The AI arena is free today

Open Superagent

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

GLM-4.5 and Ministral 3 (14B Reasoning 2512) are closely matched at 27.7 and 20.6 on the LLM Stats Score. Ministral 3 (14B Reasoning 2512) is 3.5x cheaper per token.

Zhipu AI · Mistral AI · Updated for 2026

Which is better?

GLM-4.5 and Ministral 3 (14B Reasoning 2512) are closely matched on the overall LLM Stats Score at 27.7 and 20.6.

In the 3 individual benchmarks reported for both models, GLM-4.5 wins 3; this is a narrower head-to-head signal than the composite indexes.

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.

Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.

Choose GLM-4.5

  • you value its reported benchmark strengths — it wins 3 of 3 exact shared results

Choose Ministral 3 (14B Reasoning 2512)

  • 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

At a glance

The differences that matter most.

GLM-4.5
Zhipu AI
Ministral 3 (14B Reasoning 2512)
Mistral AI
Core performance indexes
LLM Stats Score
27.7
#139
20.6
#192
Reasoning
27.1
#139
20.6
#183
Coding
16.1
#126
11.1
#159
Cost, coverage & limits
Benchmark wins
3 of 3
0 of 3
Input price
$0.40 / M
$0.20 / M
Output price
$1.60 / M
$0.20 / M
Context window
131,072
262,100

Capability indexes

Additional strengths measured across groups of related public benchmarks

1 shared
Index
GLM-4.5
Ministral 3 (14B Reasoning 2512)
Math
27.0#100
21.1#151
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

14 reported for GLM-4.5 · 4 for Ministral 3 (14B Reasoning 2512)

3 shared

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.

Thu Sep 10 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

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
Thu Sep 10 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
Thu Sep 10 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

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

1.1 years ago

Ministral 3 (14B Reasoning 2512)

Dec 4, 2025

9 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→

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

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 and Ministral 3 (14B Reasoning 2512) are closely matched on the LLM Stats Score at 27.7 and 20.6. 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 capability indexes, individual benchmarks, pricing, and limits 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 LLM Stats Score (27.7 vs 20.6), 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.

Related comparisons