Qwen3-Coder is the code version of Qwen3.
Code Example
import os
from openai import OpenAI
client = OpenAI(
api_key=os.getenv("ATLASCLOUD_API_KEY"),
base_url="https://api.atlascloud.ai/v1"
)
response = client.chat.completions.create(
model="Qwen/Qwen3-Coder",
messages=[
{
"role": "user",
"content": "hello"
}
],
max_tokens=1024,
temperature=0.7
)
print(response.choices[0].message.content)Install
Install the required package for your language.
bash
pip install requestsAuthentication
All API requests require authentication via an API key. You can get your API key from the Atlas Cloud dashboard.
bash
export ATLASCLOUD_API_KEY="your-api-key-here"HTTP Headers
python
import os
API_KEY = os.environ.get("ATLASCLOUD_API_KEY")
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {API_KEY}"
}Keep your API key secure
Never expose your API key in client-side code or public repositories. Use environment variables or a backend proxy instead.
Submit a request
import requests
url = "https://api.atlascloud.ai/v1/chat/completions"
headers = {
"Content-Type": "application/json",
"Authorization": "Bearer $ATLASCLOUD_API_KEY"
}
data = {
"model": "your-model",
"messages": [{"role": "user", "content": "Hello"}],
"max_tokens": 1024
}
response = requests.post(url, headers=headers, json=data)
print(response.json())Input Schema
The following parameters are accepted in the request body.
Total: 9Required: 2Optional: 7
modelstringrequired
The model ID to use for the completion.
messagesarray[object]required
A list of messages comprising the conversation so far.
rolestringrequired
The role of the message author. One of "system", "user", or "assistant".
systemuserassistant
contentstringrequired
The content of the message.
max_tokensinteger
The maximum number of tokens to generate in the completion.
temperaturenumber
Sampling temperature between 0 and 2. Higher values make output more random, lower values more focused and deterministic.
top_pnumber
Nucleus sampling parameter. The model considers the tokens with top_p probability mass.
streamboolean
If set to true, partial message deltas will be sent as server-sent events.
stoparray[string]
Up to 4 sequences where the API will stop generating further tokens.
frequency_penaltynumber
Penalizes new tokens based on their existing frequency in the text so far. Between -2.0 and 2.0.
presence_penaltynumber
Penalizes new tokens based on whether they appear in the text so far. Between -2.0 and 2.0.
Example Request Body
json
{
"model": "Qwen/Qwen3-Coder",
"messages": [
{
"role": "user",
"content": "Hello"
}
],
"max_tokens": 1024,
"temperature": 0.7,
"stream": false
}Output Schema
The API returns a ChatCompletion-compatible response.
idstringrequired
Unique identifier for the completion.
objectstringrequired
Object type, always "chat.completion".
createdintegerrequired
Unix timestamp of when the completion was created.
modelstringrequired
The model used for the completion.
choicesarray[object]required
List of completion choices.
indexintegerrequired
Index of the choice.
messageobjectrequired
The generated message.
finish_reasonstringrequired
The reason generation stopped.
stoplengthcontent_filter
usageobjectrequired
Token usage statistics.
prompt_tokensintegerrequired
Number of tokens in the prompt.
completion_tokensintegerrequired
Number of tokens in the completion.
total_tokensintegerrequired
Total tokens used.
Example Response
json
{
"id": "chatcmpl-abc123",
"object": "chat.completion",
"created": 1700000000,
"model": "model-name",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "Hello! How can I assist you today?"
},
"finish_reason": "stop"
}
],
"usage": {
"prompt_tokens": 10,
"completion_tokens": 20,
"total_tokens": 30
}
}Atlas Cloud Skills
Atlas Cloud Skills integrates 300+ AI models directly into your AI coding assistant. One command to install, then use natural language to generate images, videos, and chat with LLMs.
Supported Clients
Claude Code
OpenAI Codex
Gemini CLI
Cursor
Windsurf
VS Code
Trae
GitHub Copilot
Cline
Roo Code
Amp
Goose
Replit
40+ supported clients
Install
bash
npx skills add AtlasCloudAI/atlas-cloud-skillsSetup API Key
Get your API key from the Atlas Cloud dashboard and set it as an environment variable.
bash
export ATLASCLOUD_API_KEY="your-api-key-here"Capabilities
Once installed, you can use natural language in your AI assistant to access all Atlas Cloud models.
Image GenerationGenerate images with models like Nano Banana 2, Z-Image, and more.
Video CreationCreate videos from text or images with Kling, Vidu, Veo, etc.
LLM ChatChat with Qwen, DeepSeek, and other large language models.
Media UploadUpload local files for image editing and image-to-video workflows.
MCP Server
Atlas Cloud MCP Server connects your IDE with 300+ AI models via the Model Context Protocol. Works with any MCP-compatible client.
Supported Clients
Cursor
VS Code
Windsurf
Claude Code
OpenAI Codex
Gemini CLI
Cline
Roo Code
100+ supported clients
Install
bash
npx -y atlascloud-mcpConfiguration
Add the following configuration to your IDE's MCP settings file.
json
{
"mcpServers": {
"atlascloud": {
"command": "npx",
"args": [
"-y",
"atlascloud-mcp"
],
"env": {
"ATLASCLOUD_API_KEY": "your-api-key-here"
}
}
}
}Available Tools
atlas_generate_imageGenerate images from text prompts.
atlas_generate_videoCreate videos from text or images.
atlas_chatChat with large language models.
atlas_list_modelsBrowse 300+ available AI models.
atlas_quick_generateOne-step content creation with auto model selection.
atlas_upload_mediaUpload local files for API workflows.
Learn more
Qwen3-Coder-480B-A35B-Instruct
Highlights
Qwen3-Coder-480B-A35B-Instruct. featuring the following key enhancements:
- Significant Performance among open models on Agentic Coding, Agentic Browser-Use, and other foundational coding tasks, achieving results comparable to Claude Sonnet.
- Long-context Capabilities with native support for 256K tokens, extendable up to 1M tokens using Yarn, optimized for repository-scale understanding.
- Agentic Coding supporting for most platform such as Qwen Code, CLINE, featuring a specially designed function call format.
Model Overview
Qwen3-480B-A35B-Instruct has the following features:
- Type: Causal Language Models
- Training Stage: Pretraining & Post-training
- Number of Parameters: 480B in total and 35B activated
- Number of Layers: 62
- Number of Attention Heads (GQA): 96 for Q and 8 for KV
- Number of Experts: 160
- Number of Activated Experts: 8
- Context Length: 262,144 natively.
NOTE: This model supports only non-thinking mode and does not generate <think></think> blocks in its output. Meanwhile, specifying enable_thinking=False is no longer required.
