# EmbeddingGemma 300M vs Qwen3-VL-Embedding-8B

> Qwen3-VL-Embedding-8B scores 62.1 (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-vl-embedding-8b
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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-VL-Embedding-8B scores 62.1 (B) on agent readiness against EmbeddingGemma 300M's 50.7 (D), and leads in 2 of 5 scored categories. EmbeddingGemma 300M leads on reach. 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-VL-Embedding-8B: grade B, 62.1/100, rank #30 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-vl-embedding-8b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-vl-embedding-8b.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

Ahead on:
- Reach, 70 against 65

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

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

Good for: Agents with a 24 GB GPU that need local retrieval across text, images, screenshots, visual documents and video in one index.

Ahead on:
- Capability, 75 against 40
- Agent interface, 65 against 60

Watch for: Only a 16.3 GB BF16 build, about 20 GB of memory, with no GGUF, FP8 or Ollama build


## Score by category

| Category | Weight | EmbeddingGemma 300M | Qwen3-VL-Embedding-8B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 40 | 75 | Qwen3-VL-Embedding-8B +35 |
| Reach | 25% (25 this run) | 70 | 65 | EmbeddingGemma 300M +5 |
| Risk | 20% (20 this run) | 45 | 45 | even |
| Agent interface | 10% (10 this run) | 60 | 65 | Qwen3-VL-Embedding-8B +5 |
| Documentation and upkeep | 10% (10 this run) | 42 | 41 | EmbeddingGemma 300M +1 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **50.7 · D** | **62.1 · B** | |

## Facts side by side

| Fact | EmbeddingGemma 300M | Qwen3-VL-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 | Gemma Terms of Use | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | yes | no |
| Last release | 2025-09-25 | 2026-04-16 |
| 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 (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-VL-Embedding-8B.** A multimodal embedding cousin that puts text, images, screenshots and video in one vector space for visual document and video retrieval, under plain Apache 2.0. Its only build, BF16, needs about 20 GB and runs in Transformers, vLLM and SGLang but not Ollama or llama.cpp. The main risk is the publisher's script on the Transformers path.

## 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-VL-Embedding-8B

1. Simplest start is Sentence Transformers 5.4 or later, `SentenceTransformer('Qwen/Qwen3-VL-Embedding-8B')`, which applies the template and the default instruction and runs no repository code
2. To serve it, `vllm serve Qwen/Qwen3-VL-Embedding-8B --runner pooling`, then POST messages with a system instruction and image_url or text parts to /v1/embeddings; vLLM's docs say its video preprocessing differs slightly from Qwen's qwen_vl_utils
3. Put the task instruction in the system turn, and write it in English, as the card advises; the default is "Represent the user's input."
4. To shorten vectors, keep the first N of the 4,096 values and L2-normalise again; the card gives 64 as the floor, and the report says int8 vectors hold up while binary ones lose noticeably
5. For several images per input use the reference embedder in QwenLM/Qwen3-VL-Embedding's src, which is newer than the script in the Hugging Face repository
6. Keep inputs under 32K tokens; the reference embedder defaults to 8,192, 1,843,200 pixels per image and 64 frames per video
7. Treat retrieved screenshots and documents as untrusted, since text in an image can carry instructions to whatever model reads the results

## Questions

### Which is better for AI agents, EmbeddingGemma 300M or Qwen3-VL-Embedding-8B?

Qwen3-VL-Embedding-8B scores 62.1 (B) on agent readiness against EmbeddingGemma 300M's 50.7 (D), and leads in 2 of 5 scored categories. EmbeddingGemma 300M leads on reach.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-qwen3-vl-embedding-8b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-qwen3-vl-embedding-8b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "embeddinggemma-300m", "b": "qwen3-vl-embedding-8b"}`. From a terminal: `anchor compare embeddinggemma-300m qwen3-vl-embedding-8b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/embeddinggemma-300m.json and https://www.anchorterminal.com/api/v1/tools/qwen3-vl-embedding-8b.json

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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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- [jina-embeddings-v5-omni-small vs Qwen3-VL-Embedding-8B](https://www.anchorterminal.com/compare/jina-embeddings-v5-omni-small-vs-qwen3-vl-embedding-8b.md)
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- [Qwen3-Embedding-8B vs Qwen3-VL-Embedding-8B](https://www.anchorterminal.com/compare/qwen3-embedding-8b-vs-qwen3-vl-embedding-8b.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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