Qwen3 VL 235B A22B Thinking

Chat Completions

qwen3-vl-235b-a22b-thinking

Qwen|Created Sep 24, 2025|262.1k context

Chat Completions

Qwen3-VL-235B-A22B Thinking is a multimodal model that combines advanced text generation with visual understanding for images and video, specifically optimized for multimodal reasoning in STEM and math. It delivers robust perception, strong spatial (2D/3D) understanding, and long-form visual comprehension, showing competitive performance in public benchmarks for both perception and reasoning. Beyond analysis, Qwen3-VL supports agentic interaction, tool use, following complex instructions in multi-image dialogues, aligning text with video timelines, and automating GUI operations. The model also enables visual coding workflows, such as turning sketches into code and assisting with UI debugging, while maintaining strong text-only capabilities on par with Qwen3 language models. This makes it ideal for use cases like document AI, multilingual OCR, UI/software help, spatial reasoning, and vision-language agent research.

Overview Specifications Activity Performance Uptime Examples API Reference

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Pricing-50%

Pay-as-you-go rates for this model. More details can be found here.

Input Tokens (1M)

$0.35

Output Tokens (1M)

$4.20

Capabilities

Input Modalities

TextImage

Output Modalities

Text

Supported Parameters

Available parameters for API requests

Max Completion TokensPresence PenaltyResponse FormatTemperatureTool ChoiceToolsTop P

Usage Analytics

Token usage of this model on our platform

Throughput

Time-To-First-Token (TTFT)

Code Example

Example code for using this model through our API with Python (OpenAI SDK) or cURL. Replace placeholders with your API key and model ID.

Basic request example. Ensure API key permissions. For more details, see our documentation.

from openai import OpenAI

client = OpenAI(
    base_url="https://api.naga.ac/v1",
    api_key="YOUR_API_KEY",
)

resp = client.chat.completions.create(
    model="qwen3-vl-235b-a22b-thinking",
    messages=[
        {"role": "user", "content": "What's 2+2?"}
    ],
    temperature=0.2,
)
print(resp.choices[0].message.content)