Model Comparison
GLM-5 vs Ministral 3 (14B Reasoning 2512)Which is better in 2026?
Comparing GLM-5 and Ministral 3 (14B Reasoning 2512) across benchmarks, pricing, and capabilities.
Verdict: GLM-5 vs Ministral 3 (14B Reasoning 2512) — which is better?
GLM-5 (by Zhipu AI) and Ministral 3 (14B Reasoning 2512) (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.
On price, Ministral 3 (14B Reasoning 2512) is roughly 7.8x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Ministral 3 (14B Reasoning 2512) also accepts a larger context window (262,100 input tokens), making it the stronger choice for long documents and large codebases.
Choose GLM-5 if…
- you want the most recent training data — it shipped Feb 2026
Choose Ministral 3 (14B Reasoning 2512) if…
- cost matters — it's about 7.8x cheaper per token
- you process long inputs — it offers a 262,100 token context window
Performance Benchmarks
Comparative analysis across standard metrics
GLM-5 and Ministral 3 (14B Reasoning 2512)don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, GLM-5 ($1.00/1M tokens) is 5.0x more expensive than Ministral 3 (14B Reasoning 2512) ($0.20/1M tokens).
For output processing, GLM-5 ($3.20/1M tokens) is 16.0x more expensive than Ministral 3 (14B Reasoning 2512) ($0.20/1M tokens).
In conclusion, GLM-5 is more expensive than Ministral 3 (14B Reasoning 2512).*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
GLM-5 has 730.0B more parameters than Ministral 3 (14B Reasoning 2512), making it 5214.3% larger.
Context Window
Maximum input and output token capacity
Ministral 3 (14B Reasoning 2512) accepts 262,100 input tokens compared to GLM-5's 200,000 tokens. Ministral 3 (14B Reasoning 2512) can generate longer responses up to 262,100 tokens, while GLM-5 is limited to 128,000 tokens.
Input Capabilities
Supported data types and modalities
Ministral 3 (14B Reasoning 2512) supports multimodal inputs, whereas GLM-5 does not.
Ministral 3 (14B Reasoning 2512) can handle both text and other forms of data like images, making it suitable for multimodal applications.
GLM-5
Ministral 3 (14B Reasoning 2512)
License
Usage and distribution terms
GLM-5 is licensed under MIT, while Ministral 3 (14B Reasoning 2512) uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
GLM-5 was released on 2026-02-11, while Ministral 3 (14B Reasoning 2512) was released on 2025-12-04.
GLM-5 is 2 months newer than Ministral 3 (14B Reasoning 2512).
Feb 11, 2026
3 months ago
2mo newerDec 4, 2025
6 months ago
Knowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
GLM-5 is available from FriendliAI, ZAI. Ministral 3 (14B Reasoning 2512) is available from Mistral AI.
GLM-5
Ministral 3 (14B Reasoning 2512)
Outputs Comparison
Key Takeaways
GLM-5
View detailsZhipu AI
No standout differentiators in the data we have for this pair.
Detailed Comparison
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FAQ
Common questions about GLM-5 vs Ministral 3 (14B Reasoning 2512).