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GLM-5.3-Flash vs QwQ-32B

Comparing GLM-5.3-Flash and QwQ-32B across benchmarks, pricing, and capabilities.

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

Which is better?

GLM-5.3-Flash and QwQ-32B trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.

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

Choose GLM-5.3-Flash

  • you want the most recent training data — it shipped Aug 2026

Choose QwQ-32B

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

At a glance

The differences that matter most.

Benchmark wins
Input price
$0.15 / M
— / M
Output price
$0.50 / M
— / M
Context window
1,048,576
Released
Aug 2026
Mar 2025
License
MIT
Apache 2.0

Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

GLM-5.3-Flash and QwQ-32Bdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Arena Performance

Playground indexes and blind preference scores

Model Size

Parameter count comparison

287.5B diff

GLM-5.3-Flash has 287.5B more parameters than QwQ-32B, making it 884.6% larger.

Zhipu AI
GLM-5.3-Flash
320.0Bparameters
Alibaba Cloud / Qwen Team
QwQ-32B
32.5Bparameters
320.0B
GLM-5.3-Flash
32.5B
QwQ-32B

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
QwQ-32B
Input- tokens
Output- tokens
Wed Aug 26 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

GLM-5.3-Flash supports multimodal inputs, whereas QwQ-32B does not.

GLM-5.3-Flash can handle both text and other forms of data like images, making it suitable for multimodal applications.

GLM-5.3-Flash

Text
Images
Audio
Video

QwQ-32B

Text
Images
Audio
Video

License

Usage and distribution terms

GLM-5.3-Flash is licensed under MIT, while QwQ-32B uses Apache 2.0.

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

GLM-5.3-Flash

MIT

Open weights

QwQ-32B

Apache 2.0

Open weights

Release Timeline

When each model was launched

GLM-5.3-Flash was released on 2026-08-26, while QwQ-32B was released on 2025-03-05.

GLM-5.3-Flash is 18 months newer than QwQ-32B.

GLM-5.3-Flash

Aug 26, 2026

0 days ago

1.5yr newer
QwQ-32B

Mar 5, 2025

1.5 years ago

Knowledge Cutoff

When training data ends

QwQ-32B has a documented knowledge cutoff of 2024-11-28, while GLM-5.3-Flash's cutoff date is not specified.

We can confirm QwQ-32B's training data extends to 2024-11-28, but cannot make a direct comparison without GLM-5.3-Flash's cutoff date.

GLM-5.3-Flash

QwQ-32B

Nov 2024

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion

Judge for yourself.

Run your own prompts against GLM-5.3-Flash and QwQ-32B side-by-side, then vote on the output you prefer.

GLM-5.3-Flash
✓ Preferred
QwQ-32B
Open in Playground

FAQ

Common questions about GLM-5.3-Flash vs QwQ-32B.

Which is better, GLM-5.3-Flash or QwQ-32B?

GLM-5.3-Flash (Zhipu AI) and QwQ-32B (Alibaba Cloud / Qwen Team) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.

How does GLM-5.3-Flash compare to QwQ-32B 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%. QwQ-32B scores MATH-500: 90.6%, IFEval: 83.9%, AIME 2024: 79.5%, LiveBench: 73.1%, BFCL: 66.4%.

What are the context window sizes for GLM-5.3-Flash and QwQ-32B?

GLM-5.3-Flash supports 1.0M tokens and QwQ-32B 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 QwQ-32B?

Key differences include multimodal support (yes vs no), licensing (MIT vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

Who makes GLM-5.3-Flash and QwQ-32B?

GLM-5.3-Flash is developed by Zhipu AI and QwQ-32B is developed by Alibaba Cloud / Qwen Team.