> ## Documentation Index
> Fetch the complete documentation index at: https://docs.aieev.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Qwen3.6-35B-A3B
> High-performance mixture-of-experts model with multimodal support.
**Model ID:** `qwen/qwen3.6-35b-a3b` | **Endpoint ID:** `qwen3-6-35b-a3b` | **Parameters:** 35B (A3B MoE) | **Released:** 2026-04-27
| Input Price | Output Price | Context | Modality | Quantization |
| :----------: | :----------: | :-----: | :-----------------: | :----------: |
| \$0.140 / 1M | \$1.000 / 1M | 262,144 | text + image → text | FP8 |
## Overview
Qwen 3.6 35B (A3B) is a mixture-of-experts (MoE) large language model designed to deliver strong performance with efficient inference. It uses sparse activation, where only a subset of parameters is activated per token, enabling a better balance between capability and serving cost. Compared to smaller models such as 9B variants, it offers improved reasoning, coding, and analytical performance for more complex workloads. The model supports long-context inference up to 262,144 tokens and multimodal input (text and image), making it suitable for advanced assistants, backend automation, multimodal understanding, and large-scale inference systems.
Try the model in the playground.
Deploy with AIR Container.
Connect with the OpenAI SDK as-is.
## Pricing
| Currency | Input (1M tokens) | Output (1M tokens) |
| :------- | ----------------: | -----------------: |
| USD | \$0.140 | \$1.000 |
| KRW | ₩210 | ₩1,500 |
## Supported Features
All features are available through the [OpenAI Compatible API](/docs/air-api/openai-compatible-api).
| Feature | Supported | Notes |
| :-------------------------------------------------------------------------------- | :-------: | :------------------------------------------------------------ |
| [Streaming](/docs/air-api/openai-compatible-api#streaming) | ✅ | SSE, `include_usage` supported |
| [Tool Calling](/docs/air-api/openai-compatible-api#tool-calling-function-calling) | ✅ | Parallel and streaming tool calls supported |
| [Vision](/docs/air-api/openai-compatible-api#vision-image-input) | ✅ | Up to 4 images per request |
| [Reasoning](/docs/air-api/openai-compatible-api#reasoning) | ✅ | Returned separately in the `reasoning` field, can be disabled |
| [JSON Mode](/docs/air-api/openai-compatible-api#json-mode-structured-outputs) | ✅ | `response_format: json_object` |
| logprobs | ✅ | `logprobs` / `top_logprobs` |
## Key Features
* Mixture-of-Experts (MoE, A3B) architecture with sparse activation for efficient scaling
* Stronger reasoning and coding performance compared to smaller models such as 9B variants
* Supports long-context inference up to 262,144 tokens
* Multimodal capability with text and image input support (max 4 images per request; exceeding the limit returns a 400 error)
* Strong multilingual understanding and generation
* Efficient inference through sparse expert activation
* Compatible with high-throughput serving engines such as vLLM
* Provided in FP8 format for efficient deployment
## Use Cases
### Complex Q\&A
Handle complex multi-step reasoning and analytical queries.
```text Input theme={null}
Compare transformer-based models and mixture-of-experts models
in terms of scalability and efficiency.
```
```text Output theme={null}
Transformer models scale by increasing parameter count and compute,
while MoE models improve efficiency by activating only a subset of
parameters per token...
MoE advantages:
1. Higher parameter capacity with lower compute cost
2. Better scalability across distributed systems
3. Improved efficiency for large-scale inference workloads
```
### Advanced Code Generation
Generate production-level code and system design explanations.
```text Input theme={null}
Design a scalable distributed task queue system using Python
and explain key components.
```
```text Output theme={null}
A scalable distributed task queue system can be built using components
such as a message broker (Redis/Kafka), worker nodes, and a task scheduler...
Key components:
- Producer: submits tasks
- Broker: queues tasks
- Worker: executes tasks
- Result backend: stores results
```
### Deep Analysis & Summarization
Perform deeper document understanding and insight extraction.
### Visual Understanding
Analyze images and extract insights through multimodal reasoning.
## Parameters
| Parameter | Type | Required | Default | Description |
| -------------------- | ---------------- | -------- | ------- | ---------------------------------------------------------------------------------- |
| `messages` | array | Required | - | List of input messages for chat-based generation. Supports text and image content. |
| `max_tokens` | integer | Optional | - | Maximum output tokens to generate |
| `temperature` | number | Optional | 1 | Sampling temperature (0.0-2.0) |
| `top_p` | number | Optional | 1 | Nucleus sampling threshold |
| `top_k` | integer | Optional | - | Limit sampling to the top-k most likely tokens |
| `min_p` | number | Optional | - | Minimum probability threshold for token sampling |
| `frequency_penalty` | number | Optional | 0 | Penalty for token frequency |
| `presence_penalty` | number | Optional | 0 | Penalty for token presence |
| `repetition_penalty` | number | Optional | - | Penalty for repeated token generation |
| `stop` | string \| array | Optional | - | Stop sequence(s) that end generation |
| `seed` | integer | Optional | - | Random seed for reproducible sampling |
| `stream` | boolean | Optional | false | Enable streaming responses |
| `tools` | array | Optional | - | Tool calling function definitions |
| `tool_choice` | string \| object | Optional | auto | How tools are invoked (`auto`, `none`, or a specific function) |
| `response_format` | object | Optional | - | JSON mode (`{"type": "json_object"}`) |
| `logprobs` | boolean | Optional | false | Return per-token log probabilities |
## Model Details
| Property | Value |
| ------------------ | --------------------------------------------------------- |
| Context Length | 262,144 |
| Max Output Length | 262,144 |
| Quantization | fp8 |
| Input Modalities | text, image (max 4 images per request) |
| Output Modalities | text |
| Supported Features | tools, reasoning, streaming, vision, json\_mode, logprobs |
## Quick Start
Generate an API key from your [AirCloud account](https://aieev.com).
Replace `YOUR_API_KEY` with your actual key and choose your preferred language (Python, cURL, Node.js).
```python Python theme={null}
from openai import OpenAI
client = OpenAI(
api_key="YOUR_API_KEY",
base_url="https://ap-1.aieev.cloud/endpoints/qwen3-6-35b-a3b/v1"
)
response = client.chat.completions.create(
model="qwen/qwen3.6-35b-a3b",
messages=[{"role": "user", "content": "Hello!"}]
)
print(response.choices[0].message.content)
```
```bash cURL theme={null}
curl https://ap-1.aieev.cloud/endpoints/qwen3-6-35b-a3b/v1/chat/completions \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "qwen/qwen3.6-35b-a3b",
"messages": [{"role": "user", "content": "Hello!"}]
}'
```
```javascript Node.js theme={null}
import OpenAI from "openai";
const client = new OpenAI({
apiKey: "YOUR_API_KEY",
baseURL: "https://ap-1.aieev.cloud/endpoints/qwen3-6-35b-a3b/v1",
});
const response = await client.chat.completions.create({
model: "qwen/qwen3.6-35b-a3b",
messages: [{ role: "user", content: "Hello!" }],
});
console.log(response.choices[0].message.content);
```
## Tags
`open-source` `conversational` `35B` `reasoning` `multilingual` `moe` `high-performance`