MiniMax M2.1 Quantization Performance: Q6 vs. Q8
Published:Jan 3, 2026 20:28
•1 min read
•r/LocalLLaMA
Analysis
The article describes a user's experience testing the Q6_K quantized version of the MiniMax M2.1 language model using llama.cpp. The user found the model struggled with a simple coding task (writing unit tests for a time interval formatting function), exhibiting inconsistent and incorrect reasoning, particularly regarding the number of components in the output. The model's performance suggests potential limitations in the Q6 quantization, leading to significant errors and extensive, unproductive 'thinking' cycles.
Key Takeaways
- •Q6 quantization of MiniMax M2.1 showed significant performance issues in a coding task.
- •The model exhibited flawed reasoning and struggled with a simple function.
- •The model engaged in extensive, unproductive 'thinking' cycles, indicating potential limitations of the quantization.
- •The user's experience highlights the importance of evaluating quantized models thoroughly.
Reference
“The model struggled to write unit tests for a simple function called interval2short() that just formats a time interval as a short, approximate string... It really struggled to identify that the output is "2h 0m" instead of "2h." ... It then went on a multi-thousand-token thinking bender before deciding that it was very important to document that interval2short() always returns two components.”