The newer Qwen loses to the older one in most tests — and the config file says why

Alibaba's first open-weights model of the Qwen3.6 line comes with a comparison table in which the company's own model from seven weeks earlier scores higher on most rows. The version number went from 3.5 to 3.6; the configuration file inside the repository still identifies the architecture as qwen3_5_moe. On the vendor's own numbers, Qwen3.5-27B beats the newer Qwen3.6-35B-A3B on SWE-bench Verified (75.0 against 73.4), SWE-bench Multilingual (69.3 against 67.2), SWE-bench Pro (51.2 against 49.5), MMLU-Pro (86.1 against 85.2) and Humanity's Last Exam (24.3 against 21.4). Four of those five are the benchmarks a buyer would look at first. The newer model wins a narrower and more specific set. Terminal-Bench 2.0 goes from 41.6 to 51.5 — ten points on a test that measures whether a model can keep working in a shell without a human. QwenWebBench goes from 1,068 to 1,397. NL2Repo, which asks a model to build a repository rather than patch one, goes from 27.3 to 29.4. These are the tasks an autonomous agent runs for minutes at a time, calling tools in a loop. The design explains the split. Qwen3.5-27B is dense: all 27.78 billion parameters compute on every token. Qwen3.6-35B-A3B is a mixture of experts — 35.95 billion parameters in store, 256 experts, of which 8 routed plus 1 shared fire per token, so roughly 3 billion are active. Per token it costs something like an eighth of the dense model to run. An agent that makes hundreds of tool calls to finish one task pays that cost hundreds of times; a chat that answers once pays it once. So the newer release is not a better model. It is a cheaper one that lost little and gained where the cost matters. Alibaba does not oversell it either — the release notes describe the work as built on community feedback and prioritising "stability and real-world utility", and against its actual predecessor at the same size and sparsity, Qwen3.5-35B-A3B, the gains are unambiguous: Terminal-Bench from 40.5 to 51.5, QwenWebBench from 978 to 1,397, NL2Repo from 20.5 to 29.4. What the notes do not mention is the model_type field. A version bump that leaves the architecture identifier untouched is a post-training and reinforcement-learning revision, not a new family — useful to know before assuming that a higher number means a rebuilt model. There is a second reading, less flattering to the whole industry. The same model card that credits Qwen3.5-27B with 75.0 on SWE-bench Verified sits next to that model's own card, which reports 72.4. Alibaba does not explain the 2.6-point difference; the older repository was updated the same day the newer card appeared. Both figures are vendor-reported, and neither has been independently reproduced. Three profiles from these two generations have been added to the catalogue: Qwen3.5-9B, Qwen3.5-27B and Qwen3.6-35B-A3B. All are Apache 2.0.