235B-parameter MoE thinking model in Qwen3 series.
程式碼範例
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)安裝
為您的程式語言安裝所需的套件。
pip install requests驗證
所有 API 請求都需要透過 API 金鑰進行驗證。您可以從 Atlas Cloud 儀表板取得 API 金鑰。
export ATLASCLOUD_API_KEY="your-api-key-here"HTTP 標頭
import os
API_KEY = os.environ.get("ATLASCLOUD_API_KEY")
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {API_KEY}"
}切勿在客戶端程式碼或公開儲存庫中暴露您的 API 金鑰。請改用環境變數或後端代理。
提交請求
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())輸入 Schema
以下參數可在請求主體中使用。
範例請求主體
{
"model": "Qwen/Qwen3-235B-A22B-Instruct-2507",
"messages": [
{
"role": "user",
"content": "Hello"
}
],
"max_tokens": 1024,
"temperature": 0.7,
"stream": false
}輸出 Schema
API 傳回與 ChatCompletion 相容的回應。
範例回應
{
"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 將 300 多個 AI 模型直接整合至您的 AI 程式碼助手。一鍵安裝，即可使用自然語言生成圖片、影片，以及與 LLM 對話。
支援的客戶端
安裝
npx skills add AtlasCloudAI/atlas-cloud-skills設定 API 金鑰
從 Atlas Cloud 儀表板取得 API 金鑰，並設為環境變數。
export ATLASCLOUD_API_KEY="your-api-key-here"功能
安裝完成後，您可以在 AI 助手中使用自然語言存取所有 Atlas Cloud 模型。
MCP Server
Atlas Cloud MCP Server 透過 Model Context Protocol 將您的 IDE 與 300 多個 AI 模型連接。支援任何 MCP 相容的客戶端。
支援的客戶端
安裝
npx -y atlascloud-mcp設定
將以下設定新增至您 IDE 的 MCP 設定檔中。
{
"mcpServers": {
"atlascloud": {
"command": "npx",
"args": [
"-y",
"atlascloud-mcp"
],
"env": {
"ATLASCLOUD_API_KEY": "your-api-key-here"
}
}
}
}可用工具
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: 0.00063 (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.
