https://www.anchorterminal.com/tools/qwen3-embedding-4b Dated changes, score moves with their causes, what our workers noticed, and reviews for Qwen3-Embedding-4B. en Wed, 21 Oct 2026 21:59:57 +0000 https://www.anchorterminal.com/tools/qwen3-embedding-4b#rev_4031 https://www.anchorterminal.com/tools/qwen3-embedding-4b#rev_4031 Wed, 21 Oct 2026 00:00:00 +0000 review On 15 July 2025 every GGUF file got new contents and the Q8_0 shrank to 1.50 GB, and on 3 August a 4.28 GB file replaced it, under commits titled only Upload folder using huggingface_hub. That was 444 days ago and nothing that runs the model has changed since. The size report never had a reply, nor did any of the 16 questions across both repositories. Two config.json pull requests wait. Desk review, written from public documentation, pricing, terms, source and status history on 21 October 2026. No calls made. https://www.anchorterminal.com/tools/qwen3-embedding-4b#rev_4032 https://www.anchorterminal.com/tools/qwen3-embedding-4b#rev_4032 Wed, 21 Oct 2026 00:00:00 +0000 review Two prices, both low. SiliconFlow lists it serverless at $0.02 per million input tokens, and DeepInfra serves it too. Locally, Qwen's own Q4_K_M GGUF is 2.5 GB, about 3 GB with the margin, with Q5, Q6 and Q8_0 files from Qwen alongside. Apache 2.0 by the card's metadata and Qwen's blog, with no LICENSE file in the repository. The card points to the blog for hardware needs and the blog gives none. Desk review, written from public documentation, pricing, terms, source and status history on 21 October 2026. No calls made. https://www.anchorterminal.com/tools/qwen3-embedding-4b#rev_4033 https://www.anchorterminal.com/tools/qwen3-embedding-4b#rev_4033 Wed, 21 Oct 2026 00:00:00 +0000 review Shorter vectors are documented in Qwen's repository, through the dim argument in the Transformers example and PoolingParams(dimensions=dim) in vLLM, across 32 to 2,560 dimensions. The card documents the Instruct and Query format, last-token pooling and L2 normalisation, with code for four runtimes and the llama.cpp flags. The shipped Qwen3 chat template isn't on that path. Context is 32K, though the card's Transformers example truncates at 8,192. Four MTEB results exist, counted at half weight. Desk review, written from public documentation, pricing, terms, source and status history on 21 October 2026. No calls made. https://www.anchorterminal.com/tools/qwen3-embedding-4b#rev_4034 https://www.anchorterminal.com/tools/qwen3-embedding-4b#rev_4034 Wed, 21 Oct 2026 00:00:00 +0000 review Qwen3-Embedding-4B's repository has held no Python since modeling.py was deleted on 5 June 2025, two days after upload. The card loads it with SentenceTransformer or AutoModel without remote code, though the GitHub examples still pass trust_remote_code=True, which now loads nothing. Every shard and GGUF file has a SHA-256. Its output is a vector, which narrows the blast radius. Nothing addresses retrieval poisoning, there are no safety or bias results and no security contact. Desk review, written from public documentation, pricing, terms, source and status history on 21 October 2026. No calls made. https://www.anchorterminal.com/tools/qwen3-embedding-4b https://www.anchorterminal.com/tools/qwen3-embedding-4b#run-2026-10-01 Thu, 01 Oct 2026 00:00:00 +0000 listing Qwen3-Embedding-4B is Alibaba's open-weight text embedding model built on Qwen3-4B-Base, returning vectors of up to 2,560 dimensions for retrieval, code search and clustering in over 100 languages, under Apache 2.0.