Model Comparison

DeepSeek-V3.2 (Non-thinking) vs Mistral Large 3

Comparing DeepSeek-V3.2 (Non-thinking) and Mistral Large 3 across benchmarks, pricing, and capabilities.

Want to compare interactively?Try the playground

Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

DeepSeek-V3.2 (Non-thinking) and Mistral Large 3 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

DeepSeek-V3.2 (Non-thinking) costs less

For input processing, DeepSeek-V3.2 (Non-thinking) ($0.28/1M tokens) is 7.1x cheaper than Mistral Large 3 ($2.00/1M tokens).

For output processing, DeepSeek-V3.2 (Non-thinking) ($0.42/1M tokens) is 11.9x cheaper than Mistral Large 3 ($5.00/1M tokens).

In conclusion, Mistral Large 3 is more expensive than DeepSeek-V3.2 (Non-thinking).*

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

Lowest available price from all providers
Thu Apr 16 2026 • llm-stats.com
DeepSeek
DeepSeek-V3.2 (Non-thinking)
Input tokens$0.28
Output tokens$0.42
Best providerDeepSeek
Mistral AI
Mistral Large 3
Input tokens$2.00
Output tokens$5.00
Best providerMistral
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

10.0B diff

DeepSeek-V3.2 (Non-thinking) has 10.0B more parameters than Mistral Large 3, making it 1.5% larger.

DeepSeek
DeepSeek-V3.2 (Non-thinking)
685.0Bparameters
Mistral AI
Mistral Large 3
675.0Bparameters
685.0B
DeepSeek-V3.2 (Non-thinking)
675.0B
Mistral Large 3

Context Window

Maximum input and output token capacity

DeepSeek-V3.2 (Non-thinking) accepts 131,072 input tokens compared to Mistral Large 3's 128,000 tokens. Both models can generate responses up to 8,192 tokens.

DeepSeek
DeepSeek-V3.2 (Non-thinking)
Input131,072 tokens
Output8,192 tokens
Mistral AI
Mistral Large 3
Input128,000 tokens
Output8,192 tokens
Thu Apr 16 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Mistral Large 3 supports multimodal inputs, whereas DeepSeek-V3.2 (Non-thinking) does not.

Mistral Large 3 can handle both text and other forms of data like images, making it suitable for multimodal applications.

DeepSeek-V3.2 (Non-thinking)

Text
Images
Audio
Video

Mistral Large 3

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-V3.2 (Non-thinking) is licensed under MIT, while Mistral Large 3 uses Apache 2.0.

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

DeepSeek-V3.2 (Non-thinking)

MIT

Open weights

Mistral Large 3

Apache 2.0

Open weights

Release Timeline

When each model was launched

DeepSeek-V3.2 (Non-thinking) was released on 2025-12-01, while Mistral Large 3 was released on 2025-09-01.

DeepSeek-V3.2 (Non-thinking) is 3 months newer than Mistral Large 3.

DeepSeek-V3.2 (Non-thinking)

Dec 1, 2025

4 months ago

3mo newer
Mistral Large 3

Sep 1, 2025

7 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

Provider Availability

DeepSeek-V3.2 (Non-thinking) is available from DeepSeek. Mistral Large 3 is available from Mistral AI.

DeepSeek-V3.2 (Non-thinking)

deepseek logo
DeepSeek
Input Price:Input: $0.28/1MOutput Price:Output: $0.42/1M

Mistral Large 3

mistral logo
Mistral
Input Price:Input: $2.00/1MOutput Price:Output: $5.00/1M
* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

DeepSeek
Larger context window (131,072 tokens)
Less expensive input tokens
Less expensive output tokens
Mistral AI
Supports multimodal inputs

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-V3.2 (Non-thinking)
Mistral AI
Mistral Large 3

FAQ

Common questions about DeepSeek-V3.2 (Non-thinking) vs Mistral Large 3

DeepSeek-V3.2 (Non-thinking) (DeepSeek) and Mistral Large 3 (Mistral AI) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.
Mistral Large 3 scores MATH: 90.4%, MM-MT-Bench: 84.9%, MMLU-Redux: 82.0%, TriviaQA: 74.9%, MMMLU: 74.2%.
DeepSeek-V3.2 (Non-thinking) is 7.1x cheaper for input tokens. DeepSeek-V3.2 (Non-thinking) costs $0.28/M input and $0.42/M output via deepseek. Mistral Large 3 costs $2.00/M input and $5.00/M output via mistral.
DeepSeek-V3.2 (Non-thinking) supports 131K tokens and Mistral Large 3 supports 128K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include context window (131K vs 128K), input pricing ($0.28 vs $2.00/M), multimodal support (no vs yes), licensing (MIT vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.
DeepSeek-V3.2 (Non-thinking) is developed by DeepSeek and Mistral Large 3 is developed by Mistral AI.