DeepSeek R1 Distill Qwen 32B vs Ministral 3 (8B Reasoning 2512) Comparison

Performance Benchmarks

Comparative analysis across standard metrics

3 benchmarks

DeepSeek R1 Distill Qwen 32B outperforms in 0 benchmarks, while Ministral 3 (8B Reasoning 2512) is better at 3 benchmarks (AIME 2024, GPQA, LiveCodeBench).

Ministral 3 (8B Reasoning 2512) significantly outperforms across most benchmarks.

Sat Mar 14 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

DeepSeek R1 Distill Qwen 32B costs less

For input processing, DeepSeek R1 Distill Qwen 32B ($0.12/1M tokens) is 1.3x cheaper than Ministral 3 (8B Reasoning 2512) ($0.15/1M tokens).

For output processing, DeepSeek R1 Distill Qwen 32B ($0.18/1M tokens) is 1.2x more expensive than Ministral 3 (8B Reasoning 2512) ($0.15/1M tokens).

In conclusion, Ministral 3 (8B Reasoning 2512) is more expensive than DeepSeek R1 Distill Qwen 32B.*

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

Lowest available price from all providers
Sat Mar 14 2026 • llm-stats.com
DeepSeek
DeepSeek R1 Distill Qwen 32B
Input tokens$0.12
Output tokens$0.18
Best providerDeepinfra
Mistral AI
Ministral 3 (8B Reasoning 2512)
Input tokens$0.15
Output tokens$0.15
Best providerMistral
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Model Size

Parameter count comparison

24.8B diff

DeepSeek R1 Distill Qwen 32B has 24.8B more parameters than Ministral 3 (8B Reasoning 2512), making it 310.0% larger.

DeepSeek
DeepSeek R1 Distill Qwen 32B
32.8Bparameters
Mistral AI
Ministral 3 (8B Reasoning 2512)
8.0Bparameters
32.8B
DeepSeek R1 Distill Qwen 32B
8.0B
Ministral 3 (8B Reasoning 2512)

Context Window

Maximum input and output token capacity

Ministral 3 (8B Reasoning 2512) accepts 262,100 input tokens compared to DeepSeek R1 Distill Qwen 32B's 128,000 tokens. Ministral 3 (8B Reasoning 2512) can generate longer responses up to 262,100 tokens, while DeepSeek R1 Distill Qwen 32B is limited to 128,000 tokens.

DeepSeek
DeepSeek R1 Distill Qwen 32B
Input128,000 tokens
Output128,000 tokens
Mistral AI
Ministral 3 (8B Reasoning 2512)
Input262,100 tokens
Output262,100 tokens
Sat Mar 14 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Ministral 3 (8B Reasoning 2512) supports multimodal inputs, whereas DeepSeek R1 Distill Qwen 32B does not.

Ministral 3 (8B Reasoning 2512) can handle both text and other forms of data like images, making it suitable for multimodal applications.

DeepSeek R1 Distill Qwen 32B

Text
Images
Audio
Video

Ministral 3 (8B Reasoning 2512)

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek R1 Distill Qwen 32B is licensed under MIT, while Ministral 3 (8B Reasoning 2512) uses Apache 2.0.

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

DeepSeek R1 Distill Qwen 32B

MIT

Open weights

Ministral 3 (8B Reasoning 2512)

Apache 2.0

Open weights

Release Timeline

When each model was launched

DeepSeek R1 Distill Qwen 32B was released on 2025-01-20, while Ministral 3 (8B Reasoning 2512) was released on 2025-12-04.

Ministral 3 (8B Reasoning 2512) is 11 months newer than DeepSeek R1 Distill Qwen 32B.

DeepSeek R1 Distill Qwen 32B

Jan 20, 2025

1.1 years ago

Ministral 3 (8B Reasoning 2512)

Dec 4, 2025

3 months ago

10mo 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

DeepSeek R1 Distill Qwen 32B is available from DeepInfra. Ministral 3 (8B Reasoning 2512) is available from Mistral AI. The availability of providers can affect quality of the model and reliability.

DeepSeek R1 Distill Qwen 32B

deepinfra logo
Deepinfra
Input Price:Input: $0.12/1MOutput Price:Output: $0.18/1M

Ministral 3 (8B Reasoning 2512)

mistral logo
Mistral
Input Price:Input: $0.15/1MOutput Price:Output: $0.15/1M
* Prices shown are per million tokens

Outputs Comparison

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Key Takeaways

Less expensive input tokens
Larger context window (262,100 tokens)
Supports multimodal inputs
Less expensive output tokens
Higher AIME 2024 score (86.0% vs 83.3%)
Higher GPQA score (66.8% vs 62.1%)
Higher LiveCodeBench score (61.6% vs 57.2%)

Detailed Comparison