the **aki-io** PIP supports Pythons [asyncio](https://docs.python.org/3/library/asyncio.html) interface. The API request can be started with asyncio.create_task(…) function. All result processing and error handling is done through the callback functions.


```python
import asyncio
from aki_io import Aki

aki = Aki('llama3_8b_chat', 'fc3a8c50-b12b-4d6a-ba07-c9f6a6c32c37')

chat_context = [
    {"role": "system", "content": "You are a helpful assistant named AKI."},
    {"role": "assistant", "content": "How can I help you today?"},
    {"role": "user", "content": "Tell me a funny store with more than 100 words"},
]

params = {
    "chat_context": chat_context,
    "top_k": 40,
    "top_p": 0.9,
    "temperature": 0.8,
    "max_gen_tokens": 4000,
}

output_position = 0

def progress_callback(progress, progress_data):
    global output_position
    if progress_data and 'text' in progress_data:
        text = progress_data.get('text')
        print(text[output_position:], end='', flush=True)
        output_position = len(text)

def result_callback(result):
    if result['success']:
        print(result.get('text')[output_position:], end='', flush=True)    
        print("\n\nGenerated Tokens:", result['num_generated_tokens'], )
    else:
        print("API Error:", result.get('error_code'), "-", result.get('error'))            


# use create_task() instead of run() to fire in background
asyncio.run(aki.do_api_request_async(
        params,
        result_callback,
        progress_callback # optional
    )) 


asyncio.run(aki.close_session())
```


### Most Pythonic LLM Streaming with Generator Interface


The most "Pythonic" solution for doing streaming with the **aki-io** Pip is the Python generator pattern. The results are read like a file stream from the generator requests whereby the return types of the iterator can vary depending on the state of the stream. The results differ in tuples of **progress_info** and **progress_data** objects and then the final **result** dictionary.

All processing logic is nicely kept within a single loop. The generator can be of course run in an async function to run multiple generators in parallel.


```python
import asyncio
from aki_io import Aki

aki = Aki('llama3_8b_chat', 'fc3a8c50-b12b-4d6a-ba07-c9f6a6c32c37')

chat_context = [
    {"role": "system", "content": "You are a helpful assistant named AKI."},
    {"role": "assistant", "content": "How can I help you today?"},
    {"role": "user", "content": "Tell me a funny store with more than 100 words"},
]

params = {
    "chat_context": chat_context,
    "top_k": 40,
    "top_p": 0.9,
    "temperature": 0.8,
    "max_gen_tokens": 1000
}


async def do_example_request():
    output_generator = aki.get_api_request_generator(params)
    
    try:
        async for result in output_generator:
            if isinstance(result, tuple) and len(result) == 2:
                progress_info, progress_data = result
                print(f"Progress: {progress_info} - {progress_data}")
            else:
                print(f"Result: {result}")
    except Exception as e:
        print(f"Error occurred: {e}")


# use create_task() instead of run() to fire in background
asyncio.run(do_example_request()) 

asyncio.run(aki.close_session())
```


The above examples demonstrate the available patterns how to use the **aki-io** PIP. Use the pattern that suits your use case best.

The same patterns can be used on all type of AKI.IO endpoints only the send **input params** and return **progress_data** and **output data** will depend and differ on the type of the endpoint.

The expected input parameters and resulting output parameters are described in detail for [LLMs here](/docs/llm-chat/llm-request-parameters/) and [Image Generators here](/docs/image-gen/imagegen-request-parameters/). More endpoint types will be soon available.


### [PHP](https://aki.io/docs/client-interfaces/php/)

> AKI.IO PHP Interface | AKI.IO Docs


AKI.IO provides an official PHP client library that wraps all available AKI.IO models and streaming functionality in an easy-to-use yet powerful PHP interface.


### Installation via Composer


Install the aki-io PHP package via Composer with the following command:


```bash
composer require aki-io/aki-io
```


or alternatively


```bash
cd php
composer install
```


Alternatively, the library can be included manually:


```javascript
require_once 'path/to/src/Aki.php';
require_once 'path/to/src/functions.php';
```


The package requires **PHP 8.2 or higher** and the extensions ***ext-curl*** and ***ext-json***.


### Simple LLM Chat Example


This is the simplest way to call the AKI.IO API. Pass input parameters as an array, call the API, and receive the results. The example below shows a chat request with instruct-chat context to the LLama3 8b Chat endpoint.


```javascript
<?php
use AkiIO\Aki;

$aki = new Aki('llama3_8b_chat', 'your-api-key');

$result = $aki->doApiRequest([
    'chat_context' => [
        ['role' => 'user', 'content' => 'Tell me a joke']
    ],
    'max_gen_tokens' => 200,
]);

if ($result['success']) {
    echo $result['text'];
}
```


