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GLM-5.3-Flash vs Magistral Medium

GLM-5.3-Flash significantly outperforms across most benchmarks.

Zhipu AI · Mistral AI · Updated for 2026

Which is better?

GLM-5.3-Flash outperforms in 1 benchmarks (Humanity's Last Exam), while Magistral Medium is better at 0 benchmarks. GLM-5.3-Flash significantly outperforms across most 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 1 of 1 shared benchmarks
  • you want the most recent training data — it shipped Aug 2026

Choose Magistral Medium

  • you are already invested in the Mistral AI ecosystem

At a glance

The differences that matter most.

Benchmark wins
1 of 1
0 of 1
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

1 benchmarks

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

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

296.0B diff

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

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

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 Medium
Input- tokens
Output- tokens
Wed Aug 26 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Both GLM-5.3-Flash and Magistral Medium 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

Magistral Medium

Text
Images
Audio
Video

License

Usage and distribution terms

GLM-5.3-Flash is licensed under MIT, while Magistral Medium 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 Medium

Apache 2.0

Open weights

Release Timeline

When each model was launched

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

GLM-5.3-Flash is 15 months newer than Magistral Medium.

GLM-5.3-Flash

Aug 26, 2026

0 days ago

1.2yr newer
Magistral Medium

Jun 10, 2025

1.2 years ago

Knowledge Cutoff

When training data ends

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

We can confirm Magistral Medium'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 Medium

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 Medium side-by-side, then vote on the output you prefer.

GLM-5.3-Flash
✓ Preferred
Magistral Medium
Open in Playground

FAQ

Common questions about GLM-5.3-Flash vs Magistral Medium.

Which is better, GLM-5.3-Flash or Magistral Medium?

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

How does GLM-5.3-Flash compare to Magistral Medium 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 Medium scores AIME 2024: 73.6%, GPQA: 70.8%, AIME 2025: 64.9%, LiveCodeBench: 50.3%, Aider-Polyglot: 47.1%.

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

GLM-5.3-Flash supports 1.0M tokens and Magistral Medium 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 Medium?

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 Magistral Medium?

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