---
title: "How much does quantization change DeepSeek’s accuracy on math problems?"  
description: "How much does quantization change DeepSeek’s accuracy on math problems?"  
author: "Eetu Saarinen"  
published: 2026-10-21  
updated: 2026-10-21  
canonical: https://www.mindstick.com/forum/162614/how-much-does-quantization-change-deepseek-s-accuracy-on-math-problems  
category: "DeepSeek Models"  
tags: ["DeepSeek", "quantization", "Local AI", "Model evaluation"]  
reading_time: 1 minute  

---

# How much does quantization change DeepSeek’s accuracy on math problems?

I’m comparing quantized versions of a DeepSeek model for local use and want to understand the accuracy trade-off, not just the difference in speed or memory use. Has anyone tested whether lower-bit versions make more mistakes on multi-step math problems, especially when the final answer looks plausible?

If you’ve run a comparison, what did you use as a test set, and did the results vary much between model sizes or quantization formats? I’m trying to decide what level of quantization is acceptable for a workload where wrong answers matter more than a few extra seconds.


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Original Source: https://www.mindstick.com/forum/162614/how-much-does-quantization-change-deepseek-s-accuracy-on-math-problems

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