### LLM Streaming Chat Example with Callbacks


To use the streaming functionality, the **aki-io** PHP package provides a callback mechanism. The ***$progressCallback(...)*** function is invoked whenever new chat output chunks are received. The main call ***doApiRequest(...)*** blocks until all data has been received and the function returns with the final result. This is a simple way to provide progress updates to the user without requiring a PHP async framework.


```javascript
<?php
use AkiIO\Aki;

$aki = new Aki('llama3_8b_chat', 'your-api-key');

$outputPosition = 0;

$progressCallback = function ($progressInfo, $progressData) use (&$outputPosition) {
    if ($progressData && isset($progressData['text'])) {
        echo substr($progressData['text'], $outputPosition);
        $outputPosition = strlen($progressData['text']);
    }
};

$result = $aki->doApiRequest([
    'chat_context' => [
        ['role' => 'user', 'content' => 'Write a short story']
    ],
], $progressCallback);
```


### Asynchronous Processing & Background Jobs


PHP executes synchronously by default. For long-running tasks (e.g., high-resolution image generation or long LLM contexts), blocking the web request is not recommended. Instead, dispatch the job to a background worker or CLI script. The aki-io client works identically in CLI environments. Below is an example of decoupling the submission from the processing.

**1. Web Controller (Dispatch Job)**

In your web application, store the task parameters and trigger a background process. Do not wait for the result here.


```javascript
<?php
// web/index.php
use AkiIO\Aki;

// Store task in database...
$taskId = saveTaskToDB(['prompt' => 'Complex image...', 'status' => 'pending']);

// Trigger background worker (non-blocking)
exec("php worker.php {$taskId} > /dev/null &");

// Return immediate response to user
echo json_encode(['status' => 'processing', 'task_id' => $taskId]);
```


**CLI Worker (Process Job)**

Run this script via CLI or a queue worker (e.g., Laravel Queue, Symfony Messenger). It handles the blocking API call without affecting web server timeouts.

**Variant 1: Simple Blocking Worker**

Use ***doApiRequest()*** for straightforward background processing. The worker blocks until the result is ready.


```javascript
<?php
// worker.php
use AkiIO\Aki;

require_once 'vendor/autoload.php';

$taskId = $argv[1];
$task = getTaskFromDB($taskId);

$aki = new Aki('z_image_turbo', getenv('AKI_API_KEY'));

$result = $aki->doApiRequest([
    'prompt' => $task['prompt'],
    'width' => 1024,
    'height' => 1024,
]);

if ($result['success']) {
    saveResult($taskId, $result['images']);
    updateTaskStatus($taskId, 'completed');
} else {
    updateTaskStatus($taskId, 'failed', $result['error']);
}
```


**Variant 2: Worker with Progress Tracking & Cancellation**

Use ***doApiRequestAwait()*** with a progress callback to enable real-time progress updates and optional cancellation. This variant requires the worker to stay active during processing.


```javascript
<?php
// worker_with_progress.php
use AkiIO\Aki;

require_once 'vendor/autoload.php';

$taskId = $argv[1];
$task = getTaskFromDB($taskId);

$aki = new Aki('z_image_turbo', getenv('AKI_API_KEY'));

$result = $aki->doApiRequestAwait([
    'prompt' => $task['prompt'],
    'width' => 1024,
    'height' => 1024,
], function ($progressInfo, $progressData) use ($taskId, $aki) {
    // Update progress in your database/UI
    updateTaskProgress($taskId, $progressInfo['progress']);

    // Optional: Support external cancellation (e.g., via DB flag)
    if (isTaskCanceled($taskId)) {
        $aki->cancelRequest($progressInfo['job_id']);
    }
});

if ($result['success']) {
    saveResult($taskId, $result['images']);
    updateTaskStatus($taskId, 'completed');
} else {
    updateTaskStatus($taskId, 'failed', $result['error']);
}
```


**Progress Callback Parameters**

The callback function receives two arguments:


| Parameter | Type | Description |
| --- | --- | --- |
| $progressInfo | array | Contains job_id, progress (0-100), queue_position, estimate, job_state, success |
| $progressData | ?array | Partial results (e.g., ['text' => '...'] for LLM streaming) or null |


**Cancellation Flow**
- Set a cancellation flag in your database/UI (e.g., *isTaskCanceled($taskId) returns true*).
- The worker checks this flag inside the progress callback.
- If canceled, ***cancelRequest()*** is called to stop processing on the server. Handing over *$jobId* is optional, but it is recommended to pass it explicitly.
- The API returns a result with success = false and an appropriate error message.

**Web Dispatcher (unchanged)**

Both worker variants are triggered the same way from your web layer:


```javascript
<?php
// web/index.php
$taskId = saveTaskToDB(['prompt' => 'Complex image...', 'status' => 'pending']);

// Trigger worker (choose variant: worker.php or worker_with_progress.php)
exec("php worker_with_progress.php {$taskId} > /dev/null &");

echo json_encode(['status' => 'processing', 'task_id' => $taskId]);
```


**3. Checking Job Status**

If the API returns a job_id, you can store it to manage the task lifecycle (e.g., cancellation) even if the processing happens elsewhere.


```javascript
<?php
use AkiIO\Aki;

$aki = new Aki('llama3_8b_chat', 'your-api-key');

// Example: Cancel a long-running job by ID
$aki->cancelRequest($storedJobId);
```


> ⚠️ Timeouts: When running in a web context, ensure your PHP max_execution_time is sufficient or use the background job pattern above to avoid gateway timeouts.


