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GLM-5.3-Flash vs Qwen2-VL-72B-Instruct

GLM-5.3-Flash significantly outperforms across most benchmarks.

Zhipu AI · Alibaba Cloud / Qwen Team · Updated for 2026

Which is better?

GLM-5.3-Flash outperforms in 1 benchmarks (MVBench), while Qwen2-VL-72B-Instruct is better at 0 benchmarks. GLM-5.3-Flash significantly outperforms across most benchmarks.

Based on current benchmark, pricing, and model metadata for 2026.

Choose GLM-5.3-Flash

  • you want the strongest raw capability — it leads on 1 of 1 shared benchmarks
  • you want the most recent training data — it shipped Aug 2026

Choose Qwen2-VL-72B-Instruct

  • you are already invested in the Alibaba Cloud / Qwen Team ecosystem

At a glance

The differences that matter most.

Benchmark wins
1 of 1
0 of 1
Input price
$0.15 / M
— / M
Output price
$0.50 / M
— / M
Context window
1,048,576
Released
Aug 2026
Aug 2024
License
MIT
tongyi-qianwen

Performance Benchmarks

Comparative analysis across standard metrics

1 benchmarks

GLM-5.3-Flash outperforms in 1 benchmarks (MVBench), while Qwen2-VL-72B-Instruct is better at 0 benchmarks.

GLM-5.3-Flash significantly outperforms across most benchmarks.

Thu Aug 27 2026 • llm-stats.com

Arena Performance

Playground indexes and blind preference scores

Model Size

Parameter count comparison

246.6B diff

GLM-5.3-Flash has 246.6B more parameters than Qwen2-VL-72B-Instruct, making it 336.0% larger.

Zhipu AI
GLM-5.3-Flash
320.0Bparameters
Alibaba Cloud / Qwen Team
Qwen2-VL-72B-Instruct
73.4Bparameters
320.0B
GLM-5.3-Flash
73.4B
Qwen2-VL-72B-Instruct

Context Window

Maximum input and output token capacity

Only GLM-5.3-Flash specifies input context (1,048,576 tokens). Only GLM-5.3-Flash specifies output context (131,072 tokens).

Zhipu AI
GLM-5.3-Flash
Input1,048,576 tokens
Output131,072 tokens
Alibaba Cloud / Qwen Team
Qwen2-VL-72B-Instruct
Input- tokens
Output- tokens
Thu Aug 27 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Both GLM-5.3-Flash and Qwen2-VL-72B-Instruct support multimodal inputs.

They are both capable of processing various types of data, offering versatility in application.

GLM-5.3-Flash

Text
Images
Audio
Video

Qwen2-VL-72B-Instruct

Text
Images
Audio
Video

License

Usage and distribution terms

GLM-5.3-Flash is licensed under MIT, while Qwen2-VL-72B-Instruct uses tongyi-qianwen.

License differences may affect how you can use these models in commercial or open-source projects.

GLM-5.3-Flash

MIT

Open weights

Qwen2-VL-72B-Instruct

tongyi-qianwen

Open weights

Release Timeline

When each model was launched

GLM-5.3-Flash was released on 2026-08-26, while Qwen2-VL-72B-Instruct was released on 2024-08-29.

GLM-5.3-Flash is 24 months newer than Qwen2-VL-72B-Instruct.

GLM-5.3-Flash

Aug 26, 2026

1 days ago

2.0yr newer
Qwen2-VL-72B-Instruct

Aug 29, 2024

2.0 years ago

Knowledge Cutoff

When training data ends

Qwen2-VL-72B-Instruct has a documented knowledge cutoff of 2023-06-30, while GLM-5.3-Flash's cutoff date is not specified.

We can confirm Qwen2-VL-72B-Instruct's training data extends to 2023-06-30, but cannot make a direct comparison without GLM-5.3-Flash's cutoff date.

GLM-5.3-Flash

Qwen2-VL-72B-Instruct

Jun 2023

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion

Judge for yourself.

Run your own prompts against GLM-5.3-Flash and Qwen2-VL-72B-Instruct side-by-side, then vote on the output you prefer.

GLM-5.3-Flash
✓ Preferred
Qwen2-VL-72B-Instruct
Open in Playground

FAQ

Common questions about GLM-5.3-Flash vs Qwen2-VL-72B-Instruct.

Which is better, GLM-5.3-Flash or Qwen2-VL-72B-Instruct?

GLM-5.3-Flash significantly outperforms across most benchmarks. GLM-5.3-Flash is made by Zhipu AI and Qwen2-VL-72B-Instruct is made by Alibaba Cloud / Qwen Team. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does GLM-5.3-Flash compare to Qwen2-VL-72B-Instruct in benchmarks?

GLM-5.3-Flash scores CharXiv-R: 89.4%, Terminal-Bench 2.1: 84.3%, MMVU: 80.5%, Toolathlon: 78.4%, Chartography: 78.0%. Qwen2-VL-72B-Instruct scores DocVQAtest: 96.5%, VCR_en_easy: 91.9%, ChartQA: 88.3%, OCRBench: 87.7%, MMBench: 86.5%.

What are the context window sizes for GLM-5.3-Flash and Qwen2-VL-72B-Instruct?

GLM-5.3-Flash supports 1.0M tokens and Qwen2-VL-72B-Instruct supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between GLM-5.3-Flash and Qwen2-VL-72B-Instruct?

Key differences include licensing (MIT vs tongyi-qianwen). See the full comparison above for benchmark-by-benchmark results.

Who makes GLM-5.3-Flash and Qwen2-VL-72B-Instruct?

GLM-5.3-Flash is developed by Zhipu AI and Qwen2-VL-72B-Instruct is developed by Alibaba Cloud / Qwen Team.