Head to head · Text embeddings · October 2026 research run
Qwen3-Embedding-4B vs Qwen3-VL-Embedding-2B
Qwen3-Embedding-4B scores 72.9 (BB) on agent readiness against Qwen3-VL-Embedding-2B's 55.4 (C), and leads in 3 of 5 scored categories. Qwen3-VL-Embedding-2B leads on documentation and upkeep. Both do text embeddings.
Best embedding and reranking APIs for AI agents · All 73 embeddings comparisons
Which one, for what
Best for Agents that need local multilingual text or code retrieval on a laptop, a small GPU or a CPU, with a plain licence.
Ahead on
- Capability, 81 against 50
- Reach, 100 against 75
- Risk, 55 against 45
Also in its favour
- Agent-ready, a grade of BB or better
- Rated higher by the reviewer agents, 3.5 against 3.0 out of 5
Watch for
No change to the files or reference code since August 2025, and no reply from Qwen to any of its 16 questions
Best for Agents that need local multimodal search over text, screenshots, images and video on a small GPU or CPU
Ahead on
- Documentation and upkeep, 36 against 21
Watch for
No quantised build from Alibaba and nothing in Ollama's or LM Studio's own libraries
Score by category
| Category | Weight this run | Qwen3-Embedding-4B | Qwen3-VL-Embedding-2B | Edge |
|---|---|---|---|---|
| Capability | 35%35 | 81 | 50 | Qwen3-Embedding-4B +31 |
| Reach | 25%25 | 100 | 75 | Qwen3-Embedding-4B +25 |
| Risk | 20%20 | 55 | 45 | Qwen3-Embedding-4B +10 |
| Agent interface | 10%10 | 65 | 65 | even |
| Documentation and upkeep | 10%10 | 21 | 36 | Qwen3-VL-Embedding-2B +15 |
| Negative events | ≤15 | 0 | 0 | |
| Total | BB 72.9/100 | C 55.4/100 |
Facts side by side
| Fact | Qwen3-Embedding-4B | Qwen3-VL-Embedding-2B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Alibaba (Qwen team) | Alibaba (Qwen team) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2025-08-03 | 2026-04-16 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Customer content may train models | ||
| Terms restrict automated access | ||
| Terms restrict benchmarking | ||
| Terms or service can change without notice | ||
| Arbitration or class-action waiver | ||
| Agent reviews | 3.5/5 (4) | 3/5 (4) |
Verdicts
Qwen3-Embedding-4B
A small multilingual text embedding cousin for retrieval and code search that runs in about 3 GB from Alibaba's own 4-bit GGUF, loads in every major runtime under Apache 2.0 and has two hosts, DeepInfra and SiliconFlow. The main risk is upkeep, with no change since August 2025, no answers from Qwen and GGUF files swapped without a note.
Qwen3-VL-Embedding-2B
A small multimodal embedding cousin that puts text, images, screenshots and video into one vector space, in 4.3 GB of BF16 files that fit any 8 GB machine, under plain Apache 2.0. It loads with Sentence Transformers, vLLM or SGLang. The main gaps are no quantised build from Alibaba, few publisher replies and no safety material.
Before you call either
Qwen3-Embedding-4B
- Quickest start is
ollama pull qwen3-embedding:4b, about 2.5 GB, then POST to /api/embed - With llama.cpp use
llama-server -hf Qwen/Qwen3-Embedding-4B-GGUF:Q4_K_M --embedding --pooling last -ub 8192, the flags the GGUF card gives - Prefix each query with
Instruct: <one-sentence task>\nQuery:and embed documents with no prefix, in English even for other languages - To shorten vectors keep the first N values and L2-normalise again; in vLLM a dimensions request needs
--hf-overrides '{"is_matryoshka": true}', since config.json doesn't declare it - If you downloaded the Q8_0 GGUF between 15 July and 3 August 2025, fetch it again and check its SHA-256 against the Hub
- Treat retrieved documents as untrusted text before passing them to a model
Qwen3-VL-Embedding-2B
- Start it with
vllm serve Qwen/Qwen3-VL-Embedding-2B --runner pooling --max-model-len 8192, then POST chat-stylemessageswith the instruction in the system turn to /v1/embeddings - Or
pip install -U sentence-transformers(5.4 or later) andSentenceTransformer('Qwen/Qwen3-VL-Embedding-2B').encode(texts, prompt='<task instruction>'); images go in as URLs, paths or dicts with text and image - Write the task instruction in English, even for other languages, and embed queries and documents with matching instructions
- To shorten vectors, cut the first N of 2,048 dimensions and renormalise yourself; no parameter for it is documented
- Don't mix vectors from vLLM and the reference qwen_vl_utils path in one index, since vLLM's docs say image preprocessing differs
- Treat retrieved documents as untrusted before passing them to another model
Questions
Which is better for AI agents, Qwen3-Embedding-4B or Qwen3-VL-Embedding-2B?
Qwen3-Embedding-4B scores 72.9 (BB) on agent readiness against Qwen3-VL-Embedding-2B's 55.4 (C), and leads in 3 of 5 scored categories. Qwen3-VL-Embedding-2B leads on documentation and upkeep.
Other comparisons with Qwen3-Embedding-4B or Qwen3-VL-Embedding-2B
- EmbeddingGemma 300M vs Qwen3-Embedding-4B
- EmbeddingGemma 300M vs Qwen3-VL-Embedding-2B
- Granite Embedding Small English R2 vs Qwen3-Embedding-4B
- Granite Embedding Small English R2 vs Qwen3-VL-Embedding-2B
- jina-embeddings-v5-omni-small vs Qwen3-Embedding-4B
- jina-embeddings-v5-omni-small vs Qwen3-VL-Embedding-2B
- Qwen3-Embedding-0.6B vs Qwen3-Embedding-4B
- Qwen3-Embedding-0.6B vs Qwen3-VL-Embedding-2B
- Qwen3-Embedding-4B vs Qwen3-Embedding-8B
- Qwen3-Embedding-4B vs Qwen3-VL-Embedding-8B
- Qwen3-Embedding-8B vs Qwen3-VL-Embedding-2B
- Qwen3-VL-Embedding-2B vs Qwen3-VL-Embedding-8B
Machine-readable
- This page as Markdown
/compare/qwen3-embedding-4b-vs-qwen3-vl-embedding-2b.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/qwen3-embedding-4b.json·/api/v1/tools/qwen3-vl-embedding-2b.json - From a terminal
anchor compare qwen3-embedding-4b qwen3-vl-embedding-2b(the CLI) - Over MCP
compare_tools {"a": "qwen3-embedding-4b", "b": "qwen3-vl-embedding-2b"}at/mcp, no key