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

VISIONPricing:Input $0.32 / Cached $0.016 / Output $1.6Concurrent 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 is a 2-billion-parameter dense vision-language model from the Qwen3-VL series, developed by Qwen. It integrates a vision encoder with a dense transformer decoder, utilizing Interleaved-MRoPE positional embeddings and DeepStack multi-level feature fusion. This model excels at visual understanding tasks including OCR, document/chart analysis, spatial reasoning, and video comprehension, and natively supports tool calling. The "Thinking" edition explicitly generates a chain-of-thought before its final answer, making it suitable for applications requiring transparent reasoning.

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Qwen3-VL-2B-Thinking Overview

Qwen3-VL-2B-Thinking is a 2-billion-parameter dense vision-language model from the Qwen3-VL series, developed by Qwen. It combines a vision encoder with a dense transformer decoder, leveraging Interleaved-MRoPE positional embeddings and DeepStack multi-level feature fusion. This model is distinguished by its "Thinking" edition, which generates an explicit chain-of-thought prior to its final answer, providing transparency in its reasoning process. It is designed to run efficiently on FuriosaAI RNGD hardware using Furiosa-LLM, with a tensor-parallel size of 8 PEs per card.

Key Capabilities

  • Multimodal Vision-Language: Processes both images and text inputs, supporting OpenAI-style multimodal chat messages with image_url content parts.
  • Explicit Reasoning: Outputs a detailed chain-of-thought, parsed by Furiosa-LLM's qwen3 reasoning parser, separate from the final answer.
  • Native Tool Calling: Supports function calling via the hermes tool-call parser, enabling integration with external tools and APIs.
  • Comprehensive Visual Understanding: Capable of tasks such as OCR, document and chart analysis, spatial reasoning, and video comprehension.

Good for

  • Applications requiring transparent, step-by-step reasoning in multimodal contexts.
  • Visual understanding tasks where detailed analysis of images and videos is crucial.
  • Integrating with external tools and functions through native tool-calling capabilities.
  • Deployment on FuriosaAI RNGD hardware for optimized performance.

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