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GLM-5.3-Flash vs Ministral 3 (14B Base 2512)

Comparing GLM-5.3-Flash and Ministral 3 (14B Base 2512) across benchmarks, pricing, and capabilities.

Zhipu AI · Mistral AI · Updated for 2026

Which is better?

GLM-5.3-Flash and Ministral 3 (14B Base 2512) trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.

Based on current benchmark, pricing, and model metadata for 2026.

Choose GLM-5.3-Flash

  • you want the most recent training data — it shipped Aug 2026

Choose Ministral 3 (14B Base 2512)

  • you are already invested in the Mistral AI ecosystem

At a glance

The differences that matter most.

GLM-5.3-Flash
Zhipu AI
Ministral 3 (14B Base 2512)
Mistral AI
Benchmark wins
—
—
Input price
$0.15 / M
— / M
Output price
$0.50 / M
— / M
Context window
1,048,576
—
Released
Aug 2026
Dec 2025
License
MIT
Apache 2.0

Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

GLM-5.3-Flash and Ministral 3 (14B Base 2512)don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Arena Performance

Playground indexes and blind preference scores

Model Size

Parameter count comparison

306.0B diff

GLM-5.3-Flash has 306.0B more parameters than Ministral 3 (14B Base 2512), making it 2185.7% larger.

Zhipu AI
GLM-5.3-Flash
320.0Bparameters
Mistral AI
Ministral 3 (14B Base 2512)
14.0Bparameters
320.0B
GLM-5.3-Flash
14.0B
Ministral 3 (14B Base 2512)

Context Window

Maximum input and output token capacity

Only GLM-5.3-Flash specifies input context (1,048,576 tokens). Only GLM-5.3-Flash specifies output context (131,072 tokens).

Zhipu AI
GLM-5.3-Flash
Input1,048,576 tokens
Output131,072 tokens
Mistral AI
Ministral 3 (14B Base 2512)
Input- tokens
Output- tokens
Thu Aug 27 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Both GLM-5.3-Flash and Ministral 3 (14B Base 2512) support multimodal inputs.

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

GLM-5.3-Flash

Text
Images
Audio
Video

Ministral 3 (14B Base 2512)

Text
Images
Audio
Video

License

Usage and distribution terms

GLM-5.3-Flash is licensed under MIT, while Ministral 3 (14B Base 2512) uses Apache 2.0.

License differences may affect how you can use these models in commercial or open-source projects.

GLM-5.3-Flash

MIT

Open weights

Ministral 3 (14B Base 2512)

Apache 2.0

Open weights

Release Timeline

When each model was launched

GLM-5.3-Flash was released on 2026-08-26, while Ministral 3 (14B Base 2512) was released on 2025-12-04.

GLM-5.3-Flash is 9 months newer than Ministral 3 (14B Base 2512).

GLM-5.3-Flash

Aug 26, 2026

1 days ago

8mo newer
Ministral 3 (14B Base 2512)

Dec 4, 2025

8 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→

Judge for yourself.

Run your own prompts against GLM-5.3-Flash and Ministral 3 (14B Base 2512) side-by-side, then vote on the output you prefer.

GLM-5.3-Flash
✓ Preferred
Ministral 3 (14B Base 2512)
Open in Playground

FAQ

Common questions about GLM-5.3-Flash vs Ministral 3 (14B Base 2512).

Which is better, GLM-5.3-Flash or Ministral 3 (14B Base 2512)?

GLM-5.3-Flash (Zhipu AI) and Ministral 3 (14B Base 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-5.3-Flash compare to Ministral 3 (14B Base 2512) in benchmarks?

GLM-5.3-Flash scores CharXiv-R: 89.4%, Terminal-Bench 2.1: 84.3%, MMVU: 80.5%, Toolathlon: 78.4%, Chartography: 78.0%. Ministral 3 (14B Base 2512) scores MMLU-Redux: 82.0%, MMLU: 79.4%, TriviaQA: 74.9%, Multilingual MMLU: 74.2%, MATH (CoT): 67.6%.

What are the context window sizes for GLM-5.3-Flash and Ministral 3 (14B Base 2512)?

GLM-5.3-Flash supports 1.0M tokens and Ministral 3 (14B Base 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-5.3-Flash and Ministral 3 (14B Base 2512)?

Key differences include licensing (MIT vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

Who makes GLM-5.3-Flash and Ministral 3 (14B Base 2512)?

GLM-5.3-Flash is developed by Zhipu AI and Ministral 3 (14B Base 2512) is developed by Mistral AI.

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