Alibaba's own table shows what eyes cost a search model: nearly three points of text accuracy

A model that indexes a search engine does not talk. It converts a document into a vector, and the engine finds related material by measuring distance. Alibaba's Qwen3-VL-Embedding line, published on 7 January 2026 and added to our catalogue today, does that for pictures too: a photograph, a screenshot, a scanned invoice or a video clip lands in the same coordinate space as a sentence. The interesting number is not in the marketing. It is in a second table on the same model card. On MMTEB, the multilingual text benchmark, Qwen3-VL-Embedding-8B scores 67.88. Alibaba's own text-only Qwen3-Embedding-8B — same company, same parameter count, published earlier — scores 70.58. The smaller pairing is starker: the 2-billion multimodal model scores 63.87, while the text-only 4-billion model reaches 69.45. Read plainly, that is the price list. Giving a retrieval model eyes is not a free addition; on text it makes the model measurably worse than the vendor's own text model of matching size. If a search index holds nothing but text, the older instrument is the better one — and Alibaba printed the evidence itself rather than leaving it to a reviewer. The suite's second finding concerns which model people actually run. Alibaba published four: embedding and reranking, in 2-billion and 8-billion sizes. The 8-billion reranker wins every column of the vendor's comparison — 79.2 on multimodal retrieval, 86.3 on visual document retrieval, 66.7 on ViDoRe v3. In the thirty days to 1 September 2026 it was downloaded 43,201 times. The 2-billion reranker, which loses to it everywhere, was downloaded 2.07 million times: roughly forty-eight times more. That gap is architecture, not fashion. A reranker runs once per candidate, so a shortlist of a hundred documents means a hundred forward passes before a user sees a result. At eight billion parameters that is a cost few search boxes can carry, and the accuracy leader ends up confined to archives where waiting is acceptable — legal discovery, medical records, technical documentation. One more figure is worth carrying into a design decision, because it points the money in an unexpected direction. Comparing two models of identical size, the 2-billion reranker beats the 2-billion embedding model by 7.9 points on ViDoRe v3 and 9.9 on JinaVDR, both measuring retrieval from scanned and rendered pages. On ordinary photographs and video it is marginally behind. The second stage earns its keep precisely where a document is a picture of text — invoices, slides, forms — which suggests that a team with a fixed budget should add a stage before it buys a bigger model. All four models carry Apache 2.0 weights with no European carve-out, support more than 30 languages and a 32,768-token context. Profiles for each are now in the catalogue.