### Image Generation


The same patterns can be used for all AKI.IO endpoints. Here is an example for image generation:


```javascript
<?php
use AkiIO\Aki;

$aki = new Aki('z_image_turbo', 'your-api-key');

$result = $aki->doApiRequest([
    'prompt' => 'A cute robot on a beach',
    'width' => 512,
    'height' => 512,
]);

foreach ($result['images'] ?? [] as $idx => $imageData) {
    $decoded = Aki::decodeBinary($imageData);
    file_put_contents("image_{$idx}.png", $decoded[1]);
}
```


### Configuration Options


When creating a new Aki instance, the following options can be passed:


| Option | Type | Default | Description |
| --- | --- | --- | --- |
| api_server | string | https://aki.io | Base URL of the API |
| output_binary_format | string | base64 | Output format for binary data (base64 or raw) |
| raise_exceptions | bool | false | Throw exceptions on errors |
| progress_interval | float | 0.2 | Polling interval for progress updates (seconds) |
| return_tool_call_dict | bool | false | Return tool calls as array instead of JSON string |


### Available Methods


Instance Methods of the Aki Class


| Method | Description |
| --- | --- |
| doApiRequest($params, $progressCallback = null) | Executes a synchronous API request with optional progress callback |
| doApiRequestAsync($params, $progressCallback) | Async request returning a Generator (requires symfony/http-client) |
| doApiRequestAwait($params, $progressCallback) | Blocking wrapper around async — returns final result (requires symfony/http-client) |
| initApiKey($apiKey = null) | Initializes and validates the API key |
| getEndpointList($apiKey = null) | Retrieves the list of available endpoints |
| getEndpointDetails($name, $apiKey = null) | Retrieves details for a specific endpoint |
| cancelRequest($jobId = null) | Cancels a running request |
| appendProgressInputParams($jobId, $params) | Appends parameters for progress updates |
| setApiKey($apiKey) | Sets a new API key |


### Static Helper Methods


| Method | Description |
| --- | --- |
| encodeBinary($binaryData, $mediaFormat, $mediaType = null) | Encodes binary data to Base64 with MIME header |
| decodeBinary($base64Data) | Decodes Base64 to binary data [format, data] |
| checkIfValidBase64String($string) | Checks if a string is valid Base64 |
| checkIfValidJsonString($string) | Checks if a string is valid JSON |
| getVersion() | Returns the client version |
| detectMediaTypeFromFormat($mediaFormat) | Detects media type from format |


### Return Values


All API calls return an associative array with the following keys:


| Key | Type | Description |
| --- | --- | --- |
| success | bool | Indicates whether the request was successful |
| text / images / audio | mixed | Result data, depending on the endpoint |
| job_id | string | Job ID for asynchronous processing |
| error | string | Error message when success = false |
| error_code | int | HTTP status code on errors |
| num_generated_tokens | int | Number of generated tokens (LLM) |
| compute_duration / total_duration | float | Execution time in seconds |


### Progress Callback Signature


The optional progress callback receives two parameters:


```javascript
function (array $progressInfo, ?array $progressData): void
```


**$progressInfo** contains:
- job_id: The job ID of the request
- progress: Progress percentage (0-100)
- queue_position: Position in the queue (-1 if not queued)
- estimate: Estimated remaining time in seconds (-1 if unknown)
- job_state: Current status (queued, running, done, canceled, lapsed)
- success: Bool indicating if the current status is successful

**$progressData** contains:
- Partially generated results (e.g., text for LLM streaming)
- null if no partial data is available yet


### Running Tests


```bash
cd php
composer install
# Run all tests (requires valid API key)
AKI_API_KEY=your-key php tests/TestRunner.php
```


> ⚠️ Note: doApiRequestAsync() and doApiRequestAwait() require the symfony/http-client package. Install it with:
>   ▎ composer require symfony/http-client


### Examples


Complete examples can be found [in the examples/ directory of the repository](https://github.com/aki-io-labs/aki-io/tree/main/php/examples):
- llm_simple_example.php – Basic LLM request
- llm_stream_example.php – LLM with streaming callback
- llm_async_example.php
- image_generation_example.php – Image generation


### Important Notes


> ⚠️ API Key Security: Always keep your API key secret. Never expose it in publicly accessible client-side code. The PHP client interface is designed for server/backend usage.


> ⚠️ Binary Data: For endpoints that return binary data (images, audio), data is returned by default as Base64-encoded strings with MIME header. Use Aki::decodeBinary() to convert these to raw data.


The examples above demonstrate the available patterns for using the **aki-io** PHP library. Use the pattern that best fits your use case.

The same patterns can be used for all types of AKI.IO endpoints – only the **input parameters** to send and the returned **progress-data** and **output-data** vary depending on the endpoint type.

The expected input parameters are described in detail for LLMs [here](/docs/image-gen/imagegen-request-parameters/)(/docs/llm-chat/llm-request-parameters/) and for image generators [here]. Additional endpoint types will be available soon.


