Qwen/Qwen3-235B-A22B-Instruct-2507

LLM

235B-parameter MoE thinking model in Qwen3 series.

Parameters

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/Qwen3-235B-A22B-Instruct-2507",
    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 requests

Authentication

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.

Example: "Qwen/Qwen3-235B-A22B-Instruct-2507"

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.

Default: 1024Min: 1

temperaturenumber

Sampling temperature between 0 and 2. Higher values make output more random, lower values more focused and deterministic.

Default: 0.7Min: 0Max: 2

top_pnumber

Nucleus sampling parameter. The model considers the tokens with top_p probability mass.

Default: 1Min: 0Max: 1

streamboolean

If set to true, partial message deltas will be sent as server-sent events.

Default: false

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.

Default: 0Min: -2Max: 2

presence_penaltynumber

Penalizes new tokens based on whether they appear in the text so far. Between -2.0 and 2.0.

Default: 0Min: -2Max: 2

Example Request Body

json

{
  "model": "Qwen/Qwen3-235B-A22B-Instruct-2507",
  "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".

Default: "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-skills

Setup 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.

Learn more

github.com/AtlasCloudAI/atlas-cloud-skills

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-mcp

Configuration

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

github.com/AtlasCloudAI/mcp-server

Qwen3-235B-A22B

Advanced multilingual AI with 128K-token context, excelling in coding, reasoning, and enterprise applications.

Qwen 3 Model Description

Qwen3-235B-A22B, developed by Alibaba Cloud, is a flagship large language model leveraging a Mixture-of-Experts (MoE) architecture. With 235 billion total parameters and 22 billion active per inference, it delivers top-tier performance in coding, math, and reasoning across 119 languages. Optimized for enterprise tasks like software development and research, it’s accessible via AI/ML API.

Technical Specifications

Performance Benchmarks

Qwen3-235B-A22B uses a Transformer-based MoE architecture, activating 22 billion of its 235 billion parameters per token via top-8 expert selection, reducing compute costs. It features Rotary Positional Embeddings and Group-Query Attention for efficiency. Pre-trained on 36 trillion tokens across 119 languages, it uses RLHF and a four-stage post-training process for hybrid reasoning.

Context Window: 32K tokens natively, extendable to 128K with YaRN.
Benchmarks:
- Outperforms OpenAI’s o3-mini on AIME (math) and Codeforces (coding).
- Surpasses Gemini 2.5 Pro on BFCL (reasoning) and LiveCodeBench.
- MMLU score: 0.828, competitive with DeepSeek R1.
Performance: 40.1 tokens/second output speed, 0.54s latency (TTFT).
API Pricing:
- Input tokens: $0.21 per million tokens
- Output tokens: $0.63 per million tokens
- Cost for 1,000 tokens: $0.00021 (input) +$ 0.00063 (output) = $0.00084 total

Performance Metrics

Qwen3-235B-A22B comparison

Key Capabilities

Qwen3-235B-A22B excels in hybrid reasoning, toggling between thinking mode (/think) for step-by-step problem-solving and non-thinking mode (/no_think) for rapid responses. It supports 119 languages, enabling seamless global applications like multilingual chatbots and translation. With a 128K-token context, it processes large datasets, codebases, and documents with high coherence, using XML delimiters for structure retention.

Coding Excellence: Outperforms OpenAI’s o1 on LiveCodeBench, supporting 40+ languages (Python, Java, Haskell, etc.). Generates, debugs, and refactors complex codebases with precision.
Advanced Reasoning: Surpasses o3-mini on AIME for math and BFCL for logical reasoning, ideal for intricate problem-solving.
Multilingual Proficiency: Natively handles 119 languages, powering cross-lingual tasks like semantic analysis and translation.
Enterprise Applications: Drives biomedical literature parsing, financial risk modeling, e-commerce intent prediction, and legal document analysis.
Agentic Workflows: Supports tool-calling, Model Context Protocol (MCP), and function calling for autonomous AI agents.
API Features: Offers streaming, OpenAI-API compatibility, and structured output generation for real-time integration.

Optimal Use Cases

Qwen3-235B-A22B is tailored for high-complexity enterprise scenarios requiring deep reasoning and scalability:

Software Development: Autonomous code generation, debugging, and refactoring for large-scale projects, with superior performance on Codeforces and LiveCodeBench.
Biomedical Research: Parsing dense medical literature, structuring clinical notes, and generating patient dialogues with high accuracy.
Financial Modeling: Risk analysis, regulatory query answering, and financial document summarization with precise numerical reasoning.
Multilingual E-commerce: Semantic product categorization, user intent prediction, and multilingual chatbot deployment across 119 languages.
Legal Analysis: Multi-document review for regulatory compliance and legal research, leveraging 128K-token context for coherence.

Comparison with Other Models

Qwen3-235B-A22B stands out among leading models due to its MoE efficiency and multilingual capabilities:

vs. OpenAI’s o3-mini: Outperforms in math (AIME) and coding (Codeforces), with lower latency (0.54s TTFT vs. 0.7s). Offers broader language support (119 vs. ~20 languages).
vs. Google’s Gemini 2.5 Pro: Excels in reasoning (BFCL) and coding (LiveCodeBench), with a larger context window (128K vs. 96K tokens) and more efficient inference via MoE.
vs. DeepSeek R1: Matches MMLU performance (0.828) but surpasses in multilingual tasks and enterprise scalability, with cheaper API pricing.
vs. GPT-4.1: Competitive in coding and reasoning, with lower costs and native 119-language support, unlike GPT-4.1’s English focus.

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