Model Comparison

GLM-5 vs MiniStral 3 (14B Instruct 2512)

Comparing GLM-5 and MiniStral 3 (14B Instruct 2512) across benchmarks, pricing, and capabilities.

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Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

GLM-5 and MiniStral 3 (14B Instruct 2512) 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

730.0B diff

GLM-5 has 730.0B more parameters than MiniStral 3 (14B Instruct 2512), making it 5214.3% larger.

Zhipu AI
GLM-5
744.0Bparameters
Mistral AI
MiniStral 3 (14B Instruct 2512)
14.0Bparameters
744.0B
GLM-5
14.0B
MiniStral 3 (14B Instruct 2512)

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
MiniStral 3 (14B Instruct 2512)
Input- tokens
Output- tokens
Wed May 27 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

MiniStral 3 (14B Instruct 2512) supports multimodal inputs, whereas GLM-5 does not.

MiniStral 3 (14B Instruct 2512) can handle both text and other forms of data like images, making it suitable for multimodal applications.

GLM-5

Text
Images
Audio
Video

MiniStral 3 (14B Instruct 2512)

Text
Images
Audio
Video

License

Usage and distribution terms

GLM-5 is licensed under MIT, while MiniStral 3 (14B Instruct 2512) 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

MiniStral 3 (14B Instruct 2512)

Apache 2.0

Open weights

Release Timeline

When each model was launched

GLM-5 was released on 2026-02-11, while MiniStral 3 (14B Instruct 2512) was released on 2025-12-04.

GLM-5 is 2 months newer than MiniStral 3 (14B Instruct 2512).

GLM-5

Feb 11, 2026

3 months ago

2mo newer
MiniStral 3 (14B Instruct 2512)

Dec 4, 2025

5 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→

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
MiniStral 3 (14B Instruct 2512)

FAQ

Common questions about GLM-5 vs MiniStral 3 (14B Instruct 2512).

Which is better, GLM-5 or MiniStral 3 (14B Instruct 2512)?

GLM-5 (Zhipu AI) and MiniStral 3 (14B Instruct 2512) (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 MiniStral 3 (14B Instruct 2512) 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%. MiniStral 3 (14B Instruct 2512) scores MATH: 90.4%, Wild Bench: 68.5%, Arena Hard: 55.1%, MM-MT-Bench: 8.5%.

What are the context window sizes for GLM-5 and MiniStral 3 (14B Instruct 2512)?

GLM-5 supports 200K tokens and MiniStral 3 (14B Instruct 2512) 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 MiniStral 3 (14B Instruct 2512)?

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 MiniStral 3 (14B Instruct 2512)?

GLM-5 is developed by Zhipu AI and MiniStral 3 (14B Instruct 2512) is developed by Mistral AI.