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GLM-5.3-Flash vs Qwen2.5 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 Qwen2.5 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 Qwen2.5 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
1,000,000
Released
Aug 2026
Jan 2025
License
MIT
tongyi-qianwen

Performance Benchmarks

Comparative analysis across standard metrics

1 benchmarks

GLM-5.3-Flash outperforms in 1 benchmarks (MVBench), while Qwen2.5 VL 72B Instruct is better at 0 benchmarks.

GLM-5.3-Flash significantly outperforms across most benchmarks.

Wed Aug 26 2026 • llm-stats.com

Arena Performance

Playground indexes and blind preference scores

Model Size

Parameter count comparison

248.0B diff

GLM-5.3-Flash has 248.0B more parameters than Qwen2.5 VL 72B Instruct, making it 344.4% larger.

Zhipu AI
GLM-5.3-Flash
320.0Bparameters
Alibaba Cloud / Qwen Team
Qwen2.5 VL 72B Instruct
72.0Bparameters
320.0B
GLM-5.3-Flash
72.0B
Qwen2.5 VL 72B Instruct

Context Window

Maximum input and output token capacity

Only GLM-5.3-Flash specifies input context (1,000,000 tokens). Only GLM-5.3-Flash specifies output context (131,072 tokens).

Zhipu AI
GLM-5.3-Flash
Input1,000,000 tokens
Output131,072 tokens
Alibaba Cloud / Qwen Team
Qwen2.5 VL 72B Instruct
Input- tokens
Output- tokens
Wed Aug 26 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Both GLM-5.3-Flash and Qwen2.5 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

Qwen2.5 VL 72B Instruct

Text
Images
Audio
Video

License

Usage and distribution terms

GLM-5.3-Flash is licensed under MIT, while Qwen2.5 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

Qwen2.5 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 Qwen2.5 VL 72B Instruct was released on 2025-01-26.

GLM-5.3-Flash is 19 months newer than Qwen2.5 VL 72B Instruct.

GLM-5.3-Flash

Aug 26, 2026

0 days ago

1.6yr newer
Qwen2.5 VL 72B Instruct

Jan 26, 2025

1.6 years ago

Knowledge Cutoff

When training data ends

Neither model specifies a knowledge cutoff date.

Unable to compare the recency of their training data.

No cutoff dates available

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion

Judge for yourself.

Run your own prompts against GLM-5.3-Flash and Qwen2.5 VL 72B Instruct side-by-side, then vote on the output you prefer.

GLM-5.3-Flash
✓ Preferred
Qwen2.5 VL 72B Instruct
Open in Playground

FAQ

Common questions about GLM-5.3-Flash vs Qwen2.5 VL 72B Instruct.

Which is better, GLM-5.3-Flash or Qwen2.5 VL 72B Instruct?

GLM-5.3-Flash significantly outperforms across most benchmarks. GLM-5.3-Flash is made by Zhipu AI and Qwen2.5 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 Qwen2.5 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%, MVBench: 77.8%. Qwen2.5 VL 72B Instruct scores DocVQA: 96.4%, Android Control Low_EM: 93.7%, ChartQA: 89.5%, OCRBench: 88.5%, AI2D: 88.4%.

What are the context window sizes for GLM-5.3-Flash and Qwen2.5 VL 72B Instruct?

GLM-5.3-Flash supports 1.0M tokens and Qwen2.5 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 Qwen2.5 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 Qwen2.5 VL 72B Instruct?

GLM-5.3-Flash is developed by Zhipu AI and Qwen2.5 VL 72B Instruct is developed by Alibaba Cloud / Qwen Team.