# EmbeddingGemma 2 vs jina-embeddings-v5-omni-small

> EmbeddingGemma 2 scores 66.4 (B) to jina-embeddings-v5-omni-small's 50.2 (D) for text embeddings. Prices, MCP, x402, uptime and agent notes side by side.

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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
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- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)
- Updated: 2026-10-30

EmbeddingGemma 2 scores 66.4 (B) on agent readiness against jina-embeddings-v5-omni-small's 50.2 (D), and leads in every scored category. 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
- jina-embeddings-v5-omni-small: grade D, 50.2/100, rank #108 of 117 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 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 33
- Reach, 75 against 65
- Risk, 60 against 45
- Agent interface, 70 against 65
- Documentation and upkeep, 80 against 69

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

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


## Score by category

| Category | Weight | EmbeddingGemma 2 | jina-embeddings-v5-omni-small | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 59 | 33 | EmbeddingGemma 2 +26 |
| Reach | 25% (25 this run) | 75 | 65 | EmbeddingGemma 2 +10 |
| 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 | 69 | EmbeddingGemma 2 +11 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **66.4 · B** | **50.2 · D** | |

## Facts side by side

| Fact | EmbeddingGemma 2 | 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 | Apache 2.0 | CC-BY-NC-4.0 |
| Read-only variant documented | no | no |
| llms.txt | yes | no |
| Last release | 2026-10-28 | 2026-10-11 |
| 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 | 2.8/5 (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.

**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 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

### 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 2 or jina-embeddings-v5-omni-small?

EmbeddingGemma 2 scores 66.4 (B) on agent readiness against jina-embeddings-v5-omni-small's 50.2 (D), and leads in every scored category.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/embeddinggemma-2-vs-jina-embeddings-v5-omni-small.json, and with the fewest tokens: https://www.anchorterminal.com/compare/embeddinggemma-2-vs-jina-embeddings-v5-omni-small.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "embeddinggemma-2", "b": "jina-embeddings-v5-omni-small"}`. From a terminal: `anchor compare embeddinggemma-2 jina-embeddings-v5-omni-small`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/embeddinggemma-2.json and https://www.anchorterminal.com/api/v1/tools/jina-embeddings-v5-omni-small.json

## Other comparisons with EmbeddingGemma 2 or jina-embeddings-v5-omni-small

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