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[ARTICLE · art-164114] src=runtimewire.com ↗ pub= topic=large-language-models verified=true sentiment=↑ positive

Unsloth releases Dynamic v3.1 GGUFs for Qwen3.8-Flash-Next, says accuracy is up 15%

Unsloth co-founder Daniel Han released Dynamic v3.1 GGUFs for Qwen3.8-Flash-Next on November 4th, claiming the quantized files score 10-15% higher than GSQ-RCO on DeepSWE, Terminal Bench, Aider Polyglot and other tests in Unsloth's own benchmarking, with accuracy up 15%. The release offers swappable engrams ranging from 28.8 GB to 102.4 GB, giving users a direct trade between file size and measured error for running the 125B-parameter model locally.

by read1 min views6 publishedNov 4, 2026
Unsloth releases Dynamic v3.1 GGUFs for Qwen3.8-Flash-Next, says accuracy is up 15%
Image: Runtimewire (auto-discovered)

Daniel Han's Unsloth says the new quants beat GSQ-RCO by 10-15% on agentic coding tests, with swappable engrams from 28.8 GB to 102.4 GB.

        By [Ryan Merket](https://runtimewire.com/author/ryan-merket)
        · Published 
        · Updated 

Primary source: [x.com](https://x.com/danielhanchen/status/2118037508139413742)

Why it matters #

Quantization affects whether a 125B-parameter model can run locally. Unsloth's engram switching gives users a direct trade between file size and measured error.

Unsloth co-founder Daniel Han (@danielhanchen) released new Dynamic v3.1 GGUFs for Qwen3.8-Flash-Next on November 4th, saying the files score 10-15% higher on DeepSWE, Terminal Bench, Aider Polyglot and other tests than GSQ-RCO in Unsloth's own benchmarking.…

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