DeepSeek-V3.2 (Non-thinking) vs Mistral Large 4
Comparing DeepSeek-V3.2 (Non-thinking) and Mistral Large 4 across benchmarks, pricing, and capabilities.
DeepSeek · Mistral AI · Updated for 2026
Which is better?
DeepSeek-V3.2 (Non-thinking) and Mistral Large 4 trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.
On price, DeepSeek-V3.2 (Non-thinking) is roughly 3.3x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Mistral Large 4 also accepts a larger context window (1,000,000 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 DeepSeek-V3.2 (Non-thinking)
- cost matters — it's about 3.3x cheaper per token
- you need open weights you can self-host or fine-tune
Choose Mistral Large 4
- you process long inputs — it offers a 1,000,000 token context window
- you want the most recent training data — it shipped Oct 2026
At a glance
The differences that matter most.
Individual benchmarks
0 reported for DeepSeek-V3.2 (Non-thinking) · 15 for Mistral Large 4
DeepSeek-V3.2 (Non-thinking) and Mistral Large 4don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V3.2 (Non-thinking) ($0.28/1M tokens) is 2.4x cheaper than Mistral Large 4 ($0.68/1M tokens).
For output processing, DeepSeek-V3.2 (Non-thinking) ($0.42/1M tokens) is 5.0x cheaper than Mistral Large 4 ($2.09/1M tokens).
In conclusion, Mistral Large 4 is more expensive than DeepSeek-V3.2 (Non-thinking).*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Mistral Large 4 has 365.0B more parameters than DeepSeek-V3.2 (Non-thinking), making it 53.3% larger.
Context Window
Maximum input and output token capacity
Mistral Large 4 accepts 1,000,000 input tokens compared to DeepSeek-V3.2 (Non-thinking)'s 131,072 tokens. Only DeepSeek-V3.2 (Non-thinking) specifies output context (8,192 tokens).
Input capabilities
Documented input modalities across available providers
Mistral Large 4 supports multimodal inputs, whereas DeepSeek-V3.2 (Non-thinking) does not.
Mistral Large 4 can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V3.2 (Non-thinking)
Mistral Large 4
License
Usage and distribution terms
DeepSeek-V3.2 (Non-thinking) is licensed under MIT, while Mistral Large 4 uses a proprietary license.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Proprietary
Closed source
Release Timeline
When each model was launched
DeepSeek-V3.2 (Non-thinking) was released on 2025-12-01, while Mistral Large 4 was released on 2026-10-06.
Mistral Large 4 is 10 months newer than DeepSeek-V3.2 (Non-thinking).
Dec 1, 2025
10 months ago
Oct 6, 2026
1 days ago
10mo newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
DeepSeek-V3.2 (Non-thinking) is available from DeepSeek. Mistral Large 4 is available from Mistral AI.
DeepSeek-V3.2 (Non-thinking)
Mistral Large 4
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V3.2 (Non-thinking) and Mistral Large 4 side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V3.2 (Non-thinking) vs Mistral Large 4.