A user tested Qwen 3.8 27B in Q4 quantization against GPT 5.6 SOL in complex animated SVG tasks and found that Qwen performed better with fewer errors, highlighting strengths in spatial reasoning and coding.
I created 3 SVG prompts, each one rather hard. Perspective: A animated drone view perspective on a park. Beauty: A beach scene with an evil cat Composition: An AGI breaking out of a virtual sandbox prison in a lab I deliberately ran Qwen 3.8 27B in just 4 bit quantization, and used a 8 bit KV cache (given the hard long context reasoning required I didn't want to try lower) Each task contains two prompts, one core prompt and a 2nd "make it better" follow up. My expectation was that Qwen will show up as solid 2nd place, with funny errors. And the actual result was that SOL made those funny errors while Qwen was significantly better. #1 #2 https://www.reddit.com/r/LocalAIStack/comments/1vqf2vs/battle_i_gave_qwen_38_27b_in_q4_with_q8_kv_cache/ #3 https://www.reddit.com/r/LocalAIStack/comments/1vqf7pa/battle_v2_qwen_27b_q4_vs_gpt_sol_56_high/ I'm not claiming that Qwen is better than Sol generally. But .. SVG animation is a very complicated task, it involved spatial reasoning, coding, long context construction and any error made in up to 60kb of dense code will cause serious visual defects. I am sure there are plenty tasks where SOL will win, especially related to deep knowledge. It might also win at deep context, e.g. above 200k context I've yet to test Qwen 3.8 27B in agentic coding - that's not the same as two-turn coding But in these 3 elaborate SVG tests Qwen took the crown without a problem. The only scene where SOL was close, in my eyes, is the beach prompt. But SOL made grave errors in every scene, Qwen didn't. SOL was a lot more verbose in code, many details but the correctness was lacking. When looking at the details drawn, at the perspectives, at the animation paths: Each time SOL chooses something that is more simplified while Qwen chooses the hard path. And despite that SOL makes significant errors, Qwen doesn't This is stunning.
User reports Qwen 3.5 122B significantly outperforms Qwen 3.6 35B on multi-step tasks despite benchmark claims, questioning if quantization or setup issues are to blame.
Qwen 3.6 27B scored 2% on the DeepSWE benchmark, placing 18/20 above Haiku 4.5 and Minimax M2.7, highlighting the gap between local and leading-edge models.
A hands-on benchmark of four local LLMs—Qwen3.6-27B, Qwen3.6-35B, Qwen3.5-27B and Gemma 4—on a 20k-token architecture-writing task shows Qwen3.6-27B delivering the best overall balance of clarity, completeness and usefulness on an RTX 5090.
The article evaluates how different quantizations of the Qwen3.6-27B model affect output quality using KL divergence and top-1 token accuracy, as well as visual examples like SVG drawings.