# Qwen3-Embedding-0.6B vs Qwen3-VL-Embedding-2B

> Qwen3-Embedding-0.6B scores 63.5 (B) to Qwen3-VL-Embedding-2B's 55.4 (C) for text embeddings. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/qwen3-embedding-0-6b-vs-qwen3-vl-embedding-2b
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- Site index for agents: https://www.anchorterminal.com/llms.txt (full text: https://www.anchorterminal.com/llms-full.txt)
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- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)
- Updated: 2026-10-21

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.

- Qwen3-Embedding-0.6B: grade B, 63.5/100, rank #23 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-embedding-0-6b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-embedding-0-6b.json
- Qwen3-VL-Embedding-2B: grade C, 55.4/100, rank #80 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-vl-embedding-2b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-vl-embedding-2b.json
- Best embedding and reranking APIs for AI agents: https://www.anchorterminal.com/best/embeddings/index.md
- All 73 embeddings comparisons: https://www.anchorterminal.com/compare/embeddings/index.md

## Which one, for what

### Qwen3-Embedding-0.6B (B)

Good 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

### Qwen3-VL-Embedding-2B (C)

Good for: Agents that need local multimodal search over text, screenshots, images and video on a small GPU or CPU

Watch for: No quantised build from Alibaba and nothing in Ollama's or LM Studio's own libraries


## Score by category

| Category | Weight | Qwen3-Embedding-0.6B | Qwen3-VL-Embedding-2B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 56 | 50 | Qwen3-Embedding-0.6B +6 |
| Reach | 25% (25 this run) | 90 | 75 | Qwen3-Embedding-0.6B +15 |
| Risk | 20% (20 this run) | 55 | 45 | Qwen3-Embedding-0.6B +10 |
| Agent interface | 10% (10 this run) | 65 | 65 | even |
| Documentation and upkeep | 10% (10 this run) | 39 | 36 | Qwen3-Embedding-0.6B +3 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **63.5 · B** | **55.4 · C** | |

## 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

1. Start it with `ollama pull qwen3-embedding:0.6b`, then POST to /api/embed with the text in `input`
2. With llama.cpp, run `llama-server -hf Qwen/Qwen3-Embedding-0.6B-GGUF:Q8_0 --embedding --pooling last -ub 8192`; without `--pooling last` the vectors are wrong
3. Prefix queries with `Instruct: <one-sentence task>\nQuery:` and send documents as they are; write the instruction in English
4. In Transformers, use left padding, take the last token's hidden state and L2-normalise it, as in the card's last_token_pool
5. In Sentence Transformers, pass `prompt_name="query"` for queries; the prompt is stored in config_sentence_transformers.json
6. If you shorten vectors below 1,024, test retrieval quality first, since the card doesn't say how it expects them cut
7. Retrieved documents are untrusted text; embedding them doesn't remove instructions an attacker put in them

### Qwen3-VL-Embedding-2B

1. Start it with `vllm serve Qwen/Qwen3-VL-Embedding-2B --runner pooling --max-model-len 8192`, then POST chat-style `messages` with the instruction in the system turn to /v1/embeddings
2. Or `pip install -U sentence-transformers` (5.4 or later) and `SentenceTransformer('Qwen/Qwen3-VL-Embedding-2B').encode(texts, prompt='<task instruction>')`; images go in as URLs, paths or dicts with text and image
3. Write the task instruction in English, even for other languages, and embed queries and documents with matching instructions
4. To shorten vectors, cut the first N of 2,048 dimensions and renormalise yourself; no parameter for it is documented
5. Don't mix vectors from vLLM and the reference qwen_vl_utils path in one index, since vLLM's docs say image preprocessing differs
6. 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.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/qwen3-embedding-0-6b-vs-qwen3-vl-embedding-2b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/qwen3-embedding-0-6b-vs-qwen3-vl-embedding-2b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "qwen3-embedding-0-6b", "b": "qwen3-vl-embedding-2b"}`. From a terminal: `anchor compare qwen3-embedding-0-6b qwen3-vl-embedding-2b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/qwen3-embedding-0-6b.json and https://www.anchorterminal.com/api/v1/tools/qwen3-vl-embedding-2b.json

## Other comparisons with Qwen3-Embedding-0.6B or Qwen3-VL-Embedding-2B

- [EmbeddingGemma 300M vs Qwen3-Embedding-0.6B](https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-qwen3-embedding-0-6b.md)
- [EmbeddingGemma 300M vs Qwen3-VL-Embedding-2B](https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-qwen3-vl-embedding-2b.md)
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- [Granite Embedding Small English R2 vs Qwen3-VL-Embedding-2B](https://www.anchorterminal.com/compare/granite-embedding-small-english-r2-vs-qwen3-vl-embedding-2b.md)
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- [jina-embeddings-v5-omni-small vs Qwen3-VL-Embedding-2B](https://www.anchorterminal.com/compare/jina-embeddings-v5-omni-small-vs-qwen3-vl-embedding-2b.md)
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- [Qwen3-Embedding-0.6B vs Qwen3-VL-Embedding-8B](https://www.anchorterminal.com/compare/qwen3-embedding-0-6b-vs-qwen3-vl-embedding-8b.md)
- [Qwen3-Embedding-4B vs Qwen3-VL-Embedding-2B](https://www.anchorterminal.com/compare/qwen3-embedding-4b-vs-qwen3-vl-embedding-2b.md)
- [Qwen3-Embedding-8B vs Qwen3-VL-Embedding-2B](https://www.anchorterminal.com/compare/qwen3-embedding-8b-vs-qwen3-vl-embedding-2b.md)
- [Qwen3-VL-Embedding-2B vs Qwen3-VL-Embedding-8B](https://www.anchorterminal.com/compare/qwen3-vl-embedding-2b-vs-qwen3-vl-embedding-8b.md)

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