We're releasing new Unsloth v3.1 GGUF quants for DeepSeek-V4.1-Flash-1017. We ran benchmarks and v3 + v3.1 do better than GSQ-RCO. We also cannot reproduce numbers ran by GSQ-RCO - AIME25 for 1bit is not 100% but rather 91.88%. Livecodebench and GPQA are similarly incorrect. There are also claims that KLD is not a good metric, and that people can "game" KLD. The pirmary goal of quantization is to minimize KLD but also minimize disk space. If you get a KLD of 0, this means your quantized model == 16bit model, as logits are perfectly modelled. We ran benchmarks on multiple benchmarks including AIME25, Aider Polyglot, LiveCodeBench v6, HMMT 2026, GPQA Diamond, MMLU-Pro and IFEval, and also recorded reasoning length to see if lower bit quants think for longer. In we show that lower bit quants do think somewhat longer than the unquantized model - we plan to investigate and make quants even better. However, we're still much ahead vs GSQ-RCO on even reasoning length as well. We provide full benchmarking data, benchmarking methods (for eg we ran AIME25 16 times per question and took an average) and more in our updated blog at