Q

qwen3-235b-a22b

Entrada:$0.336/M
Salida:$1.344/M
Qwen3-235B-A22B is the flagship model of the Qwen3 series, with 23.5 billion parameters, using a Mixture of Experts (MoE) architecture. - Particularly suitable for complex tasks requiring high-performance Inference, such as coding, mathematics, and Multimodal applications.
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Uso comercial

Technical Specifications of qwen3-235b-a22b

SpecificationDetails
Model IDqwen3-235b-a22b
Model FamilyQwen3
ArchitectureMixture of Experts (MoE)
Parameter Scale23.5 billion parameters
Primary StrengthsCoding, mathematics, complex reasoning, multimodal applications
Inference ProfileHigh-performance inference for demanding tasks
Best Use CasesAdvanced code generation, mathematical problem-solving, multimodal workflows, complex enterprise AI tasks

What is qwen3-235b-a22b?

qwen3-235b-a22b is the flagship model in the Qwen3 series, designed for advanced AI workloads that require strong reasoning, efficient inference, and broad task coverage. Built with a Mixture of Experts (MoE) architecture, it is optimized to deliver high performance across complex scenarios while maintaining practical deployment efficiency.

This model is particularly suitable for users who need reliable output quality in areas such as software development, mathematical reasoning, and multimodal applications. Whether you are building intelligent assistants, automation pipelines, coding copilots, or analytical tools, qwen3-235b-a22b is positioned as a powerful general-purpose foundation model for demanding production environments.

Main features of qwen3-235b-a22b

  • Flagship Qwen3 model: qwen3-235b-a22b represents the top-tier model in the Qwen3 lineup, intended for the most challenging inference scenarios.
  • Mixture of Experts architecture: Its MoE design helps optimize performance and efficiency by activating specialized expert pathways for different tasks.
  • Strong coding capabilities: Well-suited for code generation, code explanation, refactoring, debugging support, and other software engineering workflows.
  • Advanced mathematical reasoning: Effective for complex calculations, symbolic reasoning, problem-solving, and structured analytical tasks.
  • Multimodal application potential: Designed to support advanced use cases that involve multimodal workflows and rich AI interactions.
  • High-performance inference: Built for tasks where response quality and computational capability are critical.
  • Production-friendly versatility: Can be applied across research, enterprise automation, developer tools, intelligent agents, and custom AI product experiences.

How to access and integrate qwen3-235b-a22b

Step 1: Sign Up for API Key

To start using qwen3-235b-a22b, first create an account on CometAPI and generate your API key from the dashboard. This key is required to authenticate all requests and securely access the model through the API platform.

Step 2: Send Requests to qwen3-235b-a22b API

Once you have your API key, you can call the OpenAI-compatible chat completions endpoint and specify qwen3-235b-a22b as the model.

curl https://api.cometapi.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $COMETAPI_API_KEY" \
  -d '{
    "model": "qwen3-235b-a22b",
    "messages": [
      {
        "role": "user",
        "content": "Write a Python function that checks whether a number is prime."
      }
    ]
  }'
from openai import OpenAI

client = OpenAI(
    api_key="YOUR_COMETAPI_API_KEY",
    base_url="https://api.cometapi.com/v1"
)

response = client.chat.completions.create(
    model="qwen3-235b-a22b",
    messages=[
        {"role": "user", "content": "Write a Python function that checks whether a number is prime."}
    ]
)

print(response.choices[0].message.content)

Step 3: Retrieve and Verify Results

After sending your request, the API will return the model's generated output in a structured response format. You can then parse the returned content, display it in your application, and verify that the result matches your expected quality, format, and task requirements before deploying it into production workflows.