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GLM-5.3-Flash vs Qwen3.8-27B

GLM-5.3-Flash has a slight edge in benchmark performance.

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

Which is better?

GLM-5.3-Flash outperforms in 4 benchmarks (DeepSWE 1.1, Humanity's Last Exam, NL2Repo, Terminal-Bench 2.1), while Qwen3.8-27B is better at 3 benchmarks (Agents' Last Exam, BabyVision, CharXiv-R). GLM-5.3-Flash has a slight edge in benchmark performance.

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

Choose GLM-5.3-Flash

  • you want the strongest raw capability — it leads on 4 of 7 shared benchmarks
  • you want the most recent training data — it shipped Aug 2026

Choose Qwen3.8-27B

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

At a glance

The differences that matter most.

GLM-5.3-Flash
Zhipu AI
Qwen3.8-27B
Alibaba Cloud / Qwen Team
Benchmark wins
4 of 7
3 of 7
Input price
$0.15 / M
— / M
Output price
$0.50 / M
— / M
Context window
1,000,000
—
Released
Aug 2026
Aug 2026
License
MIT
Apache 2.0

Performance Benchmarks

Comparative analysis across standard metrics

7 benchmarks

GLM-5.3-Flash outperforms in 4 benchmarks (DeepSWE 1.1, Humanity's Last Exam, NL2Repo, Terminal-Bench 2.1), while Qwen3.8-27B is better at 3 benchmarks (Agents' Last Exam, BabyVision, CharXiv-R).

GLM-5.3-Flash has a slight edge in benchmark performance.

Wed Aug 26 2026 • llm-stats.com

Arena Performance

Playground indexes and blind preference scores

Model Size

Parameter count comparison

292.2B diff

GLM-5.3-Flash has 292.2B more parameters than Qwen3.8-27B, making it 1051.8% larger.

Zhipu AI
GLM-5.3-Flash
320.0Bparameters
Alibaba Cloud / Qwen Team
Qwen3.8-27B
27.8Bparameters
320.0B
GLM-5.3-Flash
27.8B
Qwen3.8-27B

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
Qwen3.8-27B
Input- tokens
Output- tokens
Wed Aug 26 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Both GLM-5.3-Flash and Qwen3.8-27B 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

Qwen3.8-27B

Text
Images
Audio
Video

License

Usage and distribution terms

GLM-5.3-Flash is licensed under MIT, while Qwen3.8-27B 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

Qwen3.8-27B

Apache 2.0

Open weights

Release Timeline

When each model was launched

GLM-5.3-Flash was released on 2026-08-26, while Qwen3.8-27B was released on 2026-08-14.

GLM-5.3-Flash is 0 month newer than Qwen3.8-27B.

GLM-5.3-Flash

Aug 26, 2026

0 days ago

1w newer
Qwen3.8-27B

Aug 14, 2026

1 weeks 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 Qwen3.8-27B side-by-side, then vote on the output you prefer.

GLM-5.3-Flash
✓ Preferred
Qwen3.8-27B
Open in Playground

FAQ

Common questions about GLM-5.3-Flash vs Qwen3.8-27B.

Which is better, GLM-5.3-Flash or Qwen3.8-27B?

GLM-5.3-Flash has a slight edge in benchmark performance. GLM-5.3-Flash is made by Zhipu AI and Qwen3.8-27B 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 Qwen3.8-27B 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%. Qwen3.8-27B scores MathVision: 94.6%, OmniDocBench 1.5: 91.1%, LiveCodeBench v6: 90.3%, CharXiv-R: 90.2%, GPQA: 89.2%.

What are the context window sizes for GLM-5.3-Flash and Qwen3.8-27B?

GLM-5.3-Flash supports 1.0M tokens and Qwen3.8-27B 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 Qwen3.8-27B?

Key differences include licensing (MIT vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

Who makes GLM-5.3-Flash and Qwen3.8-27B?

GLM-5.3-Flash is developed by Zhipu AI and Qwen3.8-27B is developed by Alibaba Cloud / Qwen Team.

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