Head to head · Text embeddings · October 2026 research run
Qwen3-Embedding-0.6B vs Qwen3-VL-Embedding-2B
Qwen3-Embedding-0.6B scores 63.5 (B) on agent readiness against Qwen3-VL-Embedding-2B's 55.4 (C), and leads in 4 of 5 scored categories. 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 or code retrieval embeddings on any hardware, CPU included
Ahead on
- Capability, 56 against 50
- Reach, 90 against 75
- Risk, 55 against 45
Also in its favour
- Rated higher by the reviewer agents, 3.5 against 3.0 out of 5
Watch for
Queries need the right task instruction, and the card says leaving it out costs 1% to 5% in retrieval
Best for Agents that need local multimodal search over text, screenshots, images and video on a small GPU or CPU
No category where it leads by five points or more, and no fact that sets it apart.
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-0.6B | Qwen3-VL-Embedding-2B | Edge |
|---|---|---|---|---|
| Capability | 35%35 | 56 | 50 | Qwen3-Embedding-0.6B +6 |
| Reach | 25%25 | 90 | 75 | Qwen3-Embedding-0.6B +15 |
| Risk | 20%20 | 55 | 45 | Qwen3-Embedding-0.6B +10 |
| Agent interface | 10%10 | 65 | 65 | even |
| Documentation and upkeep | 10%10 | 39 | 36 | Qwen3-Embedding-0.6B +3 |
| Negative events | ≤15 | 0 | 0 | |
| Total | B 63.5/100 | C 55.4/100 |
Facts side by side
| Fact | Qwen3-Embedding-0.6B | 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 | 2026-04-20 | 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-0.6B
A tiny local embedding cousin for multilingual and code retrieval, under plain Apache 2.0, that fits in under 1 GB at Q8_0 and runs in llama.cpp, Ollama, vLLM, SGLang and Transformers. Queries need a task instruction and last-token pooling. The main risks are slow upkeep since mid 2025, few publisher replies and no safety results.
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-0.6B
- Start it with
ollama pull qwen3-embedding:0.6b, then POST to /api/embed with the text ininput - With llama.cpp, run
llama-server -hf Qwen/Qwen3-Embedding-0.6B-GGUF:Q8_0 --embedding --pooling last -ub 8192; without--pooling lastthe vectors are wrong - Prefix queries with
Instruct: <one-sentence task>\nQuery:and send documents as they are; write the instruction in English - In Transformers, use left padding, take the last token's hidden state and L2-normalise it, as in the card's last_token_pool
- In Sentence Transformers, pass
prompt_name="query"for queries; the prompt is stored in config_sentence_transformers.json - If you shorten vectors below 1,024, test retrieval quality first, since the card doesn't say how it expects them cut
- Retrieved documents are untrusted text; embedding them doesn't remove instructions an attacker put in them
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-0.6B or Qwen3-VL-Embedding-2B?
Qwen3-Embedding-0.6B scores 63.5 (B) on agent readiness against Qwen3-VL-Embedding-2B's 55.4 (C), and leads in 4 of 5 scored categories.
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- jina-embeddings-v5-omni-small vs Qwen3-Embedding-0.6B
- jina-embeddings-v5-omni-small vs Qwen3-VL-Embedding-2B
- Qwen3-Embedding-0.6B vs Qwen3-Embedding-4B
- Qwen3-Embedding-0.6B vs Qwen3-Embedding-8B
- Qwen3-Embedding-0.6B vs Qwen3-VL-Embedding-8B
- Qwen3-Embedding-4B vs Qwen3-VL-Embedding-2B
- 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-0-6b-vs-qwen3-vl-embedding-2b.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/qwen3-embedding-0-6b.json·/api/v1/tools/qwen3-vl-embedding-2b.json - From a terminal
anchor compare qwen3-embedding-0-6b qwen3-vl-embedding-2b(the CLI) - Over MCP
compare_tools {"a": "qwen3-embedding-0-6b", "b": "qwen3-vl-embedding-2b"}at/mcp, no key