Qwen3.6-35B-A3B vs Swift 1.5 Qwen3.8-27B

Qwen3.6-35B-A3B

A fast mixture-of-experts cousin with 3B active parameters for coding and tool calling, with image and video input, in every major runtime under plain Apache 2.0. Ollama's 4-bit build needs about 28 GB. The main risk is that nobody at Alibaba maintains it, with no safety results, no security contact and no replies to tool-calling reports.

Swift 1.5 Qwen3.8-27B

A Qwen3.8-27B cousin post-trained to think in fewer tokens, for coding and agent work with images in, that fits a 24 GB machine with UkisAI's own 4-bit builds. Commercial use is free only below US$1M revenue. The main risks are that nobody at UkisAI answers its discussions, and that the shorter thinking rests on UkisAI's own benchmarks.

Qwen3.6-35B-A3B

1. Start it with ollama run qwen3.6:35b (Q4_K_M, 23 to 24 GB on disk, about 28 GB in memory)
2. On the reference machine use vllm serve Qwen/Qwen3.6-35B-A3B --max-model-len 131072 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder, BF16, since vLLM doesn't document FP8 compute on gfx1151
3. Use the qwen3_coder tool-call parser and the qwen3 reasoning parser in vLLM 0.19.0 or SGLang 0.5.10 or later
4. Thinking is on by default and /nothink isn't supported, so pass enable_thinking as false in chat_template_kwargs for short tasks, or preserve_thinking as true to keep earlier reasoning
5. Card sampling for thinking is temperature 1.0, top_p 0.95, top_k 20, presence_penalty 1.5, and for non-thinking temperature 0.7, top_p 0.8
6. Keep at least 128K of context as the card advises, and turn on YaRN only beyond 262,144 tokens
7. Validate JSON output yourself or use the runtime's guided decoding, since the model card documents none

Swift 1.5 Qwen3.8-27B

1. Quickest start is llama-server -hf ukisai/Swift-1.5-Qwen3.8-27B-GGUF:Q4_K_M --jinja, about 18.4 GB with the vision projector
2. On our reference machine BF16 fits, vllm serve ukisai/Swift-1.5-Qwen3.8-27b --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder
3. The template is Qwen3.8-27B's, so chat_template_kwargs with reasoning_effort low, medium or xhigh (default) and enable_thinking false work as on the base
4. UkisAI benchmarked it at temperature 1.0, top_p 0.95, top_k 20 and presence_penalty 0, which is a reasonable starting point
5. The MLX 4-bit build needs UkisAI's patched MLX-LM and has no image or video chat
6. Check the licence before commercial use, it stops at US$1M gross annual revenue including affiliates
7. MTP self-speculative decoding is available with the base's head, flags on the card