### [Android / iOS  / Java / C / C++ and more](https://aki.io/docs/client-interfaces/android-ios-java-php-c-c-and-more/)

Please contact us at [support@aki.io](mailto:support@aki.io) for help using the AKI.IO API with your preferred programming language and application framework.


---

## LLM Chat


### [LLM Request Parameters](https://aki.io/docs/llm-chat/llm-request-parameters/)

A call to a AKI.IO LLM endpoint is done with a HTTPS POST request to:


> https://aki.io/api/call/{endpoint name}


For a list of [available LLM endpoints please read here](/docs/llm-chat/available-llm-models/).

The data of the POST request are send to the endpoint in JSON format.

The basic parameters of an LLM chat request are standardized across all models. An example of the JSON request payload to an LLM endpoint:


```python
{
    "key": "fc3a8c50-b12b-4d6a-ba07-c9f6a6c32c37",
    "chat_context": "[{\"role\":\"system\",\"content\":\"You are a helpful assistant named AKI\"},{\"role\":\"user\",\"content\":\"Tell a joke\"}]",
    "text_context": "",
    "chat_output_format": "chatml",
    "top_k": 40,
    "top_p": 0.9,
    "temperature": 0.6,
    "max_gen_tokens": 1000,
    "wait_for_result": false
}
```


The description of the JSON attributes:key:string

Your AKI.IO API key for authentication and authorization. Get your AKI.IO API key at [https://aki.io/signup](https://aki.io/signup)chat_context:json

The current chat context, the last statement should be role “user” stating the question, command or request for which a response should be generated. The AKI.IO chat_context format is standardized across all offered LLM endpoints and converted to the model specific instruct syntax. The chat_context can also define embedded multimodal inputs. For a detail description of the [AKI.IO chat format please read here](/docs/llm-chat/llm-chat-context/).text_context:string

Alternative to the chat context a string based context prompt can be set, please note that most instruct based LLMs require a model specific syntax as input to respond. Please consult the documentation of the model in case you would like to use it on a low level or use it without instruct format.

Please take note that some models do not give any response at all in case the instruct format is not followed precisely.

We would advise to use the [AKI.IO chat format](/docs/llm-chat/llm-chat-context/) if you are unsure as it give the option to exchange the model with very little to no adjustments.chat_output_format:string

The desired format of the chat response should be outputted, available options are: "chatml", "raw" and "json". If not specified the default chat output is “chatml”. The most capable and future proof output format is currently “json” as it also can embed reasoning channels, multimodal output and support for tool calls.

For a detailed explanation of the [llm chat output formats please read here](/docs/llm-chat/llm-output-format/).temperature:float

The temperature sampling parameter in the range 0.0 – 1.0


> A LLM is a deterministic numeric model which calculates the most probable token based on the previous tokens. To introduce variations in the responses a randomness in picking the next tokens is introduced by not always use the token with the best scoring but to pick a random token of a bucket of the N best tokens. This process is called “sampling”.
> The temperature controls the distribution of the sampling process. A temperature closer to zero stays close to the best scoring token and seldom strays to the Nth best token. A temperature of 1.0 picks a token with even distribution across the N best tokens. With a temperature of 0.0 always the best token is picked, in this case the LLM will always answer with the exact same response to a given context.
> Rule of thumb for deterministic tasks the temperature should be close to 0. For creative writing tasks a higher temperature can give more variations and maybe unexpected but interesting responses.


top_k:int

The Top-K sampling parameter in the range 0..1000

The size of the sampling bucket. Top-K of 40 means that the answer is sampled among the 40 tokens with the best scoring. Setting to Top-K to 1 disables the sampling process as always the token with best scoring will be taken.top_p:float

The Top-P sampling parameter in the range 0.0 – 1.0

The Top-P sampling parameter controls the “quality” of the best tokens that should be considered in the sampling bucket. If the quality is below the Top-P value tokens are not considered and the bucket will be cut off and have less parameters as specified by top_k as there are not enough tokens that score above the Top-P parameter.

It is advised to keep this parameter around 0.9 to only allow synonyms in the sampling bucket. E.g. “yes”, “true”, “correct” could all be good answers with a quality above 0.9 . But not “maybe” which was the 4th best token but scored only 0.8 .max_gen_tokens:int

Limit the response to generate maximum number of tokens. For most endpoints the maximum output tokens length limit to 16.000 tokens.wait_for_result:boolean

For blocking API call set to "true". For a streaming response set to "false"

Read more about the powerful [AKI.IO streaming responses](/docs/llm-chat/chat-response/)


### [LLM Chat Response](https://aki.io/docs/llm-chat/chat-response/)

### Blocking Call – Wait for Result


Depending on if “wait_for_result” was set to True or False we get two different responses. In case wait_for_result is set to True the request will wait until the LLM is finished generating the answer and will then return the completed result of the request in following format:


```json
{
	'text': "Here's one:\n\nWhat do you call a fake noodle?\n\nAn impasta.", '	num_generated_tokens': 18, 
	'model_name': 'Llama-3.1-8B-Instruct', 
	'max_seq_len': 65536, 
	'current_context_length': 88, 
	'prompt_length': 70, 
	'success': True, 
	'job_id': '41520777-93c8-4119-a34d-4f5b21b8b856', 
	'total_duration': 0.295, 
	'compute_duration': 0.278
}
```


text:[string|json]

The complete generate chat response in the desired chat_output_format. For an explanation of the different chat output formats please see here.num_generated_tokens:integer

Total number of generated tokens in this request.prompt_length:integer

The length of the given chat_context aka prompt in tokens.current_context_length:integer

The total length of the current chat context in tokens.max_seq_len:integer

The maximum context length in tokens this endpoint is capable to process.success:string

"true" in case the request could be processed successfulerror:string

The error message in case success is "false". Errors could be not correct syntax of the JSON, invalid parameters, range errors, not valid chat context, not available endpoint, access and authorization issues.


