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

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 submitted by /u/WonderRico [link] [comments]

Related

• Local agentic coding Benchmark : Qwen 3.8 27B (in many weights quants / cache quants / engine / reasoning effort) vs others.
• DeepSeek v4 Flash vs. Qwen3.6-27B, 3.5-122B, and Gemma 4 31B Benchmark
• Qwen3.8-Flash-Next (UD-IQ4_XS) on 2x RTX 3060 + 7800X3D, from initial 36 tps prefill to 400 tps and other benchmarks (-sm tensor trap) + VRAM/RAM usage

Source: r/LocalLLaMA | 2026-08-28