Alibaba publishes four small models and quietly stops recommending two of them for real work

Vendors rarely tell you where their own product stops being useful. Alibaba does, in one sentence, and it is easy to miss. The Qwen3.5 generation runs from a 2.4-trillion-parameter flagship down to a model of 800 million. We have now catalogued the bottom three rungs — Qwen3.5-4B, Qwen3.5-2B and Qwen3.5-0.8B — and reading their model cards side by side shows a line the company draws through its own line-up. At 4B and above, the card describes a product. At 2B and below, it adds a sentence that appears nowhere higher up: given the parameter scale, the intended uses are prototyping, task-specific fine-tuning, and research or development. In the same place, a second thing disappears. The 27B, 9B and 4B cards all promise a context window of 262,144 tokens extensible to 1,010,000. The 2B and 0.8B cards promise the 262,144 and say nothing about the million. The benchmark tables show why. On text, the 0.8B scores 11.9 on GPQA and 8.2 on PolyMATH, and on the HMMT competition-mathematics sets Alibaba reports no figure at all — the dashes in its own table are the honest answer. Instruction following lands at 44.0, meaning a complicated prompt is not reliably obeyed. What does not collapse is perception. Every model in this generation is a vision-language model, and the small ones read remarkably well for their size. The 2B posts 84.5 on OCRBench, beating not only the previous generation's dedicated 2B vision model (79.2) but its 4B one as well (80.8). The 0.8B, at roughly 1.7 GB of weights, reaches 74.5 — and 79.1 with thinking switched off, which is level with a dedicated vision model more than twice its size. That is the practical reading. Below two billion parameters, Alibaba's own documentation says: use these to read, label and extract, or to fine-tune for one narrow job. Do not use them to think. It is an unusually candid piece of labelling in a field where every release is described as a breakthrough, and it is worth more to a reader choosing a local model than any leaderboard position.