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Available now on ModelsLab · Language Model

Qwen2.5-VL (72B) InstructVision. Language. Understanding.

Try Qwen2.5-VL (72B) InstructAPI Documentation

Multimodal Intelligence at Scale

Visual Reasoning

Image, Video, Document Understanding

Process images, videos up to 1 hour, and documents with precise visual localization and event detection.

Extended Context

32K to 128K Token Window

Handle long-form content and complex queries with native 32K tokens, extendable to 128K using YaRN.

Production Ready

Fine-Tuning and Customization

Optimize for your domain using LoRA-based fine-tuning on dedicated GPUs for personalized performance.

Examples

See what Qwen2.5-VL (72B) Instruct can create

Copy any prompt below and try it yourself in the playground.

Document Analysis

“Analyze this invoice image and extract all line items, totals, and payment terms in structured JSON format.”

Video Summarization

“Watch this 30-minute tutorial video and provide a detailed summary with timestamps of key concepts and action items.”

Chart Interpretation

“Examine this quarterly sales chart and identify trends, anomalies, and provide forecasting insights for the next quarter.”

Multi-Image Reasoning

“Compare these three product photos and generate a detailed comparison report highlighting design differences and material quality.”

For Developers

A few lines of code.
Multimodal intelligence. Few lines.

ModelsLab handles the infrastructure: fast inference, auto-scaling, and a developer-friendly API. No GPU management needed.

  • Serverless: scales to zero, scales to millions
  • Pay per token, no minimums
  • Python and JavaScript SDKs, plus REST API
API Documentation 
import requests


response = requests.post(
    "https://modelslab.com/api/v7/llm/chat/completions",
    json={
  "key": "YOUR_API_KEY",
  "prompt": "",
  "model_id": ""
}
)
print(response.json())

FAQ

Common questions about Qwen2.5-VL (72B) Instruct

Read the docs

Qwen2.5-VL (72B) Instruct excels at vision-language tasks including image analysis, video comprehension up to 1 hour, document understanding, and visual reasoning. It supports 201 languages and handles complex multimodal queries with high accuracy.

The default context length is 32,768 tokens, extendable up to 128K tokens using YaRN. Maximum output is 33K tokens per response for comprehensive long-form generation.

Yes. LoRA-based fine-tuning is supported on dedicated GPUs, allowing you to customize the model with your own data for improved domain-specific performance.

The model runs efficiently on high-performance GPU setups, supporting both 8x NVIDIA L40S and 8x NVIDIA H100 configurations for optimal throughput and latency.

Qwen2.5-VL (72B) Instruct contains 73.4 billion parameters, making it the largest model in the Qwen2.5-VL series with superior reasoning and understanding capabilities.

The model supports 201 languages natively, making it suitable for global applications requiring multilingual document analysis, video understanding, and cross-language reasoning tasks.

Ready to create?

Start generating with Qwen2.5-VL (72B) Instruct on ModelsLab.

Try Qwen2.5-VL (72B) InstructAPI Documentation