Qwen: Qwen2.5-VL 72B Instruct
This model is no longer available.Add AI to your application with Puter.js.
Explore Other Models
Model Card
Qwen2.5-VL 72B Instruct is Alibaba's flagship open-source vision-language model, matching state-of-the-art closed models like GPT-4o and Claude 3.5 Sonnet on multimodal tasks.
The model excels at document understanding (96.4 on DocVQA), OCR (88.8 on OCRBench), and structured data extraction from invoices, forms, tables, and charts. On MMMU it scores 70.2, and across 21 benchmarks it outperforms Gemini 2.0 Flash, GPT-4o, and Claude 3.5 Sonnet on 13 of them.
Video understanding extends to over one hour of footage with second-level event pinpointing, enabled by dynamic FPS sampling and absolute time encoding. The model also functions as a visual agent capable of computer and phone use. A strong choice for developers building document pipelines, OCR workflows, visual Q&A systems, or multimodal agents.
Context Window 131K
tokens
Max Output 8K
tokens
Input Cost $2.8
per million tokens
Output Cost $8.4
per million tokens
Input text, image
modalities
Tool Use Yes
Knowledge Cutoff Apr 2024
Release Date Sep 2024
Code Example
Add AI to your app with the Puter.js AI API — no API keys or setup required.
// npm install @heyputer/puter.js
import { puter } from '@heyputer/puter.js';
puter.ai.chat("Explain quantum computing in simple terms").then(response => {
document.body.innerHTML = response.message.content;
});
<html>
<body>
<script src="https://js.puter.com/v2/"></script>
<script>
puter.ai.chat("Explain quantum computing in simple terms").then(response => {
document.body.innerHTML = response.message.content;
});
</script>
</body>
</html>
More AI Models From Qwen
Qwen3.8 2.4T A95B
Qwen3.8 2.4T A95B is Alibaba's open-weight release of its Qwen3.8 Max flagship, a sparse mixture-of-experts model with 2.4 trillion total parameters and 95 billion active per token, routed across 512 experts. It uses a hybrid attention design (Gated DeltaNet and Gated Attention layers) across 92 layers, with a native 262K context window and thinking mode enabled for every response. Alibaba reports 93.0 on PaperBench (ahead of GPT-5.6 Sol's 90.5), 92.6 on GPQA Diamond, 86.6 on Terminal-Bench 2.1, and 67.7 on SWE-bench Pro, positioning it for coding, research, and long-horizon agentic work. It gives developers access to Qwen-Max-class capability under open weights, useful for teams that want frontier-level coding and agentic performance without a closed API.
ChatQwen3.8 Max
Qwen3.8 Max is Alibaba's flagship large language model, released August 3, 2026 as the most capable model in the Qwen family to date. It uses a mixture-of-experts architecture with 2.4 trillion total parameters and about 95 billion active per request, and accepts text, image, and video input with a context window of up to 1 million tokens. Alibaba positions it for coding and long-horizon agentic work: in testing the model ran autonomously for over 10 days building a self-evolving software harness. Reported benchmarks include 93.0 on PaperBench, 82.8 on IFBench, 86.6 on Terminal-Bench 2.1, and 86.1 on OSWorld-Verified, ahead of Claude Opus 4.8 on several coding and agent tasks and roughly matching Claude Fable 5 and GPT-5.6 Sol, though it trails both on some evaluations. On the Arena.AI leaderboard it ranks as the top Chinese model for text tasks. Alibaba plans to open-source the weights on Hugging Face and ModelScope.
ChatQwen3.7 Flash
Qwen3.7 Flash is the low-cost, fast tier of Alibaba's Qwen3.7 family, released in July 2026. It is a vision-language model that accepts text, image, and video input across a 1 million-token context window, with reasoning enabled by default and a 262K-token thinking budget. Alibaba positions it as an upgrade over Qwen3.6 Flash in multimodal understanding and agent execution, with better object recognition and spatial intelligence. It supports function calling and structured outputs. Pricing is tiered by prompt length. Requests under 32K input tokens cost $0.03/$0.13 per million, rising to $0.20/$0.80 above 256K. Alibaba published no benchmarks at launch. An independent vision evaluation by Roboflow measured strong object identification (84.4%) but weak OCR and object detection, so it fits high-volume multimodal tasks (classification, visual agents, lightweight extraction) better than document-heavy pipelines.
Frequently Asked Questions
You can access Qwen2.5-VL 72B Instruct by Qwen through Puter.js AI API. Include the library in your web app or Node.js project and start making calls with just a few lines of JavaScript — no backend and no configuration required. You can also use it with Python or cURL via Puter's OpenAI-compatible API.
Yes, it is free if you're using it through Puter.js. With the User-Pays Model, you can add Qwen2.5-VL 72B Instruct to your app at no cost — your users pay for their own AI usage directly, making it completely free for you as a developer.
| Price per 1M tokens | |
|---|---|
| Input | $2.8 |
| Output | $8.4 |
Qwen2.5-VL 72B Instruct was created by Qwen and released on Sep 2024.
Qwen2.5-VL 72B Instruct supports a context window of 131K tokens. For reference, that is roughly equivalent to 262 pages of text.
Qwen2.5-VL 72B Instruct can generate up to 8K tokens in a single response.
Qwen2.5-VL 72B Instruct has a knowledge cutoff date of Apr 2024. This means the model was trained on data available up to that date.
Qwen2.5-VL 72B Instruct accepts the following input types: text, image. It produces: text.
Yes, Qwen2.5-VL 72B Instruct supports tool use (function calling), allowing it to interact with external tools, APIs, and data sources as part of its response flow.
Yes — the Qwen2.5-VL 72B Instruct API works with any JavaScript framework, Node.js, or plain HTML through Puter.js. Just include the library and start building. See the documentation for more details.
Get started with Puter.js
Add AI to your application without worrying about API keys or setup.
Explore Models View Tutorials