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Qwen2-VL-72B-Instruct

Multimodal
Alibaba

An instruction-tuned large multimodal model that excels at visual understanding and step-by-step reasoning. It supports image and video input with dynamic resolution processing and improved positional embeddings (M-ROPE), enabling advanced capabilities such as complex problem-solving, multilingual text recognition in images, and agentic interaction in video contexts.

Key Specifications

Parameters
73.4B
Context
-
Release Date
August 29, 2024
Average Score
76.4%

Timeline

Key dates in the model's history
Announcement
August 29, 2024
Last Update
July 19, 2025
Today
October 5, 2026

Technical Specifications

Parameters
73.4B
Training Tokens
-
Knowledge Cutoff
June 30, 2023
Family
-
Capabilities
MultimodalZeroEval

Benchmark Results

Model performance metrics across various tests and benchmarks

Multimodal

Working with images and visual data
ChartQA
## Evaluation AI: you in order to I work or evaluation by scale. need to more information about that, that specifically you in order to I and by criteria • Self-reported
88.3%

Other Tests

Specialized benchmarks
DocVQAtest
score • Self-reported
96.5%
EgoSchema
score • Self-reported
77.9%
InfoVQAtest
Evaluation • Self-reported
84.5%
MathVista-Mini
score • Self-reported
70.5%
MMBench_test
# Evaluation Evaluation indicates on then, how well well model solves problem or task. We we provide several by which can evaluate quality solutions model: 1. **correctness**: whether answer model correct ()? In some cases model can obtain points, even if she/it uses other method solutions, than solution - in other cases, model should follow (such how verification, manner). 2. ****: whether model task fully, or only her/its part? whether she/it all possible cases or only some from them? 3. **Efficiency**: whether approach model to solving tasks ? whether model steps? 4. ****: whether solution model and for understanding? model will more score, if she/it not its steps or not verifies its work, when this necessary. model more high evaluation, if her/its solution on errors, and she/it its approach manner • Self-reported
86.5%
MMMU-Pro
score • Self-reported
46.2%
MMMUval
score • Self-reported
64.5%
MMVetGPT4Turbo
score • Self-reported
74.0%
MTVQA
score • Self-reported
30.9%
MVBench
score • Self-reported
73.6%
OCRBench
Evaluation AI: ChatGPT 4o • Self-reported
87.7%
RealWorldQA
score • Self-reported
77.8%
TextVQA
score • Self-reported
85.5%
VCR_en_easy
Evaluation AI: ChatGPT (GPT-4) • Self-reported
91.9%
MMBench
score • Self-reported
86.5%

License & Metadata

License
tongyi_qianwen
Announcement Date
August 29, 2024
Last Updated
July 19, 2025

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