# Qwen3-Embedding-0.6B vs Qwen3-Embedding-4B

> Qwen3-Embedding-4B scores 72.9 (BB) to Qwen3-Embedding-0.6B's 63.5 (B) 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-embedding-4b
- Markdown: https://www.anchorterminal.com/compare/qwen3-embedding-0-6b-vs-qwen3-embedding-4b.md (~2,200 tokens)
- Slim: https://www.anchorterminal.com/compare/qwen3-embedding-0-6b-vs-qwen3-embedding-4b.min.md (~430 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/qwen3-embedding-0-6b-vs-qwen3-embedding-4b.json (this page as data, same URL with Accept: application/json)
- Site index for agents: https://www.anchorterminal.com/llms.txt (full text: https://www.anchorterminal.com/llms-full.txt)
- API: https://www.anchorterminal.com/api/v1/index.json
- MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key)
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)
- Updated: 2026-10-21

Qwen3-Embedding-4B scores 72.9 (BB) on agent readiness against Qwen3-Embedding-0.6B's 63.5 (B), and leads in 2 of 5 scored categories. Qwen3-Embedding-0.6B leads on documentation and upkeep. 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-Embedding-4B: grade BB, 72.9/100, rank #3 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-embedding-4b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-embedding-4b.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:
- Documentation and upkeep, 39 against 21

Watch for: Queries need the right task instruction, and the card says leaving it out costs 1% to 5% in retrieval

### Qwen3-Embedding-4B (BB)

Good 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 56
- Reach, 100 against 90

Also in its favour:
- Agent-ready, a grade of BB or better

Watch for: No change to the files or reference code since August 2025, and no reply from Qwen to any of its 16 questions


## Score by category

| Category | Weight | Qwen3-Embedding-0.6B | Qwen3-Embedding-4B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 56 | 81 | Qwen3-Embedding-4B +25 |
| Reach | 25% (25 this run) | 90 | 100 | Qwen3-Embedding-4B +10 |
| Risk | 20% (20 this run) | 55 | 55 | even |
| Agent interface | 10% (10 this run) | 65 | 65 | even |
| Documentation and upkeep | 10% (10 this run) | 39 | 21 | Qwen3-Embedding-0.6B +18 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **63.5 · B** | **72.9 · BB** | |

## Facts side by side

| Fact | Qwen3-Embedding-0.6B | Qwen3-Embedding-4B |
| --- | --- | --- |
| 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 | 2025-08-03 |
| 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) | 3.5/5 (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-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.

## 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-Embedding-4B

1. Quickest start is `ollama pull qwen3-embedding:4b`, about 2.5 GB, then POST to /api/embed
2. 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
3. Prefix each query with `Instruct: <one-sentence task>\nQuery:` and embed documents with no prefix, in English even for other languages
4. 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
5. 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
6. Treat retrieved documents as untrusted text before passing them to a model

## Questions

### Which is better for AI agents, Qwen3-Embedding-0.6B or Qwen3-Embedding-4B?

Qwen3-Embedding-4B scores 72.9 (BB) on agent readiness against Qwen3-Embedding-0.6B's 63.5 (B), and leads in 2 of 5 scored categories. Qwen3-Embedding-0.6B leads on documentation and upkeep.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/qwen3-embedding-0-6b-vs-qwen3-embedding-4b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/qwen3-embedding-0-6b-vs-qwen3-embedding-4b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "qwen3-embedding-0-6b", "b": "qwen3-embedding-4b"}`. From a terminal: `anchor compare qwen3-embedding-0-6b qwen3-embedding-4b`
- 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-embedding-4b.json

## Other comparisons with Qwen3-Embedding-0.6B or Qwen3-Embedding-4B

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

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