Model Comparison

GLM-4.5 vs Ministral 3 (14B Reasoning 2512)Which is better in 2026?

GLM-4.5 significantly outperforms across most benchmarks. Ministral 3 (14B Reasoning 2512) is 3.5x cheaper per token.

Verdict: GLM-4.5 vs Ministral 3 (14B Reasoning 2512) — which is better?

GLM-4.5 (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.

GLM-4.5 outperforms in 3 benchmarks (AIME 2024, GPQA, LiveCodeBench), while Ministral 3 (14B Reasoning 2512) is better at 0 benchmarks. GLM-4.5 significantly outperforms across most benchmarks.

On price, Ministral 3 (14B Reasoning 2512) is roughly 3.5x 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-4.5 if…

  • you want the strongest raw capability — it leads on 3 of 3 shared benchmarks

Choose Ministral 3 (14B Reasoning 2512) if…

  • cost matters — it's about 3.5x cheaper per token
  • you process long inputs — it offers a 262,100 token context window
  • you want the most recent training data — it shipped Dec 2025

Performance Benchmarks

Comparative analysis across standard metrics

3 benchmarks

GLM-4.5 outperforms in 3 benchmarks (AIME 2024, GPQA, LiveCodeBench), while Ministral 3 (14B Reasoning 2512) is better at 0 benchmarks.

GLM-4.5 significantly outperforms across most benchmarks.

Mon Jul 27 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Ministral 3 (14B Reasoning 2512) costs less

For input processing, GLM-4.5 ($0.40/1M tokens) is 2.0x more expensive than Ministral 3 (14B Reasoning 2512) ($0.20/1M tokens).

For output processing, GLM-4.5 ($1.60/1M tokens) is 8.0x more expensive than Ministral 3 (14B Reasoning 2512) ($0.20/1M tokens).

In conclusion, GLM-4.5 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
Mon Jul 27 2026 • llm-stats.com
Zhipu AI
GLM-4.5
Input tokens$0.40
Output tokens$1.60
Best providerDeepinfra
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

341.0B diff

GLM-4.5 has 341.0B more parameters than Ministral 3 (14B Reasoning 2512), making it 2435.7% larger.

Zhipu AI
GLM-4.5
355.0Bparameters
Mistral AI
Ministral 3 (14B Reasoning 2512)
14.0Bparameters
355.0B
GLM-4.5
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-4.5's 131,072 tokens. Ministral 3 (14B Reasoning 2512) can generate longer responses up to 262,100 tokens, while GLM-4.5 is limited to 131,072 tokens.

Zhipu AI
GLM-4.5
Input131,072 tokens
Output131,072 tokens
Mistral AI
Ministral 3 (14B Reasoning 2512)
Input262,100 tokens
Output262,100 tokens
Mon Jul 27 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Ministral 3 (14B Reasoning 2512) supports multimodal inputs, whereas GLM-4.5 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-4.5

Text
Images
Audio
Video

Ministral 3 (14B Reasoning 2512)

Text
Images
Audio
Video

License

Usage and distribution terms

GLM-4.5 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.

GLM-4.5

MIT

Open weights

Ministral 3 (14B Reasoning 2512)

Apache 2.0

Open weights

Release Timeline

When each model was launched

GLM-4.5 was released on 2025-07-28, while Ministral 3 (14B Reasoning 2512) was released on 2025-12-04.

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

GLM-4.5

Jul 28, 2025

12 months ago

Ministral 3 (14B Reasoning 2512)

Dec 4, 2025

7 months ago

4mo 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-4.5 is available from DeepInfra, Fireworks, Novita. Ministral 3 (14B Reasoning 2512) is available from Mistral AI.

GLM-4.5

deepinfra logo
Deepinfra
Input Price:Input: $0.40/1MOutput Price:Output: $1.60/1M
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

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

Zhipu AI
Higher AIME 2024 score (91.0% vs 89.8%)
Higher GPQA score (79.1% vs 71.2%)
Higher LiveCodeBench score (72.9% vs 64.6%)
Mistral AI
Larger context window (262,100 tokens)
Supports multimodal inputs
Less expensive input tokens
Less expensive output tokens

Detailed Comparison

Interactive Arena

Judge for yourself.

Run your own prompts against GLM-4.5 and Ministral 3 (14B Reasoning 2512) side-by-side, then vote on the output you prefer.

GLM-4.5
✓ Preferred
Ministral 3 (14B Reasoning 2512)
Open in Playground
AI Model Comparison Table
Feature
Zhipu AI
GLM-4.5
Mistral AI
Ministral 3 (14B Reasoning 2512)

FAQ

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

Which is better, GLM-4.5 or Ministral 3 (14B Reasoning 2512)?

GLM-4.5 significantly outperforms across most benchmarks. GLM-4.5 is made by Zhipu AI and Ministral 3 (14B Reasoning 2512) is made by Mistral AI. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does GLM-4.5 compare to Ministral 3 (14B Reasoning 2512) in benchmarks?

GLM-4.5 scores MATH-500: 98.2%, AIME 2024: 91.0%, MMLU-Pro: 84.6%, TAU-bench Retail: 79.7%, GPQA: 79.1%. Ministral 3 (14B Reasoning 2512) scores AIME 2024: 89.8%, AIME 2025: 85.0%, GPQA: 71.2%, LiveCodeBench: 64.6%.

Is GLM-4.5 cheaper than Ministral 3 (14B Reasoning 2512)?

Ministral 3 (14B Reasoning 2512) is 2.0x cheaper for input tokens. GLM-4.5 costs $0.40/M input and $1.60/M output via deepinfra. Ministral 3 (14B Reasoning 2512) costs $0.20/M input and $0.20/M output via mistral.

What are the context window sizes for GLM-4.5 and Ministral 3 (14B Reasoning 2512)?

GLM-4.5 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.

What are the main differences between GLM-4.5 and Ministral 3 (14B Reasoning 2512)?

Key differences include context window (131K vs 262K), input pricing ($0.40 vs $0.20/M), multimodal support (no vs yes), licensing (MIT vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

Who makes GLM-4.5 and Ministral 3 (14B Reasoning 2512)?

GLM-4.5 is developed by Zhipu AI and Ministral 3 (14B Reasoning 2512) is developed by Mistral AI.

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