Local agentic coding Benchmark : Qwen3.8-Flash-Next NVFP4 vs 27B (and the others...)

Reddit r/LocalLLaMA Models

Summary

The benchmark reveals that Qwen3.8-Flash-Next-NVFP4 achieves nearly the highest scores among local models, with superior efficiency in request handling and token generation, and notable speed despite being undertrained.

Using https://huggingface.co/RadixArk/Qwen3.8-Flash-Next-NVFP4 and https://old.reddit.com/r/BlackwellPerformance/comments/1w04xb7/qwen38_flashnext_on_1x_rtx_pro_6000_171_ts_c1_428/ As usual, all the details in https://wonderrico.github.io/local_llm_benchmark/benchmark-main.html?filter=3.8 and even more in https://wonderrico.github.io/local_llm_benchmark/benchmark-detail.html?filter=3.8 (the bad score one is a "random" uncensored version from HF https://huggingface.co/dealignai/Qwen3.8-Flash-Next-UNCENSORED-NVFP4 ) I shall test other ones Bottom line : almost highest score of all local model I tested, the most efficient in both nb requests / point and fewer generated tokens / pt, all in medium reasoning. (xhigh is not useful, again, in this benchmark) and if it was not enough very fast All that for an undertrained model... https://preview.redd.it/dnk0yc90g5mh1.png?width=1366&format=png&auto=webp&s=115509753cb30cfe67f9b9d13158dcc300c3435d
Original Article

Similar Articles

Qwen3.8-Flash-Next-NVFP4 vs Qwen3.8-27B-FP Test Results

Reddit r/LocalLLaMA

This article presents detailed test results comparing the performance of Qwen3.8-Flash-Next-NVFP4 and Qwen3.8-27B-FP8 AI models across various tasks, highlighting that Flash-Next is faster with fewer failures but struggles with multi-step symbolic work.

Qwen3.8-FN at NVFP4 ( Basalt vs cachy-flash vs ninfer-ext)

Reddit r/LocalLLaMA

对 Qwen3.8-Flash-Next NVFP4 模型在单张 RTX 5090 上的三个推理引擎(basalt、capy-flash、ninfer-ext)进行全 262k 上下文基准测试,basalt 在所有上下文长度和两种解码模式下均领先(prefill 8.1k-9.2k tok/s,greedy decode 176-265 tok/s),解码排名与 MTP 草稿接受率一致。