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

> EmbeddingGemma 2 scores 66.4 (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/embeddinggemma-2-vs-qwen3-vl-embedding-2b
- Markdown: https://www.anchorterminal.com/compare/embeddinggemma-2-vs-qwen3-vl-embedding-2b.md (~2,250 tokens)
- Slim: https://www.anchorterminal.com/compare/embeddinggemma-2-vs-qwen3-vl-embedding-2b.min.md (~530 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/embeddinggemma-2-vs-qwen3-vl-embedding-2b.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

EmbeddingGemma 2 scores 66.4 (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.

- 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-VL-Embedding-2B: grade C, 55.4/100, rank #88 of 117 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 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:
- Capability, 59 against 50
- Risk, 60 against 45
- Agent interface, 70 against 65
- Documentation and upkeep, 80 against 36

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

### 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 | EmbeddingGemma 2 | Qwen3-VL-Embedding-2B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 59 | 50 | EmbeddingGemma 2 +9 |
| Reach | 25% (25 this run) | 75 | 75 | even |
| Risk | 20% (20 this run) | 60 | 45 | EmbeddingGemma 2 +15 |
| Agent interface | 10% (10 this run) | 70 | 65 | EmbeddingGemma 2 +5 |
| Documentation and upkeep | 10% (10 this run) | 80 | 36 | EmbeddingGemma 2 +44 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **66.4 · B** | **55.4 · C** | |

## Facts side by side

| Fact | EmbeddingGemma 2 | Qwen3-VL-Embedding-2B |
| --- | --- | --- |
| 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 | 2026-04-16 |
| 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 (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-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

### 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-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, EmbeddingGemma 2 or Qwen3-VL-Embedding-2B?

EmbeddingGemma 2 scores 66.4 (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/embeddinggemma-2-vs-qwen3-vl-embedding-2b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/embeddinggemma-2-vs-qwen3-vl-embedding-2b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "embeddinggemma-2", "b": "qwen3-vl-embedding-2b"}`. From a terminal: `anchor compare embeddinggemma-2 qwen3-vl-embedding-2b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/embeddinggemma-2.json and https://www.anchorterminal.com/api/v1/tools/qwen3-vl-embedding-2b.json

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

- [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-Embedding-8B](https://www.anchorterminal.com/compare/embeddinggemma-2-vs-qwen3-embedding-8b.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-VL-Embedding-2B](https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-qwen3-vl-embedding-2b.md)
- [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)
- [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)
- [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-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)

---

Query this catalogue directly over MCP: https://www.anchorterminal.com/mcp (Streamable HTTP, no key). How: https://www.anchorterminal.com/agents/#mcp