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GLM-4.5-Air vs GLM-5.3-Flash

GLM-5.3-Flash significantly outperforms across most benchmarks.

Zhipu AI · Zhipu AI · Updated for 2026

Which is better?

GLM-4.5-Air outperforms in 0 benchmarks, while GLM-5.3-Flash is better at 1 benchmark (Humanity's Last Exam). GLM-5.3-Flash significantly outperforms across most benchmarks.

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

Choose GLM-4.5-Air

  • you are already invested in the Zhipu AI ecosystem

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

At a glance

The differences that matter most.

GLM-4.5-Air
Zhipu AI
GLM-5.3-Flash
Zhipu AI
Benchmark wins
0 of 1
1 of 1
Input price
— / M
$0.15 / M
Output price
— / M
$0.50 / M
Context window
—
1,000,000
Released
Jul 2025
Aug 2026
License
MIT
MIT

Performance Benchmarks

Comparative analysis across standard metrics

1 benchmarks

GLM-4.5-Air outperforms in 0 benchmarks, while GLM-5.3-Flash is better at 1 benchmark (Humanity's Last Exam).

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

214.0B diff

GLM-5.3-Flash has 214.0B more parameters than GLM-4.5-Air, making it 201.9% larger.

Zhipu AI
GLM-4.5-Air
106.0Bparameters
Zhipu AI
GLM-5.3-Flash
320.0Bparameters
106.0B
GLM-4.5-Air
320.0B
GLM-5.3-Flash

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-4.5-Air
Input- tokens
Output- tokens
Zhipu AI
GLM-5.3-Flash
Input1,000,000 tokens
Output131,072 tokens
Wed Aug 26 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

GLM-5.3-Flash supports multimodal inputs, whereas GLM-4.5-Air does not.

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

GLM-4.5-Air

Text
Images
Audio
Video

GLM-5.3-Flash

Text
Images
Audio
Video

License

Usage and distribution terms

Both models are licensed under MIT.

Both models share the same licensing terms, providing consistent usage rights.

GLM-4.5-Air

MIT

Open weights

GLM-5.3-Flash

MIT

Open weights

Release Timeline

When each model was launched

GLM-4.5-Air was released on 2025-07-28, while GLM-5.3-Flash was released on 2026-08-26.

GLM-5.3-Flash is 13 months newer than GLM-4.5-Air.

GLM-4.5-Air

Jul 28, 2025

1.1 years ago

GLM-5.3-Flash

Aug 26, 2026

0 days ago

1.1yr newer

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-4.5-Air and GLM-5.3-Flash side-by-side, then vote on the output you prefer.

GLM-4.5-Air
✓ Preferred
GLM-5.3-Flash
Open in Playground

FAQ

Common questions about GLM-4.5-Air vs GLM-5.3-Flash.

Which is better, GLM-4.5-Air or GLM-5.3-Flash?

GLM-5.3-Flash significantly outperforms across most benchmarks. GLM-4.5-Air is made by Zhipu AI and GLM-5.3-Flash is made by Zhipu AI. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does GLM-4.5-Air compare to GLM-5.3-Flash in benchmarks?

GLM-4.5-Air scores MATH-500: 98.1%, AIME 2024: 89.4%, MMLU-Pro: 81.4%, TAU-bench Retail: 77.9%, BFCL-v3: 76.4%. GLM-5.3-Flash scores CharXiv-R: 89.4%, Terminal-Bench 2.1: 84.3%, MMVU: 80.5%, Toolathlon: 78.4%, MVBench: 77.8%.

What are the context window sizes for GLM-4.5-Air and GLM-5.3-Flash?

GLM-4.5-Air supports an unknown number of tokens and GLM-5.3-Flash supports 1.0M tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between GLM-4.5-Air and GLM-5.3-Flash?

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

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