LightweightAlibaba

Qwen 2.5 VL 32B

Qwen 2.5 VL 32B is Alibaba's lightweight multimodal model supporting text and image inputs with a 128K token context window.

Context 128K
Tier Lightweight
Modalities text, image
Input from
$0.200 / 1M tokens
across 1 provider
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API Pricing

ProviderInput / 1MOutput / 1MSpeedTTFTUpdated
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OpenRouter
$0.200$0.60066.5 t/s978ms4/14/2026

Prices updated daily. Last check: 4/14/2026

Model Details

General

Creator
Alibaba
Family
Qwen
Tier
Lightweight
Context Window
128K
Modalities
Text, Image

Capabilities

Tool Calling
No
Open Source
No

Strengths & Limitations

  • Supports both text and image inputs for multimodal processing
  • 128K token context window allows processing of lengthy documents with images
  • Output speed of 66.52 tokens per second provides efficient inference
  • Lightweight tier offers resource efficiency compared to larger multimodal models
  • Fast time to first token at 978ms enables responsive interactions
  • Part of established Qwen model family with proven capabilities
  • No tool calling or function execution capabilities
  • Proprietary model with weights not publicly available
  • Limited to text and image modalities without video or audio support
  • Smaller parameter count may limit complex reasoning compared to flagship models

Key Features

•Text and image multimodal input processing
•128K token context window
•Streaming response generation
•66.52 tokens per second output speed
•978ms time to first token latency
•Lightweight inference architecture
•Cross-modal understanding capabilities
•Document and image co-processing

About Qwen 2.5 VL 32B

Qwen 2.5 VL 32B is a lightweight multimodal model developed by Alibaba as part of the Qwen model family. Positioned as an efficient option within the Qwen lineup, this model balances capable performance with resource efficiency for applications requiring both text and visual understanding. The model supports text and image inputs with a 128,000 token context window, enabling processing of lengthy documents alongside visual content. Performance benchmarks show an output speed of 66.52 tokens per second with a time to first token of 978 milliseconds. The model processes visual inputs alongside text, making it suitable for tasks that require understanding of both modalities simultaneously. Qwen 2.5 VL 32B serves applications where multimodal capabilities are needed without the computational overhead of larger flagship models. Its lightweight classification makes it appropriate for scenarios requiring faster inference or higher throughput while maintaining visual understanding capabilities.

Common Use Cases

Qwen 2.5 VL 32B is well-suited for applications requiring efficient multimodal processing where speed and resource efficiency are priorities. Its lightweight design makes it appropriate for high-volume document analysis tasks that include charts, diagrams, or images, content moderation workflows processing visual and textual content, and educational applications that need to understand textbook pages or instructional materials. The model's balance of visual understanding capabilities with fast inference makes it valuable for customer service applications processing screenshots alongside text, e-commerce product analysis combining descriptions with images, and automated content processing pipelines where multimodal understanding is needed at scale.

Frequently Asked Questions

How much does Qwen 2.5 VL 32B cost per million tokens?

Qwen 2.5 VL 32B pricing varies by provider and pricing type. Check the pricing table above for current rates across all providers offering this model.

What is Qwen 2.5 VL 32B best used for?

Qwen 2.5 VL 32B excels at multimodal tasks requiring both text and image understanding where efficiency is important. It's particularly effective for document analysis with visual elements, content processing workflows, and applications needing fast multimodal inference at scale.

Does Qwen 2.5 VL 32B support function calling or tool use?

No, Qwen 2.5 VL 32B does not support function calling or tool execution capabilities. The model is focused on multimodal understanding tasks involving text and image inputs rather than agentic workflows requiring external tool integration.