### Non-Blocking - Streaming Response


In case “wait_for_result” was set to False in the LLM request, the request will return immediately and return following information:


```json
{
    "success": true,
    "job_id": "7412cf4b-c230-454a-a1db-7e5df401f973",
}
```


success:string

"true" in case the request could be start the stream successful.error:string

The error message in case success is "false". Errors could be not correct syntax of the JSON, invalid parameters, range errors, not valid chat context, not available endpoint, access and authorization issues.job_id:string

The id of the started streaming job. This id is required to address the request in further requests.


### Reading the Stream Progress


The progress of a LLM job can be called with a HTTPS POST request to:


> https://aki.io/api/progress/{endpoint name}


The JSON payload of the stream progress request should have following parameters:


```json
{
    "job_id": "7412cf4b-c230-454a-a1db-7e5df401f973",
    "key": "b0050e7b-286d-46a9-9354-b57ecf809952",
    "cancel": false
}
```


key:string

Your API key.job_id:string

The job id returned from the initial chat request to identify to which stream you are refering to.cancel:boolean [optional]

Optional the stream can be canceled by setting this parameter to true. No further compute of the request will be done and only the already generated tokens will be billed.

The stream progress call, will depending on the state of job, return following response:


```json
{
    "success": true,
    "job_id": "7412cf4b-c230-454a-a1db-7e5df401f973",
    "job_state": "processing",
    "progress": {
        "queue_position": 0,
        "estimate": 2.9
        "progress_data": {
            "text": "Hello",
            "num_generated_tokens": 1,
            "current_context_length": 145
        },
    }
}
```


job_state:string

It returns the state the streaming request is currently in possible values are:‘queued’

This status is returned in case the request is waiting in the queue to be processed. This state should be rarely seen, as AKI.IO is dedicated to fast streaming of LLM requests. In case this state is returned, the **“que_position”** is the waiting position in the queue and **“estimate”** the time estimated until the processing will begin.‘processing’

This is the state when the request is active processing and generating output. The “**progress_data”** branch will hold a reduced set of final result. The until this time generated output will be in the “**text”** attribute and “**num_generated_tokens”** returns the number of generated tokens until that time.‘canceled’

This state is returned in case the request was canceled on user request or in case the request has timed out because of to long time between starting the request and fetching the stream results.‘done’

The final streaming response will have the job_state ‘done’. It will have a different JSON layout and contain the same data attributes in the **“job_result”** branch as one will receive with a[ blocking call].


```json
{
    "success": true,
    "job_id": "7412cf4b-c230-454a-a1db-7e5df401f973",
    "job_state": "done",
    "job_result": {
        "text": "Here's one:\n\nWhat do you call a fake noodle?\n\nAn impasta.\n\nI hope that made you smile! Do you want to hear another one?",
        "model_name": "Llama-3.1-8B-Instruct",
        "max_seq_len": 65536,
        "prompt_length": 164,
        "num_generated_tokens": 33,
        "current_context_length": 197,
        "total_duration": 0.505,
        "compute_duration": 0.488,
    }
```


It is advised to call the **/stream/progress/{endpoint name}** at the rate suitable to your application, for real-time with a very high rate or for bulk processing at a lower rate.

The stream progress should be polled until either job_state is 'done' or 'canceled' or success is 'false' in case an error has occurred.


> The streaming polling mechanism is very robust against network issues as connection losses caused by broadcast cell changes, IP changes, etc.
> Interrupted or disconnected streams need no complicated reconnection procedure. The next progress call will pickup wherever the last missed or stalled called was discontinued.
> 
> The JSON streaming instead of a streaming HTTP response has also the advantage that out of band data can be transmitted, in this example the num_generated tokens always correspond to the received text, no side channels are required.
> Also the stream can handle bidirectional communication in this case the canceling of the stream can be directly send to the stream.


This was the low level description how to communicate with the AKI.IO LLM API directly with HTTPS JSON requests. There are currently client interfaces for [Javascript](/docs/client-interfaces/javascript/) and [Python](/docs/client-interfaces/python/) available that wrap the functionality for a fast and future proof integration in your application.


### [LLM Chat Context](https://aki.io/docs/llm-chat/llm-chat-context/)

Early LLMs were plain text continuation models, they continued to write a given text in the style as the given input text, a chat was simulated by giving it a chat style conversation, the model picked up the format and continued with writing a chat style response. Current, so called, instruction LLMs use a defined input syntax as text input to control various aspects of the chat conversation with a “system-prompt” and also allow input of encoded media (images, documents, audio) in the input text to realize a multimodal and controllable chat response.

