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GLM-5.3-Flash vs MAI-Thinking-1

GLM-5.3-Flash leads the LLM Stats Score 51.6 to 33.2.

Zhipu AI · Microsoft · Updated for 2026

Which is better?

GLM-5.3-Flash leads the overall LLM Stats Score 51.6 to 33.2, ranking #11 overall.

Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.

Choose GLM-5.3-Flash

  • overall performance matters — it scores 51.6 and ranks #11 on LLM Stats
  • your work emphasizes reasoning and coding — it leads those capability indexes
  • you want the most recent training data — it shipped Aug 2026
  • you need open weights you can self-host or fine-tune

Choose MAI-Thinking-1

  • you are already invested in the Microsoft ecosystem

At a glance

The differences that matter most.

GLM-5.3-Flash
Zhipu AI
MAI-Thinking-1
Microsoft
Core performance indexes
LLM Stats Score
51.6
#11
33.2
#88
Reasoning
50.3
#13
34.0
#82
Coding
37.8
#22
20.4
#89
Agents
39.1
#9
14.0
#82
Cost, coverage & limits
Benchmark wins
—
—
Input price
$0.15 / M
— / M
Output price
$0.50 / M
— / M
Context window
1,048,576
—

Capability indexes

Additional strengths measured across groups of related public benchmarks

1 shared
Index
GLM-5.3-Flash
MAI-Thinking-1
Tool use
34.2#4
10.2#116
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

15 reported for GLM-5.3-Flash · 23 for MAI-Thinking-1

No common benchmarks found

GLM-5.3-Flash and MAI-Thinking-1don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Human preference

Blind head-to-head votes and playground preference scores

Model Size

Parameter count comparison

680.0B diff

MAI-Thinking-1 has 680.0B more parameters than GLM-5.3-Flash, making it 212.5% larger.

Zhipu AI
GLM-5.3-Flash
320.0Bparameters
Microsoft
MAI-Thinking-1
1.0Tparameters
320.0B
GLM-5.3-Flash
1000.0B
MAI-Thinking-1

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
Microsoft
MAI-Thinking-1
Input- tokens
Output- tokens
Sun Aug 30 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

GLM-5.3-Flash supports multimodal inputs, whereas MAI-Thinking-1 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

MAI-Thinking-1

Text
Images
Audio
Video

License

Usage and distribution terms

GLM-5.3-Flash is licensed under MIT, while MAI-Thinking-1 uses a proprietary license.

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

GLM-5.3-Flash

MIT

Open weights

MAI-Thinking-1

Proprietary

Closed source

Release Timeline

When each model was launched

GLM-5.3-Flash was released on 2026-08-26, while MAI-Thinking-1 was released on 2026-06-02.

GLM-5.3-Flash is 3 months newer than MAI-Thinking-1.

GLM-5.3-Flash

Aug 26, 2026

4 days ago

2mo newer
MAI-Thinking-1

Jun 2, 2026

2 months 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 MAI-Thinking-1 side-by-side, then vote on the output you prefer.

GLM-5.3-Flash
✓ Preferred
MAI-Thinking-1
Open in Playground

FAQ

Common questions about GLM-5.3-Flash vs MAI-Thinking-1.

Which is better, GLM-5.3-Flash or MAI-Thinking-1?

GLM-5.3-Flash leads the LLM Stats Score 51.6 to 33.2. GLM-5.3-Flash is made by Zhipu AI and MAI-Thinking-1 is made by Microsoft. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

How does GLM-5.3-Flash compare to MAI-Thinking-1 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%. MAI-Thinking-1 scores LongFact: 98.0%, AIME 2025: 97.0%, AIME 2026: 94.5%, GraphWalks: 90.0%, AIR-Bench: 88.0%.

What are the context window sizes for GLM-5.3-Flash and MAI-Thinking-1?

GLM-5.3-Flash supports 1.0M tokens and MAI-Thinking-1 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 MAI-Thinking-1?

Key differences include LLM Stats Score (51.6 vs 33.2), multimodal support (yes vs no), licensing (MIT vs Proprietary). See the full comparison above for benchmark-by-benchmark results.

Who makes GLM-5.3-Flash and MAI-Thinking-1?

GLM-5.3-Flash is developed by Zhipu AI and MAI-Thinking-1 is developed by Microsoft.

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