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

LLM

Qwen3-235B-A22B-Instruct 2507 API by Alibaba

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

Qwen3-235B-A22B-Instruct-2507

235B-parameter MoE thinking model in Qwen3 series.

Parameter

Codebeispiel
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)

Installieren

Installieren Sie das erforderliche Paket für Ihre Programmiersprache.

bash

pip install requests

Authentifizierung

Alle API-Anfragen erfordern eine Authentifizierung über einen API-Schlüssel. Sie können Ihren API-Schlüssel über das Atlas Cloud Dashboard erhalten.

bash

export ATLASCLOUD_API_KEY="your-api-key-here"

HTTP-Header

python

import os

API_KEY = os.environ.get("ATLASCLOUD_API_KEY")
headers = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {API_KEY}"
}

Schützen Sie Ihren API-Schlüssel

Geben Sie Ihren API-Schlüssel niemals in clientseitigem Code oder öffentlichen Repositories preis. Verwenden Sie stattdessen Umgebungsvariablen oder einen Backend-Proxy.

Anfrage senden

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())

Eingabe-Schema

Die folgenden Parameter werden im Anfragekörper akzeptiert.

Gesamt: 9Erforderlich: 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

Beispiel-Anfragekörper

json

{
  "model": "Qwen/Qwen3-235B-A22B-Instruct-2507",
  "messages": [
    {
      "role": "user",
      "content": "Hello"
    }
  ],
  "max_tokens": 1024,
  "temperature": 0.7,
  "stream": false
}

Ausgabe-Schema

Die API gibt eine ChatCompletion-kompatible Antwort zurück.

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.

Beispielantwort

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 integriert über 300 KI-Modelle direkt in Ihren KI-Coding-Assistenten. Ein Befehl zur Installation, dann verwenden Sie natürliche Sprache, um Bilder, Videos zu generieren und mit LLMs zu chatten.

Unterstützte Clients

Claude Code

OpenAI Codex

Gemini CLI

Cursor

Windsurf

VS Code

Trae

GitHub Copilot

Cline

Roo Code

Amp

Goose

Replit

40+ unterstützte clients

Installieren

bash

npx skills add AtlasCloudAI/atlas-cloud-skills

API-Schlüssel einrichten

Erhalten Sie Ihren API-Schlüssel über das Atlas Cloud Dashboard und setzen Sie ihn als Umgebungsvariable.

bash

export ATLASCLOUD_API_KEY="your-api-key-here"

Funktionen

Nach der Installation können Sie natürliche Sprache in Ihrem KI-Assistenten verwenden, um auf alle Atlas Cloud Modelle zuzugreifen.

BildgenerierungGenerieren Sie Bilder mit Modellen wie Nano Banana 2, Z-Image und mehr.

VideoerstellungErstellen Sie Videos aus Text oder Bildern mit Kling, Vidu, Veo usw.

LLM-ChatChatten Sie mit Qwen, DeepSeek und anderen großen Sprachmodellen.

Medien-UploadLaden Sie lokale Dateien für Bildbearbeitung und Bild-zu-Video-Workflows hoch.

Mehr erfahren

github.com/AtlasCloudAI/atlas-cloud-skills

MCP-Server

Der Atlas Cloud MCP-Server verbindet Ihre IDE mit über 300 KI-Modellen über das Model Context Protocol. Funktioniert mit jedem MCP-kompatiblen Client.

Unterstützte Clients

Cursor

VS Code

Windsurf

Claude Code

OpenAI Codex

Gemini CLI

Cline

Roo Code

100+ unterstützte clients

Installieren

bash

npx -y atlascloud-mcp

Konfiguration

Fügen Sie die folgende Konfiguration zur MCP-Einstellungsdatei Ihrer IDE hinzu.

json

{
  "mcpServers": {
    "atlascloud": {
      "command": "npx",
      "args": [
        "-y",
        "atlascloud-mcp"
      ],
      "env": {
        "ATLASCLOUD_API_KEY": "your-api-key-here"
      }
    }
  }
}

Verfügbare Werkzeuge

atlas_generate_imageGenerieren Sie Bilder aus Textbeschreibungen.

atlas_generate_videoErstellen Sie Videos aus Text oder Bildern.

atlas_chatChatten Sie mit großen Sprachmodellen.

atlas_list_modelsDurchsuchen Sie über 300 verfügbare KI-Modelle.

atlas_quick_generateInhaltserstellung in einem Schritt mit automatischer Modellauswahl.

atlas_upload_mediaLaden Sie lokale Dateien für API-Workflows hoch.

Mehr erfahren

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.

Qwen3.6 35B A3B

Eingabe:$0.161/M Tokens

Ausgabe:$0.965/M Tokens

Max. Ausgabe:65.54K

$0.161/0.965M ein/aus

NEW

The latest Qwen reasoning model.

LLM

Qwen3.6 Plus

Eingabe:$0.325/M Tokens

Ausgabe:$1.95/M Tokens

Max. Ausgabe:65.54K

$0.325/1.95M ein/aus

NEW

Qwen3.5 represents a significant leap forward, integrating breakthroughs in multimodal learning, architectural efficiency, reinforcement learning scale, and global accessibility to empower developers and enterprises with unprecedented capability and efficiency.

LLM