# EmbeddingGemma 300M vs jina-embeddings-v5-omni-small

> EmbeddingGemma 300M and jina-embeddings-v5-omni-small score within a point of each other for text embeddings, 50.7 and 50.2 out of 100. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-jina-embeddings-v5-omni-small
- Markdown: https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-jina-embeddings-v5-omni-small.md (~2,250 tokens)
- Slim: https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-jina-embeddings-v5-omni-small.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-jina-embeddings-v5-omni-small.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

EmbeddingGemma 300M and jina-embeddings-v5-omni-small score within a point of each other on agent readiness, 50.7 (D) and 50.2 (D). jina-embeddings-v5-omni-small leads on agent interface and documentation and upkeep. 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
- jina-embeddings-v5-omni-small: grade D, 50.2/100, rank #93 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/jina-embeddings-v5-omni-small.md · JSON https://www.anchorterminal.com/api/v1/tools/jina-embeddings-v5-omni-small.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:
- Capability, 40 against 33
- Reach, 70 against 65

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

### jina-embeddings-v5-omni-small (D)

Good for: Non-commercial agents that want one local index across text, images, video and audio, or to extend a jina-embeddings-v5-text index

Ahead on:
- Agent interface, 65 against 60
- Documentation and upkeep, 69 against 42

Watch for: CC BY-NC 4.0, so commercial use needs a separate licence from Jina


## Score by category

| Category | Weight | EmbeddingGemma 300M | jina-embeddings-v5-omni-small | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 40 | 33 | EmbeddingGemma 300M +7 |
| 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 | jina-embeddings-v5-omni-small +5 |
| Documentation and upkeep | 10% (10 this run) | 42 | 69 | jina-embeddings-v5-omni-small +27 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **50.7 · D** | **50.2 · D** | |

## Facts side by side

| Fact | EmbeddingGemma 300M | jina-embeddings-v5-omni-small |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Google DeepMind | Jina AI (Elastic) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Gemma Terms of Use | CC-BY-NC-4.0 |
| Read-only variant documented | no | no |
| llms.txt | yes | no |
| Last release | 2025-09-25 | 2026-10-11 |
| 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) | 2.8/5 (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.

**jina-embeddings-v5-omni-small.** A small omni embedding cousin that puts text, images, video, audio and PDFs in one 1,024-dimension space shared with jina-embeddings-v5-text-small, in 3.4 GB of BF16 files or a 0.4 GB GGUF. It's for non-commercial use only under CC BY-NC 4.0, and every way to run it executes Jina's own code or a llama.cpp fork rather than a released runtime's.

## 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

### jina-embeddings-v5-omni-small

1. Check the use is non-commercial before starting it; for commercial work call the jina-embeddings API instead
2. Start it with `pip install vllm==0.20.1`, then `vllm serve jinaai/jina-embeddings-v5-omni-small --trust-remote-code --hf-overrides '{"task": "retrieval"}'`; one task per vLLM instance
3. Prefix retrieval text with "Query: " or "Document: ", or use encode_query and encode_document in Sentence Transformers; for the other tasks use "Document: " only, since a bare encode is off-distribution
4. Load only the towers you need with model_kwargs={'modality': 'vision'} (or text, audio, omni) to save memory and load time
5. Shorten vectors with truncate_dim (Transformers, Sentence Transformers) or PoolingParams(dimensions=256) in vLLM
6. Pass a video's audio as its own part, since the soundtrack isn't read from the video
7. Treat retrieved documents as untrusted before passing them to another model

## Questions

### Which is better for AI agents, EmbeddingGemma 300M or jina-embeddings-v5-omni-small?

EmbeddingGemma 300M and jina-embeddings-v5-omni-small score within a point of each other on agent readiness, 50.7 (D) and 50.2 (D). jina-embeddings-v5-omni-small leads on agent interface and documentation and upkeep.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-jina-embeddings-v5-omni-small.json, and with the fewest tokens: https://www.anchorterminal.com/compare/embeddinggemma-300m-vs-jina-embeddings-v5-omni-small.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "embeddinggemma-300m", "b": "jina-embeddings-v5-omni-small"}`. From a terminal: `anchor compare embeddinggemma-300m jina-embeddings-v5-omni-small`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/embeddinggemma-300m.json and https://www.anchorterminal.com/api/v1/tools/jina-embeddings-v5-omni-small.json

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