As every model vendor (Meta, OpenAI, Qwen, etc.) defines his own “chat context” format with subtle difference in syntax, AKI defines a meta chat context format: the “AKI Chat Context”, which is automatically converted to the syntax the specific model understands.


The AKI Chat Context Format

The Aki Chat Context is a JSON structure with following basic structure:


```json
[
    {
        "role": "system",
        "content": "You are a helpful assistant named AKI"
    },
    {
        "role": "assistant", 
        "content": “How can I help you?”
    },
    {
        "role": "user", 
        "content": “Tell a joke”
    }
]
```


There are three roles defined: The **assistant** the **user** and the **system**.

The **user** role is the input that is coming from the user or a request to the assistant.

The** assistant** is the LLM that should answer or fulfil the task.

The** system** is the boss of the assistant, describing the setting and briefs the assistant how he should do his job, what his role is and gives background information to the assistant.

A chat is the alternating conversation between user and assistant. To send a request to the LLM, the last entry should be the request of the user.


### Multimodal Content


The “content” part can have an extended form to embed media like images, audio and video. Either as input from the user for describing and asking questions about the media, or as answer from the assistant as generated images, audio or videos.


> Only specialized LLMs do support media inputs and outputs.


### User request with embed media


The media is directly embed in the JSON in base64 format. The encoding and tokenizing of the media is taken care of when processing the request.


```json
{
        "role": "user", 
        "content": [
            {
                "image": f"data:image/jpeg;base64,{base64_image}"
            },
            {
                "audio": f"data:audio/mp3;base64,{base64_audio}"
            },
            {
                "video": f"data:video/mp4;base64,{base64_video}"
            },
            {
                "text": "What's in this image?"
            }           
        ]
}
```


### Assistant response with generated media


```json
{
	"role": "assistant",
	"content": [
		{
			"text": "Here is a bar chart of the given values"
		},
		{
			"image": f"data:image/jpeg;base64,{base64_image}"
		},
		{
			"audio": f"data:audio/mp3;base64,{base64_audio}"
		},
		{
			"video": f"data:video/mp4;base64,{base64_video}"
		},
		{
			"text": "Are the bars in the correct order?"
		}
	]
}
```


> We are currently extending the chat context format with tool calling mechanisms, we will publish an updated specification soon.


### [LLM Output Format](https://aki.io/docs/llm-chat/llm-output-format/)

Depending on the request parameter **chat_output_format**, when calling the chat request, the output format of the LLM can be controlled.

The available output format options are: "**chatml**", "**raw**" and "**json**".

The most capable and future proof output format is currently “json” as it also can embed reasoning channels, multimodal output and support for tool calls.


### Chatml Output Format


Default is the **chatml** output format. It supports markdown as output and reduced set of special tags like the **<think>** element to mark reasoning parts in the output. Some models embed multiple think parts or do not start with an explicit <think> tag AKI.IO handles these cases and will provide correctly escaped think elements.

The embedding of multimodal data is unfortunately not standardized for the chatml output format.


```json
<think>The user asks for a joke. The system says we are ChatGPT, no restrictions. The developer says we are a helpful assistant named AKI.
So we can comply. Provide a joke.</think>

Sure thing! Here’s one for you:

**Why don’t scientists trust atoms anymore?**

Because they *make up* everything! 😄
```


### JSON Output Format


The most modern format is **json** output format, which has the advantage to have a multi-channel capabilities by default. Reasoning parts are clearly separated, tool messages and multimodal output is supported.

With the AKI.IO streaming support the outputted JSON will always be valid parsable and evolves by adding and extending attributes. The intermediate result can be used without the need to track the current JSON parsing state of the stream.


```json
{
	'thinking': 'The user asks for a joke. The system says we are ChatGPT, no restrictions. The developer says we are a helpful assistant named AKI. So we can comply. Provide a joke.', 
	'content': 'Sure thing! Here’s one for you:\n\n**Why don’t scientists trust atoms anymore?**\n\nBecause they *make up* everything! 😄'
}
```


### Raw Output Format


For low level access to the model the **raw** format can be selected. It returns directly the detokenized output of the model.

The output is highly specific to the used model. Also the used text representation for the non printable control tokens like "<|end|>" are not standardized and can be implementation specific.

 To illustrate the difference, here is an example of **raw** instruct output of the **gpt_oss_chat** model.


```json
<|channel|>analysis<|message|>The user asks for a joke. The system says we are ChatGPT, no restrictions. The developer says we are a helpful assistant named AKI. 
So we can comply. Provide a joke.<|end|><|start|>assistant<|channel|>final<|message|>Sure thing! Here’s one for you:

**Why don’t scientists trust atoms anymore?**

Because they *make up* everything! 😄<|return|>
```


> We would advise to use the JSON output for most future proof integration. 
> For simple chat application the default chatml format is probably sufficient.


