Vision

Apache 2.0 (2B, 7B); Tongyi Qianwen License (72B)Open weights where releasedUpdated August 2026

Qwen2 VL

Qwen2 VL remains useful as a Qwen vision-language baseline for multimodal experiments.

Alibaba Qwen · Qwen

Editorial review

Reviewed byOpenSourcesAI EditorialLast updatedAugust 2026SourcesCanonical artifact, Representative model card

Model checkpoints, context windows, provider support, local runtime compatibility, and license terms can change quickly. Verify the exact model card before production or commercial use.

Best for

Vision-language baseline comparisons and multimodal prototypes.

Who should use it

  • Vision-language baseline comparisons and multimodal prototypes.
  • Builders who want local or self-hosted testing options.

Common workflows

  • Vision-language baseline workflows
  • vision workflows
  • multimodal workflows
  • legacy workflows

Deployment and hardware notes

The representative 7B Instruct checkpoint needs ~5.0 GB at Q4_K_M (8.3B parameters), the same footprint as its 2.5 successor.

License and usage notes

Apache 2.0 (2B, 7B); Tongyi Qianwen License (72B). Open weights where released. Verify the exact model card and license terms for the checkpoint or hosted provider you use.

Strengths

  • Open weights where released model option for Qwen workflows.
  • Vision-language baseline comparisons and multimodal prototypes.
  • Because preprocessing is identical to Qwen2.5-VL, moving between the two is a checkpoint swap rather than a pipeline change — worth measuring before staying on this generation.

Limitations

  • A 32,768-token window against Qwen2.5-VL's 128,000 is the sharpest limit here: high-resolution images consume tokens quickly, so long documents run out of room. Kept mainly as a baseline for comparisons.
  • The representative 7B Instruct checkpoint needs ~5.0 GB at Q4_K_M (8.3B parameters), the same footprint as its 2.5 successor.
  • Context window and limits: 32,768 tokens.
  • Verify the exact model card, provider docs, license, and serving support before production use.

Local workflow notes

Because preprocessing is identical to Qwen2.5-VL, moving between the two is a checkpoint swap rather than a pipeline change — worth measuring before staying on this generation.

Local runtimes: Ollama where supported, LM Studio where supported, llama.cpp where supported, Transformers

Platforms: Windows, macOS, Linux

Vision spec

This page covers a family of checkpoints. The figures below describe Qwen2-VL-7B-Instruct, its representative release — other sizes in the family differ.

MemoryVaries by size; the 7B Instruct checkpoint needs ~5.0 GB at Q4_K_M (8.3B parameters)Image inputDynamic resolution, 3,136 to 12.8M pixels, 14-pixel patches; video inputContext32,768 tokens

The first Qwen-VL generation with native dynamic resolution and M-RoPE, and the direct predecessor of Qwen2.5-VL — identical image preprocessing, materially weaker document and agent performance.

Sources to verify

Canonical artifactRepresentative model card

Related resources

Continue with model source notes, local tools, and implementation guides related to this model.

Qwen family hubAI tools directoryModel selection guideModel source notesModels directory
HardwareVaries by size; the 7B Instruct checkpoint needs ~5.0 GB at Q4_K_M (8.3B parameters)RuntimeOllama or LM Studio where supported, llama.cpp, Transformers, vLLMContext32,768 tokensLast updated2026
Canonical artifact →

Model ecosystem connections

Use these next-step links to move from this profile into related tools, comparisons, guides, stacks, and curated shortlists.

Recommended runtimes and tools

Ollama →LM Studio →llama.cpp →Transformers →vLLM →

Setup and deployment

Check if this model fits your GPU →What is quantization? GGUF, Q4, Q8 explained →What is VRAM and how much do you need? →

Related model pages

Qwen family hub →Qwen3 14B →Qwen3 235B A22B →Qwen3 235B A22B Thinking →Qwen3 VL →Llama 3.2 Vision →Qwen2.5 32B →

Guides, stacks, and comparisons

AI tools directory →Model selection guide →All model comparisons →

Related models

Qwen3 14BMultilingual chat, assistant workflows, and Qwen-family comparisons.Qwen3 235B A22BBuilders testing frontier-style open-weight reasoning and coding in hosted or multi-GPU environments.Qwen3 235B A22B ThinkingMultilingual chat, assistant workflows, and Qwen-family comparisons.Qwen3 VLBuilders adding visual understanding to open AI workflows.Llama 3.2 VisionVision-language experiments, screenshot reasoning, and multimodal app prototypes.Qwen2.5 32B32B open-weight model in the Qwen family.

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