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GLM-5.3-Flash vs Qwen3.8-Flash-Next

GLM-5.3-Flash shows notably better performance in the majority of benchmarks.

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, Toolathlon), while Qwen3.8-Flash-Next is better at 2 benchmarks (Agents' Last Exam, CharXiv-R). GLM-5.3-Flash shows notably better performance in the majority of 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 4 of 6 shared benchmarks

Choose Qwen3.8-Flash-Next

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

At a glance

The differences that matter most.

Benchmark wins
4 of 6
2 of 6
Input price
$0.15 / M
— / M
Output price
$0.50 / M
— / M
Context window
1,000,000
Released
Aug 2026
Aug 2026
License
MIT
Qwen Community License 1.0

Performance Benchmarks

Comparative analysis across standard metrics

6 benchmarks

GLM-5.3-Flash outperforms in 4 benchmarks (DeepSWE 1.1, Humanity's Last Exam, NL2Repo, Toolathlon), while Qwen3.8-Flash-Next is better at 2 benchmarks (Agents' Last Exam, CharXiv-R).

GLM-5.3-Flash shows notably better performance in the majority of benchmarks.

Wed Aug 26 2026 • llm-stats.com

Arena Performance

Playground indexes and blind preference scores

Model Size

Parameter count comparison

140.0B diff

GLM-5.3-Flash has 140.0B more parameters than Qwen3.8-Flash-Next, making it 77.8% larger.

Zhipu AI
GLM-5.3-Flash
320.0Bparameters
Alibaba Cloud / Qwen Team
Qwen3.8-Flash-Next
180.0Bparameters
320.0B
GLM-5.3-Flash
180.0B
Qwen3.8-Flash-Next

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-Flash-Next
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-Flash-Next 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-Flash-Next

Text
Images
Audio
Video

License

Usage and distribution terms

GLM-5.3-Flash is licensed under MIT, while Qwen3.8-Flash-Next uses Qwen Community License 1.0.

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-Flash-Next

Qwen Community License 1.0

Open weights

Release Timeline

When each model was launched

Both models were released on 2026-08-26.

They likely represent similar generations of model development.

GLM-5.3-Flash

Aug 26, 2026

0 days ago

Qwen3.8-Flash-Next

Aug 26, 2026

0 days 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-Flash-Next side-by-side, then vote on the output you prefer.

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

FAQ

Common questions about GLM-5.3-Flash vs Qwen3.8-Flash-Next.

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

GLM-5.3-Flash shows notably better performance in the majority of benchmarks. GLM-5.3-Flash is made by Zhipu AI and Qwen3.8-Flash-Next 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-Flash-Next 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-Flash-Next scores MathVision: 95.7%, LiveCodeBench v6: 91.9%, GPQA: 91.7%, CharXiv-R: 90.6%, RealWorldQA: 88.5%.

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

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

Key differences include licensing (MIT vs Qwen Community License 1.0). See the full comparison above for benchmark-by-benchmark results.

Who makes GLM-5.3-Flash and Qwen3.8-Flash-Next?

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