# Unsloth releases Dynamic v3.1 GGUFs for Qwen3.8-Flash-Next

> Original title: We're releasing new Unsloth Dynamic v3.1 GGUFs for Qwen3.8-Flash-Next! They're better by 10-15% accuracy on DeepSWE, Terminal Bench, Aide...

- Source: Daniel Han
- Published: 2026-11-04T17:47:47.000Z
- Bad Influence: https://badinfluence.news/items/fyoijdsdrmbox49cxvgfkb0cc
- Original: https://x.com/danielhanchen/status/2118037508139413742

## Summary

Unsloth says it released Dynamic v3.1 GGUF quantizations of Qwen3.8-Flash-Next that score 10-15% higher accuracy than GSQ-RCO on DeepSWE, Terminal Bench, and Aider Polyglot. The quantizations run on setups with 32GB+ RAM, and Unsloth published RMSE versus BF16 for each format: Q8_0 at 54.4 GB (0.0053), Q5_1 at 38.4 GB (0.0376), and IQ4_NL at 28.8 GB (0.0757).

## Post

We're releasing new Unsloth Dynamic v3.1 GGUFs for Qwen3.8-Flash-Next! They're better by 10-15% accuracy on DeepSWE, Terminal Bench, Aider Polyglot and more vs GSQ-RCO.

We also provide a new engram switching guide for BF16, Q8_0, Q5_1, IQ4_NL!

This builds upon our DeepSeek-V4.1-Flash UD3.1 work, which improved over 10-20% accuracy vs others.

All benchmarks are 256K for agentic ones, and 128K for the rest. We used temperature 1.0, top_p 0.95, top_k 20, min_p 0, no presence or repetition penalty. preserve_thinking and xhigh reasoning, and ran over 26B of tokens for benchmarking.

The RMSE vs BF16 for each of the engrams:
BF16   102.4 GB    0
Q8_0     54.4 GB   0.0053
Q5_1     38.4 GB   0.0376
IQ4_NL  28.8 GB   0.0757

More details at https://unsloth.ai/docs/models/qwen3.8-next#dynamic-v3.1-update-november-2nd-2026. We also moved all benchmarking, grading, infra issues and more to https://unsloth.ai/docs/basics/dynamic-3.0-ggufs/dynamic-3.1-ggufs

![](https://badinfluence.news/api/img-proxy?u=https%3A%2F%2Fpbs.twimg.com%2Fmedia%2FHWTG_1EakAAiIPd.jpg&mode=thumb&exp=1794009600&sig=306783852a2a24cd)

## Quoted post @UnslothAI

> We’re releasing new Qwen3.8-Flash-Next GGUFs with 15% higher accuracy.
> 
> Unsloth Dynamic V3.1 outperforms others by >15% on Terminal Bench, DeepSWE & more benchmarks (256K context).
> 
> Run on 32GB+ RAM setups.
> 
> GGUF: https://huggingface.co/unsloth/Qwen3.8-Flash-Next-GGUF
> Blog: https://unsloth.ai/docs/models/qwen3.8-next

https://x.com/UnslothAI/status/2118033433045991660