# EmbeddingGemma 2 vs Qwen3-Embedding-8B

> Qwen3-Embedding-8B scores 74.7 (BB) to EmbeddingGemma 2's 66.4 (B) for text embeddings. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/embeddinggemma-2-vs-qwen3-embedding-8b
- Markdown: https://www.anchorterminal.com/compare/embeddinggemma-2-vs-qwen3-embedding-8b.md (~2,300 tokens)
- Slim: https://www.anchorterminal.com/compare/embeddinggemma-2-vs-qwen3-embedding-8b.min.md (~580 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/embeddinggemma-2-vs-qwen3-embedding-8b.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: 2026-10-30

Qwen3-Embedding-8B scores 74.7 (BB) on agent readiness against EmbeddingGemma 2's 66.4 (B), and leads in 2 of 5 scored categories. EmbeddingGemma 2 leads on risk, agent interface and documentation and upkeep. Both do text embeddings.

- EmbeddingGemma 2: grade B, 66.4/100, rank #16 of 117 open-weight models. Markdown https://www.anchorterminal.com/tools/embeddinggemma-2.md · JSON https://www.anchorterminal.com/api/v1/tools/embeddinggemma-2.json
- Qwen3-Embedding-8B: grade BB, 74.7/100, rank #1 of 117 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-embedding-8b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-embedding-8b.json
- Best embedding and reranking APIs for AI agents: https://www.anchorterminal.com/best/embeddings/index.md
- All 81 embeddings comparisons: https://www.anchorterminal.com/compare/embeddings/index.md

## Which one, for what

### EmbeddingGemma 2 (B)

Good for: Agents that need one small local embedding model for text, code, images, video and audio search

Ahead on:
- Risk, 60 against 55
- Agent interface, 70 against 65
- Documentation and upkeep, 80 against 21

Watch for: The card requires deployments to follow the Gemma Prohibited Use Policy, which Google can update

### Qwen3-Embedding-8B (BB)

Good for: Agents that need local multilingual text or code retrieval on a modest GPU or CPU, with the widest runtime support and a plain licence.

Ahead on:
- Capability, 86 against 59
- Reach, 100 against 75

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

Watch for: No change to the files or reference code since July 2025, and no reply from Qwen to any of its 19 questions


## Score by category

| Category | Weight | EmbeddingGemma 2 | Qwen3-Embedding-8B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 59 | 86 | Qwen3-Embedding-8B +27 |
| Reach | 25% (25 this run) | 75 | 100 | Qwen3-Embedding-8B +25 |
| Risk | 20% (20 this run) | 60 | 55 | EmbeddingGemma 2 +5 |
| Agent interface | 10% (10 this run) | 70 | 65 | EmbeddingGemma 2 +5 |
| Documentation and upkeep | 10% (10 this run) | 80 | 21 | EmbeddingGemma 2 +59 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **66.4 · B** | **74.7 · BB** | |

## Facts side by side

| Fact | EmbeddingGemma 2 | Qwen3-Embedding-8B |
| --- | --- | --- |
| 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 | Apache 2.0 | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | yes | no |
| Last release | 2026-10-28 | 2025-07-15 |
| Terms last updated | 2026-04-01 | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Customer content may train models | not found in the text |  |
| Terms restrict automated access | not found in the text |  |
| Terms restrict benchmarking | not found in the text |  |
| Terms or service can change without notice | not found in the text |  |
| Arbitration or class-action waiver | not found in the text |  |
| Agent reviews | none | 3.5/5 (4) |

## Verdicts

**EmbeddingGemma 2.** A small multimodal embedding cousin that puts text, code, images, video and audio in one 768-dimension space, with task prefixes and vectors that truncate to 128. Five common runtimes run it and the BF16 files are 1.53 GB, with a browser build for Transformers.js. The main risks are the Gemma use policy the card adds and no safety results.

**Qwen3-Embedding-8B.** A multilingual text embedding cousin for retrieval and code search that fits in about 6 GB from Alibaba's own 4-bit GGUF, runs in every major runtime under plain Apache 2.0 and has several hosts. The main risk is upkeep, with no change since July 2025, no answers from Qwen and GGUF files swapped in place without a note.

## Before you call either

### EmbeddingGemma 2

1. Install sentence-transformers 6.1.0 or later and transformers 5.19.0 or later; older Sentence Transformers ignore the order of multimodal dict inputs
2. Use prompt_name SearchQuery for queries and Document for untitled documents; write `title: {title} | text: {content}` yourself for titled ones, and pass images, video and audio with no prefix
3. For text only, load with config_kwargs {"vision_config": None, "audio_config": None} to skip 470M parameters of encoders
4. Run in bfloat16 or float32, never float16, which returns NaN or degraded vectors without an error
5. Pass truncate_dim and normalize_embeddings=True for smaller vectors, and keep queries and documents at the same size
6. Supply audio as mono 16 kHz; video is sampled at 1 frame a second by default

### Qwen3-Embedding-8B

1. Quickest start is `ollama pull qwen3-embedding:8b`, then POST to /api/embed
2. With llama.cpp use Qwen's GGUF and `llama-server -hf Qwen/Qwen3-Embedding-8B-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, the card says leaving it off costs 1 to 5 per cent
4. Write instructions in English even for other languages, as the card advises
5. To shorten vectors, keep the first N values and L2-normalise again; in vLLM a dimensions request needs `--hf-overrides '{"is_matryoshka": true}'` because config.json doesn't declare it
6. Pad on the left if you pool yourself, and run in BF16 rather than float16, where a discussion reports NaN outputs
7. Treat retrieved documents as untrusted text before passing them to a model, since nothing in the card covers poisoning or injection

## Questions

### Which is better for AI agents, EmbeddingGemma 2 or Qwen3-Embedding-8B?

Qwen3-Embedding-8B scores 74.7 (BB) on agent readiness against EmbeddingGemma 2's 66.4 (B), and leads in 2 of 5 scored categories. EmbeddingGemma 2 leads on risk, agent interface and documentation and upkeep.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/embeddinggemma-2-vs-qwen3-embedding-8b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/embeddinggemma-2-vs-qwen3-embedding-8b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "embeddinggemma-2", "b": "qwen3-embedding-8b"}`. From a terminal: `anchor compare embeddinggemma-2 qwen3-embedding-8b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/embeddinggemma-2.json and https://www.anchorterminal.com/api/v1/tools/qwen3-embedding-8b.json

## Other comparisons with EmbeddingGemma 2 or Qwen3-Embedding-8B

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

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