Swift 1.5 benchmark shows coding-agent token savings on Strix Halo
A r/LocalLLaMA user compared UkisAI’s Swift 1.5 fine-tune of Qwen3.8-Flash-Next with Unsloth’s quantization across Halogen, gufo and llama.cpp on an Asus ROG Flow Z13 with 128 GB of memory and a 70 W power limit. Swift produced 56,000–63,000 output tokens across 11 Pi coding-agent tasks, versus 75,000–99,000 for Qwen3.8-Flash-Next, and the gufo Swift setup completed them in 24 minutes compared with 33 minutes for gufo with Unsloth’s UD-Q4_K_XL. The results were mixed on math and LiveCodeBench, and the author cautioned that they came from a single run, so differences of one or two solved problems could reflect chance.
The comparison provides concrete local-inference evidence that Swift 1.5 can reduce coding-agent token use and runtime versus the tested Qwen3.8-Flash-Next setups, although its single-run, hardware-specific methodology limits confidence.
Here I am with another benchmark/comparison. I was curious about the efficiency claims of Swift 1.5, UkisAI's fine-tune of Qwen3.8-Flash-Next that thinks for a shorter time. UkisAI says Swift uses 63% fewer thinking tokens with under 1% accuracy loss.
I compared it with my current setup (Halogen) and Unsloth's UD-Q4_K_XL on my Asus ROG Flow Z13 (128 GB, 70 W). I ran the models on Halogen, gufo and llama.cpp, all with MTP. Since gufo can't load any of UkisAI's published 4-bit Swift GGUFs, and didnt have an XL version, I requantized their Swift Q8_0 to the same per-tensor types as Unsloth's UD-Q4_K_XL, with UkisAI's imatrix. It is even the same size as Unsloth's UD-Q4_K_XL (111.3 GB).
My main use is coding agents, so I also ran 11 coding tasks through Pi and threw in some math problems and LCB tasks to compare quality and speed. Speeds at 32k filled prompt. I got very interesting results but out of 1 run so take them with a grain of salt.
Here are the results sorted by total score and then total time.
#Swift 1.5#Qwen3.8-Flash-Next#UkisAI#Unsloth#Halogen#gufo#llama.cpp#Pi#Asus ROG Flow Z13