Can I run Qwen3-VL-30B-A3B-Thinking locally?

Yes, the weights are open and hosted on Hugging Face at Qwen/Qwen3-VL-30B-A3B-Thinking. It is a 30B MoE model, so you will need substantial VRAM; check the GGUF and quantisation guides if you want to run it on consumer hardware.

What does it cost and how long is the context?

Via API it is priced at $0.29 per million input tokens and $1.00 per million output tokens. The native context is 256K tokens, expandable to 1M according to Qwen.

What is the difference between this and the non-Thinking version?

The Thinking variant adds an extended internal reasoning step before answering, which improves performance on STEM, math and complex visual tasks at the cost of longer — and therefore more expensive — outputs.

Qwen3 VL 30B A3B Thinking

3384°

#llm #qwen #vision #reasoning #agents #long context

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Qwen | 262,144 token context | $0.29/M input, $1.00/M output. Qwen3-VL-30B-A3B-Thinking is a multimodal model that unifies strong text generation with visual understanding for images and videos. Its Thinking variant enhances reasoning in STEM, math, and complex tasks. It excels in perception of real-world/synthetic categories, 2D/3D spatial grounding, and long-form visual comprehension, achieving competitive multimodal benchmark results. For agentic use, it handles multi-image multi-turn instructions, video timeline alignments, GUI automation, and visual coding from sketches to debugged UI. Text performance matches flagship Qwen3 models, suiting document AI, OCR, UI assistance, spatial tasks, and agent research.

Guides for this model

How much VRAM do I actually need for local AI?

What is a GGUF file? Quantized models explained (Q4, Q8, fp8, NF4)

Open weights vs open source vs API-only: what you are actually allowed to do

All guides

Captain's notes

Written by Captain, the house editor

Notes from 2026-10-13

Qwen3-VL-30B-A3B-Thinking is a multimodal MoE model from Qwen that handles images, video and text in a single pass, with a Thinking variant that applies extended reasoning to STEM, math and complex visual tasks. The 256K native context, 32-language OCR, 3D spatial grounding and GUI automation set it apart from most vision-language models at this price. Open weights make it a serious option for researchers and developers who want to run or fine-tune locally.

Known for

Extended reasoning on STEM and math problems grounded in visual evidence
GUI automation on PC and mobile: recognises elements, invokes tools, completes tasks
2D and 3D spatial grounding, occlusion judgement and viewpoint reasoning
OCR across 32 languages, including low-light, blurred and ancient-character documents
Generates Draw.io, HTML, CSS and JS directly from images or video frames

Watch out for

The Thinking variant produces longer chains of thought; output tokens at $1.00/M add up fast on complex tasks.
30B MoE weights are large; running locally demands substantial VRAM — check your hardware before downloading.
Qwen has not published a detailed licence summary here; verify terms on the Hugging Face repository before commercial use.

Best for

Prompts to try

Debug a UI screenshot

Attach a screenshot of a broken web page or app screen. Identify every visible UI problem — layout breaks, clipped text, misaligned elements, contrast issues — and write the corrected HTML and CSS needed to fix each one.

Spatial reasoning from image

Attach a photo of a room or outdoor scene. List every object you can identify, estimate the rough spatial relationship between each pair of objects (left of, behind, occluded by), and flag anything that looks structurally unsafe or out of place.

STEM problem with diagram

Attach a physics or geometry diagram from a textbook. State the problem shown, identify all given values and unknowns, then solve step by step, citing which part of the diagram supports each step of your reasoning.

Compare with

Qwen3.7 Flash
API-only model
1,000,000 token context · $0.03/M input, $0.13/M output
Qwen3.8 Max (0902)
API-only model
1,000,000 token context · $2.00/M input, $6.00/M output
Qwen3.8 Omni Flash
API-only model
1,000,000 token context · $0.15/M input, $0.47/M output
Qwen3 VL 32B Instruct
API-only model
131,072 token context · $0.10/M input, $0.42/M output

More text models from qwen

Questions people ask

Can I run Qwen3-VL-30B-A3B-Thinking locally?: Yes, the weights are open and hosted on Hugging Face at Qwen/Qwen3-VL-30B-A3B-Thinking. It is a 30B MoE model, so you will need substantial VRAM; check the GGUF and quantisation guides if you want to run it on consumer hardware.
What does it cost and how long is the context?: Via API it is priced at $0.29 per million input tokens and $1.00 per million output tokens. The native context is 256K tokens, expandable to 1M according to Qwen.
What is the difference between this and the non-Thinking version?: The Thinking variant adds an extended internal reasoning step before answering, which improves performance on STEM, math and complex visual tasks at the cost of longer — and therefore more expensive — outputs.

Captain's verdict
A rare open-weights vision model that genuinely earns the reasoning label — worth the output-token cost when the task is hard.

Model card on Hugging Face

Captain writes these notes from the model's own description and model card. Prices and context sizes are the provider's at the time of writing and can change.

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Updated Oct 22, 2026

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This model has no popularity data from any source yet, so it carries a stand-in number below 7,000° that keeps the lists complete. It never ranks above a model with data. The breakdown appears here as soon as a source reports it.

Glossary: the terms used here

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Download-equivalents: Everything the score treats as a download, added up: downloads themselves, each torrent seeder as 1,000, each AI Horde image and each run on a generation site as one.
Log scale: Counting by tens, not by ones: going from 1,000 to 10,000 downloads adds the same degrees as going from 10,000 to 100,000. It keeps giants from crowding out everything else.
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Measured and estimated: Measured means two readings of a source exist and the difference is real. Estimated means only today's total is known and the past is spread over the model's life; the site says which one it is showing.
Seeder: Someone who keeps a copy of the weights online for others to download over a torrent, or a hosted mirror doing the same. Each one counts as 1,000 downloads.
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Generation sites: Sites that host their own copies of a model and let people generate with it, such as PixAI, SeaArt and Yodayo. Their downloads count like Civitai's and each generation run as one download.
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Full model (checkpoint): The weights themselves, the file that generates on its own. It gets a bonus because add-ons depend on it.
LoRA and embedding: Small add-on files that steer someone else's weights toward a style, a character or a concept. They cannot run alone.
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Age: Degrees taken off for how long ago the family's weights came out: 150° a year, up to 750°. It keeps old downloads that had years to pile up from freezing the top of the list.
Family boost: A small, bounded nudge for LoRAs of a family this site is pushing forward right now, always listed in the breakdown.
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Typical model: The middle of the catalog: half the models with a figure grew faster over the period, half slower.

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Qwen3 VL 30B A3B Thinking is a text model for the Text / LLM family listed on ModelTrends.ai, a read-only catalog of open source AI image, video and text models. Compare it with other Text / LLM models and check its heat score to see how it is trending.

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