> ## 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.

# Available Models

> Browse all available AI models on AirCloud.

AirCloud provides access to a variety of AI models for inference. Models are accessible through the Air API using an OpenAI-compatible interface.

## Text models

Conversational AI, reasoning, and code generation.

| Model | Description | Input | Output |
| - | - | - | - |
| [Qwen3.8-27B](/models/qwen3-8-27b.md) | Compact dense vision-language model for agentic and long-horizon work. | \$0.400/1M tokens | \$3.000/1M tokens |
| [Qwen3.6-35B-A3B](/models/qwen3-6-35b-a3b.md) | High-performance MoE model with multimodal support. | \$0.140/1M tokens | \$1.000/1M tokens |
| [Qwen3.5-9B](/models/qwen3-5-9b.md) | Compact and efficient model with multimodal support. | \$0.100/1M tokens | \$0.150/1M tokens |
| [Qwen3-32B](/models/qwen3-32b.md) | Dense 32B model with hybrid reasoning. | \$0.080/1M tokens | \$0.280/1M tokens |
| [Llama3.3-70B](/models/llama3-3-70b.md) | Meta's multilingual 70B instruct model. | \$0.100/1M tokens | \$0.320/1M tokens |
| [DeepSeek V4 Flash](/models/deepseek-v4-flash.md) | Efficiency-optimized MoE model for fast, low-cost inference. | \$0.100/1M tokens | \$0.200/1M tokens |

## Embedding models

Vector embeddings for semantic search and RAG.

| Model | Description | Input | Output |
| - | - | - | - |
| [Qwen3-Embedding-8B](/models/qwen3-embedding-8b.md) | Multilingual embedding model with 4,096-dimensional vectors. | \$0.010/1M tokens | \$0.000/1M tokens |

## Audio models

Text-to-speech and voice synthesis.

| Model | Description | Input | Output |
| - | - | - | - |
| [Qwen3-TTS](/models/qwen3-tts.md) | Multilingual TTS with custom voice cloning. | \$0.080/1,000 characters | \$0.000 |

## Getting Started

**1. Get your API key**

Sign up for an [AirCloud account](https://aieev.com) and generate an API key from the dashboard.

**2. Make your first request**

Replace `YOUR_API_KEY` with your actual key and run the code below.

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

response = requests.post(
    "https://ap-1.aieev.cloud/endpoints/qwen3-5-9b/v1/chat/completions",
    headers={
        "Authorization": "Bearer YOUR_API_KEY",
        "Content-Type": "application/json"
    },
    json={
        "model": "qwen/qwen3.5-9b",
        "messages": [{"role": "user", "content": "Hello!"}]
    }
)

result = response.json()
print(result["choices"][0]["message"]["content"])
```

```bash cURL theme={null}
curl --request POST \
  --url https://ap-1.aieev.cloud/endpoints/qwen3-5-9b/v1/chat/completions \
  --header 'Authorization: Bearer YOUR_API_KEY' \
  --header 'Content-Type: application/json' \
  --data '{
    "model": "qwen/qwen3.5-9b",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'
```

```javascript Node.js theme={null}
const response = await fetch(
  "https://ap-1.aieev.cloud/endpoints/qwen3-5-9b/v1/chat/completions",
  {
    method: "POST",
    headers: {
      "Authorization": "Bearer YOUR_API_KEY",
      "Content-Type": "application/json"
    },
    body: JSON.stringify({
      model: "qwen/qwen3.5-9b",
      messages: [{ role: "user", content: "Hello!" }]
    })
  }
);

const result = await response.json();
console.log(result.choices[0].message.content);
```


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