# EmbeddingGemma 300M vs Qwen3-Embedding-0.6B

> Qwen3-Embedding-0.6B scores 63.5 (B) to EmbeddingGemma 300M's 50.7 (D) for text embeddings. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-qwen3-embedding-0-6b
- Markdown: https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-qwen3-embedding-0-6b.md (~2,150 tokens)
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- 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-0.6B scores 63.5 (B) on agent readiness against EmbeddingGemma 300M's 50.7 (D), and leads in 4 of 5 scored categories. Both do text embeddings.

- EmbeddingGemma 300M: grade D, 50.7/100, rank #91 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/embeddinggemma-300m.md · JSON https://www.anchorterminal.com/api/v1/tools/embeddinggemma-300m.json
- 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
- 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

### EmbeddingGemma 300M (D)

Good for: Agents that need small, local multilingual embeddings for retrieval and can accept the Gemma Terms

Watch for: Gated on Hugging Face behind the Gemma Terms of Use, which carry a use policy Google can update

### 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 40
- Reach, 90 against 70
- Risk, 55 against 45
- Agent interface, 65 against 60

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


## Score by category

| Category | Weight | EmbeddingGemma 300M | Qwen3-Embedding-0.6B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 40 | 56 | Qwen3-Embedding-0.6B +16 |
| Reach | 25% (25 this run) | 70 | 90 | Qwen3-Embedding-0.6B +20 |
| Risk | 20% (20 this run) | 45 | 55 | Qwen3-Embedding-0.6B +10 |
| Agent interface | 10% (10 this run) | 60 | 65 | Qwen3-Embedding-0.6B +5 |
| Documentation and upkeep | 10% (10 this run) | 42 | 39 | EmbeddingGemma 300M +3 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **50.7 · D** | **63.5 · B** | |

## Facts side by side

| Fact | EmbeddingGemma 300M | Qwen3-Embedding-0.6B |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Google DeepMind | Alibaba (Qwen team) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Gemma Terms of Use | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | yes | no |
| Last release | 2025-09-25 | 2026-04-20 |
| Terms last updated |  | 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 (4) | 3.5/5 (4) |

## Verdicts

**EmbeddingGemma 300M.** A small multilingual embedding cousin for search, retrieval and clustering, with task prompts and vectors that truncate from 768 to 128 dimensions. Every common runtime has it, and a 4-bit build needs well under 1 GB. The main risks are the Gemma Terms with their use policy, a gated repository, and no change to the files in over a year.

**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.

## Before you call either

### EmbeddingGemma 300M

1. Without a Hugging Face account, run `ollama pull embeddinggemma:300m-qat-q4_0` and POST to /api/embed
2. Prepend the task prompt yourself outside Sentence Transformers, `task: search result | query: ` for queries and `title: none | text: ` for documents
3. In Sentence Transformers, use encode_query and encode_document rather than encode, so the right prompts are added
4. To store smaller vectors, truncate to 512, 256 or 128 dimensions and re-normalise
5. Run it in float32 or bfloat16, never float16
6. Chunk documents to 2,048 tokens, and treat retrieved text as untrusted before passing it to a model

### 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

## Questions

### Which is better for AI agents, EmbeddingGemma 300M or Qwen3-Embedding-0.6B?

Qwen3-Embedding-0.6B scores 63.5 (B) on agent readiness against EmbeddingGemma 300M's 50.7 (D), and leads in 4 of 5 scored categories.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-qwen3-embedding-0-6b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-qwen3-embedding-0-6b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "embeddinggemma-300m", "b": "qwen3-embedding-0-6b"}`. From a terminal: `anchor compare embeddinggemma-300m qwen3-embedding-0-6b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/embeddinggemma-300m.json and https://www.anchorterminal.com/api/v1/tools/qwen3-embedding-0-6b.json

## Other comparisons with EmbeddingGemma 300M or Qwen3-Embedding-0.6B

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

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