Model Comparison

GLM-5 vs Magistral MediumWhich is better in 2026?

Comparing GLM-5 and Magistral Medium across benchmarks, pricing, and capabilities.

Verdict: GLM-5 vs Magistral Medium — which is better?

GLM-5 (by Zhipu AI) and Magistral Medium (by Mistral AI) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.

Choose GLM-5 if…

  • you want the most recent training data — it shipped Feb 2026

Choose Magistral Medium if…

  • you are already invested in the Mistral AI ecosystem
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Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

GLM-5 and Magistral Medium don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Arena Performance

Human preference votes

Model Size

Parameter count comparison

720.0B diff

GLM-5 has 720.0B more parameters than Magistral Medium, making it 3000.0% larger.

Zhipu AI
GLM-5
744.0Bparameters
Mistral AI
Magistral Medium
24.0Bparameters
744.0B
GLM-5
24.0B
Magistral Medium

Context Window

Maximum input and output token capacity

Only GLM-5 specifies input context (200,000 tokens). Only GLM-5 specifies output context (128,000 tokens).

Zhipu AI
GLM-5
Input200,000 tokens
Output128,000 tokens
Mistral AI
Magistral Medium
Input- tokens
Output- tokens
Sun Jun 07 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Magistral Medium supports multimodal inputs, whereas GLM-5 does not.

Magistral Medium can handle both text and other forms of data like images, making it suitable for multimodal applications.

GLM-5

Text
Images
Audio
Video

Magistral Medium

Text
Images
Audio
Video

License

Usage and distribution terms

GLM-5 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

MIT

Open weights

Magistral Medium

Apache 2.0

Open weights

Release Timeline

When each model was launched

GLM-5 was released on 2026-02-11, while Magistral Medium was released on 2025-06-10.

GLM-5 is 8 months newer than Magistral Medium.

GLM-5

Feb 11, 2026

3 months ago

8mo newer
Magistral Medium

Jun 10, 2025

12 months ago

Knowledge Cutoff

When training data ends

Magistral Medium has a documented knowledge cutoff of 2025-06-01, while GLM-5'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's cutoff date.

GLM-5

—

Magistral Medium

Jun 2025

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

Zhipu AI
Larger context window (200,000 tokens)
Mistral AI
Supports multimodal inputs

Detailed Comparison

AI Model Comparison Table
Feature
Zhipu AI
GLM-5
Mistral AI
Magistral Medium

FAQ

Common questions about GLM-5 vs Magistral Medium.

Which is better, GLM-5 or Magistral Medium?

GLM-5 (Zhipu AI) and Magistral Medium (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 compare to Magistral Medium in benchmarks?

GLM-5 scores t2-bench: 89.7%, SWE-Bench Verified: 77.8%, BrowseComp: 75.9%, MCP Atlas: 67.8%, Terminal-Bench 2.0: 56.2%. 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 and Magistral Medium?

GLM-5 supports 200K 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 and Magistral Medium?

Key differences include multimodal support (no vs yes), licensing (MIT vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

Who makes GLM-5 and Magistral Medium?

GLM-5 is developed by Zhipu AI and Magistral Medium is developed by Mistral AI.

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