Model Comparison

GLM-4.5V vs Ministral 3 (14B Reasoning 2512)

Comparing GLM-4.5V and Ministral 3 (14B Reasoning 2512) across benchmarks, pricing, and capabilities.

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Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

GLM-4.5V 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

Ministral 3 (14B Reasoning 2512) costs less

For input processing, GLM-4.5V ($0.55/1M tokens) is 2.8x more expensive than Ministral 3 (14B Reasoning 2512) ($0.20/1M tokens).

For output processing, GLM-4.5V ($2.19/1M tokens) is 10.9x more expensive than Ministral 3 (14B Reasoning 2512) ($0.20/1M tokens).

In conclusion, GLM-4.5V is more expensive than Ministral 3 (14B Reasoning 2512).*

* Using a 3:1 ratio of input to output tokens

Lowest available price from all providers
Tue Mar 31 2026 • llm-stats.com
Zhipu AI
GLM-4.5V
Input tokens$0.55
Output tokens$2.19
Best providerFireworks
Mistral AI
Ministral 3 (14B Reasoning 2512)
Input tokens$0.20
Output tokens$0.20
Best providerMistral
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

94.0B diff

GLM-4.5V has 94.0B more parameters than Ministral 3 (14B Reasoning 2512), making it 671.4% larger.

Zhipu AI
GLM-4.5V
108.0Bparameters
Mistral AI
Ministral 3 (14B Reasoning 2512)
14.0Bparameters
108.0B
GLM-4.5V
14.0B
Ministral 3 (14B Reasoning 2512)

Context Window

Maximum input and output token capacity

Ministral 3 (14B Reasoning 2512) accepts 262,100 input tokens compared to GLM-4.5V's 131,072 tokens. Ministral 3 (14B Reasoning 2512) can generate longer responses up to 262,100 tokens, while GLM-4.5V is limited to 131,072 tokens.

Zhipu AI
GLM-4.5V
Input131,072 tokens
Output131,072 tokens
Mistral AI
Ministral 3 (14B Reasoning 2512)
Input262,100 tokens
Output262,100 tokens
Tue Mar 31 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Both GLM-4.5V and Ministral 3 (14B Reasoning 2512) support multimodal inputs.

They are both capable of processing various types of data, offering versatility in application.

GLM-4.5V

Text
Images
Audio
Video

Ministral 3 (14B Reasoning 2512)

Text
Images
Audio
Video

License

Usage and distribution terms

GLM-4.5V 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.

GLM-4.5V

MIT

Open weights

Ministral 3 (14B Reasoning 2512)

Apache 2.0

Open weights

Release Timeline

When each model was launched

GLM-4.5V was released on 2025-08-11, while Ministral 3 (14B Reasoning 2512) was released on 2025-12-04.

Ministral 3 (14B Reasoning 2512) is 4 months newer than GLM-4.5V.

GLM-4.5V

Aug 11, 2025

7 months ago

Ministral 3 (14B Reasoning 2512)

Dec 4, 2025

3 months ago

3mo newer

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

Provider Availability

GLM-4.5V is available from Fireworks, Novita. Ministral 3 (14B Reasoning 2512) is available from Mistral AI.

GLM-4.5V

fireworks logo
Fireworks
Input Price:Input: $0.55/1MOutput Price:Output: $2.19/1M
novita logo
Novita
Input Price:Input: $0.60/1MOutput Price:Output: $2.20/1M

Ministral 3 (14B Reasoning 2512)

mistral logo
Mistral
Input Price:Input: $0.20/1MOutput Price:Output: $0.20/1M
* Prices shown are per million tokens

Outputs Comparison

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Key Takeaways

Zhipu AI
Mistral AI
Larger context window (262,100 tokens)
Less expensive input tokens
Less expensive output tokens

Detailed Comparison

AI Model Comparison Table
Feature
Zhipu AI
GLM-4.5V
Mistral AI
Ministral 3 (14B Reasoning 2512)

FAQ

Common questions about GLM-4.5V vs Ministral 3 (14B Reasoning 2512)

GLM-4.5V (Zhipu AI) and Ministral 3 (14B Reasoning 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.
Ministral 3 (14B Reasoning 2512) scores AIME 2024: 89.8%, AIME 2025: 85.0%, GPQA: 71.2%, LiveCodeBench: 64.6%.
Ministral 3 (14B Reasoning 2512) is 2.8x cheaper for input tokens. GLM-4.5V costs $0.55/M input and $2.19/M output via fireworks. Ministral 3 (14B Reasoning 2512) costs $0.20/M input and $0.20/M output via mistral.
GLM-4.5V supports 131K tokens and Ministral 3 (14B Reasoning 2512) supports 262K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include context window (131K vs 262K), input pricing ($0.55 vs $0.20/M), licensing (MIT vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.
GLM-4.5V is developed by Zhipu AI and Ministral 3 (14B Reasoning 2512) is developed by Mistral AI.