Qwen3-235B-A22B-Instruct 2507 API by Alibaba
235B-parameter MoE thinking model in Qwen3 series.
مثال الكود
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)التثبيت
قم بتثبيت الحزمة المطلوبة للغة البرمجة الخاصة بك.
pip install requestsالمصادقة
تتطلب جميع طلبات API المصادقة عبر مفتاح API. يمكنك الحصول على مفتاح API الخاص بك من لوحة تحكم Atlas Cloud.
export ATLASCLOUD_API_KEY="your-api-key-here"ترويسات HTTP
import os
API_KEY = os.environ.get("ATLASCLOUD_API_KEY")
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {API_KEY}"
}لا تكشف أبدًا مفتاح API الخاص بك في الكود من جانب العميل أو المستودعات العامة. استخدم متغيرات البيئة أو وكيل الخادم الخلفي بدلاً من ذلك.
إرسال طلب
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
المعاملات التالية مقبولة في نص الطلب.
مثال على نص الطلب
{
"model": "Qwen/Qwen3-235B-A22B-Instruct-2507",
"messages": [
{
"role": "user",
"content": "Hello"
}
],
"max_tokens": 1024,
"temperature": 0.7,
"stream": false
}Output Schema
تُرجع API استجابة متوافقة مع ChatCompletion.
مثال على الاستجابة
{
"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
يدمج Atlas Cloud Skills أكثر من 300 نموذج ذكاء اصطناعي مباشرة في مساعد البرمجة بالذكاء الاصطناعي الخاص بك. أمر واحد للتثبيت، ثم استخدم اللغة الطبيعية لتوليد الصور ومقاطع الفيديو والدردشة مع LLM.
العملاء المدعومون
التثبيت
npx skills add AtlasCloudAI/atlas-cloud-skillsإعداد مفتاح API
احصل على مفتاح API الخاص بك من لوحة تحكم Atlas Cloud وعيّنه كمتغير بيئة.
export ATLASCLOUD_API_KEY="your-api-key-here"الإمكانيات
بمجرد التثبيت، يمكنك استخدام اللغة الطبيعية في مساعد الذكاء الاصطناعي الخاص بك للوصول إلى جميع نماذج Atlas Cloud.
MCP Server
يربط Atlas Cloud MCP Server بيئة التطوير الخاصة بك بأكثر من 300 نموذج ذكاء اصطناعي عبر Model Context Protocol. يعمل مع أي عميل متوافق مع MCP.
العملاء المدعومون
التثبيت
npx -y atlascloud-mcpالتكوين
أضف التكوين التالي إلى ملف إعدادات MCP في بيئة التطوير الخاصة بك.
{
"mcpServers": {
"atlascloud": {
"command": "npx",
"args": [
"-y",
"atlascloud-mcp"
],
"env": {
"ATLASCLOUD_API_KEY": "your-api-key-here"
}
}
}
}الأدوات المتاحة
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.
