TL;DR - Swift Flash is a killer model that massively reduces excess reasoning. Try it out! If you haven't seen from my previous comparison posts, I'm a huge fan of the Swift Qwen3.8 models. I've been using 27B since it dropped, and I'm really impressed with the performance and quality (v1.5 is even better). The reduction in overthinking is a huge win, and quality seems to be essentially equivalent in real-world use and benchmarking. The time savings are massive. When UkisAI told me they were planning to release a Swift Flash model, I was beyond hype. That's my daily, the best model I've ever used locally, but it thinks even more than 3.8-27B on very hard problems. I downloaded Q5_K_L (with Q8_0 engrams) to compare with Unsloth's Q5_K_XL base (also Q8_0 engrams). This is the highest quality that fits safely in 128GB with SSD engrams & 262k context, and I think it's as fair of a comparision as I can put together. As usual, I ran the same Aider agentic coding benchmark I run on every model. I get a lot of good data from it, including first-try and retry pass rates, median token use, wall-clock, tokens/solve, and well-formed diff rates. Here's the chart: model First-try pass Retry pass tokens/case sec/case tok/solve well-formed diff Qwen3.8-Flash-Next (xhigh) 40.2% 90.7% 17646 1542 24.8K 98.1% Swift-1.5-Qwen3.8-Flash-Next (xhigh) 41.1% 86.9% 6991 608 10.5K 100.0% As you can see, Swift performs almost exactly as well as the base model. The differences don't quite reach statistical significance on a dataset of this size, given the inherent noise in the benchmark results. Realistically, ~5% difference is significant here, and we're seeing under 4%. From first-try pass you can see that Swift gets the easier ones at the same rate as base, and loses out slightly on the hardest ones requiring a second attempt. Base recovers 84% of cases requiring a retry, vs. only 78% for Swift. For token use and wall clock, there is no comparison. Swift does what UkisAI claims -- it uses literally 40% of the median tokens and completes tasks in 40% of the median time, with nearly the same quality. That's incredible, and it's a testament to their RL/OPD work. One particularly valuable insight: base frequently goes on long reasoning binges, looping back several times on itself. Swift almost never does. On base's 20 most token-hungry runs, Swift used 29% of the tokens and solved 16/20 vs. base's 17/20. It keeps nearly all of the quality even on the most-challenging problems where base thought the hardest. The most tokens Swift uses on any case is 44k, against 203k for base. Here's a breakdown of the top 3 coding languages: model cpp javascript python Qwen3.8-Flash-Next (xhigh) 23.1% / 84.6% 37.5% / 91.7% 57.6% / 93.9% Swift-1.5-Qwen3.8-Flash-Next Q5_K_L (xhigh) 30.8% / 73.1% 41.7% / 93.8% 48.5% / 87.9% Paired vs base (n=107): 99 agree, 2 gains, 6 losses (net −4), McNemar exact p ≈ 0.29 (not significant). Once again, they're statistically indistinguishable in quality. C++ is the most compressed, at just 29% of base's token use (vs. ~46% for python/javascript), and it takes 3/6 losses as well. Worth knowing if you code a lot in C++. Anyways, I think this post is long enough. I'm sure some of you wish there was a Swift version of me by now. Hopefully you got something out of it. Thanks u/Secure_Recording_472 and UkisAI team for sharing such a useful model with the community!   submitted by   /u/returnity [link]   [comments]