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GLM-5.3-Flash vs Magistral Small 2506

Comparing GLM-5.3-Flash and Magistral Small 2506 across benchmarks, pricing, and capabilities.

Zhipu AI · Mistral AI · Updated for 2026

Which is better?

GLM-5.3-Flash and Magistral Small 2506 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 Magistral Small 2506

  • you are already invested in the Mistral AI ecosystem

At a glance

The differences that matter most.

GLM-5.3-Flash
Zhipu AI
Magistral Small 2506
Mistral AI
Benchmark wins
—
—
Input price
$0.15 / M
— / M
Output price
$0.50 / M
— / M
Context window
1,048,576
—
Released
Aug 2026
Jun 2025
License
MIT
Apache 2.0

Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

GLM-5.3-Flash and Magistral Small 2506don'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

296.0B diff

GLM-5.3-Flash has 296.0B more parameters than Magistral Small 2506, making it 1233.3% larger.

Zhipu AI
GLM-5.3-Flash
320.0Bparameters
Mistral AI
Magistral Small 2506
24.0Bparameters
320.0B
GLM-5.3-Flash
24.0B
Magistral Small 2506

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
Mistral AI
Magistral Small 2506
Input- tokens
Output- tokens
Thu Aug 27 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

GLM-5.3-Flash supports multimodal inputs, whereas Magistral Small 2506 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

Magistral Small 2506

Text
Images
Audio
Video

License

Usage and distribution terms

GLM-5.3-Flash is licensed under MIT, while Magistral Small 2506 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

Magistral Small 2506

Apache 2.0

Open weights

Release Timeline

When each model was launched

GLM-5.3-Flash was released on 2026-08-26, while Magistral Small 2506 was released on 2025-06-10.

GLM-5.3-Flash is 15 months newer than Magistral Small 2506.

GLM-5.3-Flash

Aug 26, 2026

0 days ago

1.2yr newer
Magistral Small 2506

Jun 10, 2025

1.2 years ago

Knowledge Cutoff

When training data ends

Magistral Small 2506 has a documented knowledge cutoff of 2025-06-01, while GLM-5.3-Flash's cutoff date is not specified.

We can confirm Magistral Small 2506's training data extends to 2025-06-01, but cannot make a direct comparison without GLM-5.3-Flash's cutoff date.

GLM-5.3-Flash

—

Magistral Small 2506

Jun 2025

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Judge for yourself.

Run your own prompts against GLM-5.3-Flash and Magistral Small 2506 side-by-side, then vote on the output you prefer.

GLM-5.3-Flash
✓ Preferred
Magistral Small 2506
Open in Playground

FAQ

Common questions about GLM-5.3-Flash vs Magistral Small 2506.

Which is better, GLM-5.3-Flash or Magistral Small 2506?

GLM-5.3-Flash (Zhipu AI) and Magistral Small 2506 (Mistral AI) 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 Magistral Small 2506 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%. Magistral Small 2506 scores AIME 2024: 70.7%, GPQA: 68.2%, AIME 2025: 62.8%, LiveCodeBench: 51.3%.

What are the context window sizes for GLM-5.3-Flash and Magistral Small 2506?

GLM-5.3-Flash supports 1.0M tokens and Magistral Small 2506 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 Magistral Small 2506?

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 Magistral Small 2506?

GLM-5.3-Flash is developed by Zhipu AI and Magistral Small 2506 is developed by Mistral AI.

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