User Compares Qwen 3.8 Flash Next and DeepSeek V4.1 Flash Models
A user shared a local benchmark comparison using HumanEval between Qwen3.8-Flash-Next-W4A16-FP8PLE and a custom quantized DeepSeek-V4.1-Flash model. The Qwen model achieved full marks on a small sample while the DeepSeek model failed certain coding tasks after entering a timeout or reasoning loop. Local LLM enthusiasts frequently benchmark custom quantized model variants to determine optimal memory usage and coding performance for local hardware setups. This provides informal insights into how different architecture configurations handle reasoning tasks under specific constraints. The Qwen model used W4A16-FP8PLE quantization at an xhigh reasoning setting, whereas the DeepSeek model used a custom 2.6-bit quantization at max reasoning. During the evaluation, the DeepSeek model experienced a doom loop and a lengthy timeout on specific Python programming prompts.
## BACKGROUND
LLM quantization is a technique that reduces memory and compute requirements by converting model weights from high-precision formats to lower-precision formats like INT4 or low-bit representations. HumanEval is a standard benchmark used to evaluate the coding capabilities of language models through Python programming problems.