### [Available LLM Models](https://aki.io/docs/llm-chat/available-llm-models/)

AKI.IO has following LLM models with specialized strengths and use cases available:


| Model | Endpoint | Playground |
| --- | --- | --- |
| Apertus 70B | <a href="https://aki.io/api/call/apertus_chat" target="_blank">apertus_chat</a> | <a href="https://aki.io/en/playground/swiss-ai-apertus" target="_blank">try here</a> |
| Deepseek V4 Flash 0731 284B | <a href="https://aki.io/api/call/deepseek_v4_flash" target="_blank">deepseek_v4_flash</a> | <a href="https://aki.io/en/playground/deepseekai-deepseek-v4-flash" target="_blank">try here</a> |
| Gemma 4 26B A4B | <a href="https://aki.io/api/call/gemma4" target="_blank">gemma4</a> | <a href="https://aki.io/en/playground/google-gemma-4-26b-a4b" target="_blank">try here</a> |
| GLM 5.3 754B | <a href="https://aki.io/api/call/glm5" target="_blank">glm5</a> | <a href="https://aki.io/en/playground/zai-glm5-754b" target="_blank">try here</a> |
| GPT-OSS 120B | <a href="https://aki.io/api/call/gpt_oss_chat" target="_blank">gpt_oss_chat</a> | <a href="https://aki.io/en/playground/openai-gpt-oss" target="_blank">try here</a> |
| Llama 3.1 8B | <a href="https://aki.io/api/call/llama3_8b_chat" target="_blank">llama3_8b_chat</a> | <a href="https://aki.io/en/playground/meta-llama3-8b" target="_blank">try here</a> |
| Llama 3.3 70B | <a href="https://aki.io/api/call/llama3_chat" target="_blank">llama3_chat</a> | <a href="https://aki.io/en/playground/meta-llama3" target="_blank">try here</a> |
| Mistral 4 119B | <a href="https://aki.io/api/call/mistral4" target="_blank">mistral4</a> | <a href="https://aki.io/en/playground/mistralai-mistral-small-4-119b" target="_blank">try here</a> |
| Qwen 3.6 35B A3B | <a href="https://aki.io/api/call/qwen3_6" target="_blank">qwen3_6</a> | <a href="https://aki.io/en/playground/qwen-qwen3_6-35b-a3b" target="_blank">try here</a> |
| Qwen 3.8 27B | <a href="https://aki.io/api/call/qwen3_8" target="_blank">qwen3_8</a> | <a href="https://aki.io/en/playground/qwen-qwen3_8-27b" target="_blank">try here</a> |


### [Implementation Examples](https://aki.io/docs/llm-chat/implementation-examples/)

## Javascript LLM Example


For examples how to connect to the AKI.IO LLM API with Javascript please see the chapter [Javascript Client Interface](/docs/client-interfaces/javascript/).


## Python LLM Example


For examples how to connect to the AKI.IO LLM API with Python please see the chapter [Python Client Interface](/docs/client-interfaces/python/).


---

## Image Gen


### [Image Gen Request Parameters](https://aki.io/docs/image-gen/imagegen-request-parameters/)

The basic parameters of all AKI.IO image generator models are standardized across all models. The standard request parameters for image generation endpoints are in JSON format.

The call to an image generator is done with a HTTPS POST request to:


> https://aki.io/api/call/{endpoint name}


For a overview of [available image generator endpoints please read here](/docs/image-gen/available-image-gen-models/).


## Request Parameters


key:string

Your AKI.IO API key for authentication and authorization. Get your AKI.IO API key at [https://aki.io/signup](https://aki.io/signup)prompt:string

The prompt that describes the image that should be generated. Depending on the model it can be very detailed and describing different parts of the image, objects, persons, background, lighting, colors, camera lense or style like painting, comic, photo realistic etc.width:integer

Depending on the capabilities of the model but usually in the range of 128 to 2048 pixels.height:integer

Depending on the capabilities of the model but usually in the range of 128 to 2048 pixels.


> The aspect ratio of the image can have influence in the generated image like landscape and portrait images can trigger different styles as they are most commonly used for one or the other sort of images. Some models work best on a defined aspect ratio, please check the details of the used model.


seed:integer (optional)

The seed of the initial random image that is taken as starting point for generating the image.

The default setting -1 means that a random seed should be used of generating a “surprise” image. The used seed is returned in the output.


> With the same seed, prompt and width and height the exact image can be recreated. Changing either height or width will give a different image, although the seed and prompt was not changed.


negative_prompt (optional)

The optional negative prompt can be used to suppress unwanted features for example specific colors, objects or styles in the generated image.steps:integer (optional)

The inference steps the diffusion process should be run to develop the image. For ‘turbo’ models only a few steps, usually below 10, are required. For larger models that have a higher control over the diffusion process up to 50 steps can be set. A lower value for steps will result in a faster image, but the resulting image will still be faded and have rough or no details. A higher then recommended setting will over sharpen the image or result in very high contrast HDR like images.


> The default setting is the recommended setting for the model.


true_cfg_scale:float (optional)

The cfg_scale stands for “classifier‑free‑guidance” (CFG) scale, it tells the diffusion process how strongly to mix the prompt‑driven noise reduction with the *unconditional* one at each sampling step. The higher the setting, the more the image generation will focus on the text prompt and the objects it should render; the lower the number, the more the image will have additional random features and lively background.

