Qwen2.5 VL 72B Instruct
3720°
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Qwen | 128,000 token context | $0.80/M input, $1.00/M output. Qwen2.5-VL is proficient in recognizing common objects such as flowers, birds, fish, and insects. It is also highly capable of analyzing texts, charts, icons, graphics, and layouts within images.
Guides for this model
Ollama vs LM Studio (2026): which local AI app to use, and which top models each one runs
How much VRAM do I actually need for local AI?
Open weights vs open source vs API-only: what you are actually allowed to do
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Captain's notes
Notes from 2026-10-13
Qwen2.5 VL 72B Instruct is Alibaba's flagship vision-language model, pairing a 72-billion-parameter language core with strong visual understanding across images and long-form video. It stands out for agentic computer and phone use, precise object localization, and structured output from documents like invoices and forms. It suits developers building document pipelines, visual agents, or anything that needs a capable open-weights multimodal model at a reasonable API price.
Known for
- Reads charts, tables, icons and dense document layouts with high accuracy.
- Acts as a visual agent: can reason over a screen and direct tools for computer or phone use.
- Localizes objects with bounding boxes or points and outputs stable JSON coordinates.
- Understands videos over one hour long and can pinpoint specific moments within them.
- Generates structured output from scanned invoices, forms and tables.
Watch out for
- The licence is Qwen's own, not Apache or MIT — check it before any commercial deployment.
- At 72B parameters, running locally demands serious hardware; expect significant VRAM requirements.
- Benchmark scores on MMMU and MMMU_Pro sit roughly level with GPT-4o, not clearly ahead.
Prompts to try
Extract invoice to JSON
I'm attaching a scanned invoice. Extract every line item, unit price, quantity, subtotal, tax amount and grand total. Return the result as a single JSON object with clearly named keys. If a field is illegible or absent, use null.
Describe and localize objects
Look at the attached image. List every distinct object you can identify, give each one a short label, and provide its bounding box as [x_min, y_min, x_max, y_max] in pixel coordinates relative to the image dimensions. Return the result as a JSON array.
Analyze a chart
I'm attaching a chart or graph. Describe what it shows, identify the axes and units, summarize the main trend or finding in two sentences, and flag any anomalies or data points that stand out. Be specific about values where you can read them.
Compare with
- Qwen3.7 FlashAPI-only model1,000,000 token context · $0.03/M input, $0.13/M output
- Qwen3.8 Max (0902)API-only model1,000,000 token context · $2.00/M input, $6.00/M output
- Qwen3.8 Omni FlashAPI-only model1,000,000 token context · $0.15/M input, $0.47/M output
- Qwen3.7 PlusAPI-only model1,000,000 token context · $0.32/M input, $1.28/M output
More text models from qwen
Questions people ask
- Can I run Qwen2.5 VL 72B locally?
- Yes, the weights are open and available on Hugging Face under Qwen's own licence. At 72B parameters you will need substantial VRAM — realistically a multi-GPU setup or a high-end single card.
- What does it cost to use via API?
- Pricing is $0.80 per million input tokens and $1.00 per million output tokens, with a 128,000-token context window.
- How does the 72B compare to the smaller Qwen2.5 VL models?
- Alibaba also offers 3B and 7B variants; the 72B is the top of the family and the one to reach for when accuracy on complex documents, long video or agentic tasks matters more than speed or cost.
Captain's verdictA serious open-weights vision model that earns its place when you need document parsing, visual agents, or long-video understanding without a closed-API dependency.
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.
Ranking badge
Updated Oct 22, 2026
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How this score adds up
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Glossary: the terms used here
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- Log scale
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- 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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- 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
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- A stable number below the floor given to a model no source reports on yet, so lists stay complete. It never ranks above a model with data.
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- Trend
- How a model's growth over a period compares with the typical model's growth over the same period. Faster than typical reads as a rise, slower as a fall.
- Typical model
- The middle of the catalog: half the models with a figure grew faster over the period, half slower.
Qwen2.5 VL 72B Instruct 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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