Reddit user reports mixed Qwen3.8-Flash-Next performance
A Reddit user said Qwen3.8-Flash-Next performed well on coding and adjacent tasks, including evaluating open-source repositories and running models. In general research, however, the user reported confusion, verbose and dense prose, and difficulty maintaining consistent answers. The test used Pi with llama.cpp, UD_Q4_K_M quantization, and MTP, with sampling parameters copied from Qwen3.8 27B.
The report highlights a potentially task-dependent pattern in Qwen3.8-Flash-Next, combining strong local coding performance with weaker general-research behavior, but it remains an unverified user account.
I was quite excited with how well QFN did with some coding and adjacent tasks (like evaluating OSS repos, downloading and running models etc.). Using it for more general research is starting to show that it isnt actually very intelligent and gets lost in its own maze seemingly.
Here's a recent example to a simple question: "Have humans always slept for a huge amount of time at once, like eight hours at once, or been multiple sleep sessions through the day from like the early modern civilization?"
The verbosity and dense way of writing is too much (feels like a lot of Opus 5 distillation)
Setup:
- pi
- llama.cpp, UD_Q4_K_M with MTP.
- Copied from Qwen3.8 27B: --temp 1 --top-p 0.95 --top-k 20 --min-p 0.00 --presence-penalty 0 --repeat-penalty 1.0
- Minimal AGENTS.md, mostly focused on sw related tasks
Chat session: https://share.jotbird.com/radiant-bold-desert-wind
TL;DR: forgetting the complexity of the first question, the responses to simple follow ups: "Are there logical inconsistencies inside your response?", "Why did this happen?" and "So how do we fix this so that it never happens again?" are** not only massive essays, but each "uncovers more" stuff. To the point I have lost faith in what QFN is saying**.
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