The range is 0 to 10. The default value is in the mid range.


> Not all image-gen models provide this setting, especially the turbo models require a fixed setting of this parameter and will ignore this setting.


quality:string (optional)

The quality parameter has following options “**max**”, “**high**”, “**fast**” and “**preview**”. It controls the trade off in quality vs speed when computing the diffusion process. The settings are defined as:

- “**max**” no compromises in computation are made, the complete process is calculated with highest resolution

- “**high**” is the default setting, the compute resolution is optimized in a way that no noticeable reduction in image quality is achieved

- “**fast**” the compute makes minimal compromises in image quality to produce the image faster

- “**preview**” noticeable image quality reduction, useable to get a fast preview of the image that will be generated

In case the prompt, seed and resolution settings are kept the same, the quality setting will reproduce an image with the same composition but depending on the quality setting a shaper, detailed and better illuminated version of the image.


> The quality parameter has high impact on the generation speed and offers a more reproducible image than just reducing the number of steps to generate. We would suggest to use this parameter to control the image generation speed in favor over reducing the number of diffusion steps.


### [Available Image Gen Models](https://aki.io/docs/image-gen/available-image-gen-models/)

AKI.IO has following image gen and image edit models with specialized strengths and use cases available:


| Model | Endpoint | Playground |
| --- | --- | --- |
| Flux.2 [klein] | <a href="https://aki.io/api/call/flux2_klein" target="_blank">flux2_klein</a> | <a href="https://aki.io/en/playground/black-forest-labs-flux2-klein" target="_blank">try here</a> |
| Qwen Image | <a href="https://aki.io/api/call/qwen_image" target="_blank">qwen_image</a> | <a href="https://aki.io/en/playground/qwen-image" target="_blank">try here</a> |
| Qwen Image Edit | <a href="https://aki.io/api/call/qwen_image_edit" target="_blank">qwen_image_edit</a> | <a href="https://aki.io/en/playground/qwen-image-edit" target="_blank">try here</a> |
| Z-Image Turbo | <a href="https://aki.io/api/call/z_image_turbo" target="_blank">z_image_turbo</a> | <a href="https://aki.io/en/playground/tongyi-mai-z-image" target="_blank">try here</a> |


### [Image Gen Python Examples](https://aki.io/docs/image-gen/image-gen-python-examples/)

## Simple Image Gen Example


Below a basic example to call AKI.IO Image Gen endpoints using the [aki-io interface pip](/docs/client-interfaces/python/).

The example includes a callback to show the progress of the image generation.

For decoding the generate image data and the file format the **Aki.decode_binary(…)** function is used.


```python
from aki_io import Aki

aki = Aki(
    'z_image_turbo',
    'fc3a8c50-b12b-4d6a-ba07-c9f6a6c32c37',
)

def progress_callback(progress, progress_data):
    print(f'Progress: {progress.get("progress", 0)}%')

result = aki.do_api_request(
    {
        'prompt': 'Astronaut on Mars holding a banner which states "AKI is happy to serve your model" during sunset sitting on a giant yellow rubber duck',
        'seed': -1,
        'height': 512,
        'width': 512,
    },
    progress_callback, # optional
)

if result['success']:
    # Save images to files
    images = result.get('images')
    for idx, image_data in enumerate(images):
        media_format, image_binary = Aki.decode_binary(image_data)
        output_file = f'image_{idx}.{media_format}'
        with open(output_file, 'wb') as f:
            f.write(image_binary)
        print(f'Output image saved at {output_file}')
else:
    print("API Error:", result.get('error_code'), "-", result.get('error'))
```


## Simple Image Edit Example


A basic example to call AKI.IO Image Edit endpoints using the [aki-io interface pip](/docs/client-interfaces/python/).

The example includes a callback to show the progress of the image generation.

For encoding and decoding the image data and the file format the **Aki.encode_binary(…)** and **Aki.decode_binary(…)** function are used.


```python
from aki_io import Aki

aki = Aki(
    'qwen_image_edit',
    'fc3a8c50-b12b-4d6a-ba07-c9f6a6c32c37',
)

def progress_callback(progress, progress_data):
    print(f'Progress: {progress.get("progress", 0)}%')

# read image created with image_gen_example.py (run first to get an example image)
with open('image_0.jpeg', 'rb') as f:
    input_image = f.read()

result = aki.do_api_request(
    {
        'prompt': 'replace astronaut with ice bear',
        'seed': -1,
        'height': 1024,
        'width': 1024,
        'image': Aki.encode_binary(input_image, 'jpeg')
    },
    progress_callback, # optional
)

if result['success']:
    # Save images to files
    images = result.get('images')
    for idx, image_data in enumerate(images):
        media_format, image_binary = Aki.decode_binary(image_data)
        output_file = f'image_edit_{idx}.{media_format}'
        with open(output_file, 'wb') as f:
            f.write(image_binary)
        print(f'Output image saved at {output_file}')
else:
    print("API Error:", result.get('error_code'), "-", result.get('error'))
```


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