furiosa-ai/Qwen3-VL-2B-Thinking

VISIONConcurrent Unit Cost:1Model Size:2BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Jul 24, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

Qwen3-VL-2B-Thinking by Qwen is a 2-billion-parameter dense vision-language model from the Qwen3-VL series, designed for multimodal understanding. It integrates a vision encoder with a transformer decoder, utilizing Interleaved-MRoPE and DeepStack for robust image and video processing alongside text. This "Thinking" edition explicitly generates a chain-of-thought before its final answer, making it suitable for complex visual reasoning, OCR, document analysis, and tool-calling tasks.

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Overview of Qwen3-VL-2B-Thinking

Qwen3-VL-2B-Thinking is a 2-billion-parameter dense vision-language model developed by Qwen, part of the Qwen3-VL series. This model is distinguished by its ability to process both images and text inputs, making it highly versatile for multimodal applications. It employs a vision encoder paired with a dense transformer decoder, leveraging Interleaved-MRoPE positional embeddings and DeepStack multi-level feature fusion for comprehensive visual understanding.

Key Capabilities

  • Vision-Language Integration: Accepts OpenAI-style multimodal chat messages, including image_url content parts alongside text, enabling rich visual interaction.
  • Explicit Chain-of-Thought Reasoning: As a "Thinking" edition, it generates an explicit chain-of-thought before providing its final answer, which can be parsed separately, aiding in transparency and debugging of its reasoning process.
  • Tool Calling Support: Natively supports tool (function) calling, allowing it to interact with external systems and perform actions based on its understanding.
  • Broad Visual Understanding: Excels in tasks such as OCR, document and chart analysis, spatial reasoning, and video comprehension.

Good for

  • Applications requiring detailed visual analysis combined with textual understanding.
  • Use cases where an explicit reasoning trace (chain-of-thought) is beneficial for interpretability.
  • Integrating AI with external tools or APIs through function calling.
  • Deployment on FuriosaAI RNGD hardware, with specific support for Furiosa-LLM inference, though it also runs on other frameworks like vLLM and Transformers.

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