Qwen3.8-Flash-Next: Time to Update Those Benchmarks

specs hardware: M4 Max 128GB Studio inference engine: oMLX & lllama.cpp insights it still very early, so had to disable oMLX K/V caching, qwen4_exp architectureis not yet supported + the obvious n-grams with which the whole 4 bit quant takes ~100G, so pretty tight nevertheless, this is the first model for the year that was able to break through 94% on my cupel benchmark one interesting bit is Qwen 3.8 27B is obviously great, but it did not do that well, since I have coding, general knowledge and science. it did outperform most in coding, but its general knowledge lost to Gemma 31B as well as to Qwen 3.6 omlx this is the quant I tried with oMLX, which performed better than other 4 bit quants due to the mixed quantization: pipenetwork/Qwen3.8-Flash-Next-MLX-mixed-4_8bit buld perplexity bfloat16 4.4708 mixed-4_8bit 4.5286 llama.cpp this is a very good quant from Unsloth, it is not as strong as "MLX-mixed-4_8bit", but I could not fit a larger one from unsloth to be able to bench. You can see it on position #6 in the above leaderboard unsloth/Qwen3.8-Flash-Next-GGUF:UD-IQ4_XS I am working on collecting all I did for the last few months codingwise, and will add more pieces into the benchmark (hermes => pi / opencode, etc..) because models are getting too good to differentiate: I love it! submitted by /u/tolitius [link] [comments]

Related

• [[megathread-qwen38-flash-next---release-day|[Megathread] Qwen3.8-Flash-Next - Release Day]]
• Qwen 3.8 27B SlopCodeBench results
• Benchmark results: what is the best and fastest engine to run Qwen3.8-27B on macOS

Source: r/LocalLLaMA | 2026-08-27