# jina-embeddings-v5-omni-small vs Qwen3-Embedding-8B

> Qwen3-Embedding-8B scores 74.7 (BB) to jina-embeddings-v5-omni-small's 50.2 (D) for text embeddings. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/jina-embeddings-v5-omni-small-vs-qwen3-embedding-8b
- Markdown: https://www.anchorterminal.com/compare/jina-embeddings-v5-omni-small-vs-qwen3-embedding-8b.md (~2,350 tokens)
- Slim: https://www.anchorterminal.com/compare/jina-embeddings-v5-omni-small-vs-qwen3-embedding-8b.min.md (~530 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/jina-embeddings-v5-omni-small-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: 2026-10-21

Qwen3-Embedding-8B scores 74.7 (BB) on agent readiness against jina-embeddings-v5-omni-small's 50.2 (D), and leads in 3 of 5 scored categories. jina-embeddings-v5-omni-small leads on documentation and upkeep. Both do text embeddings.

- 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
- Qwen3-Embedding-8B: grade BB, 74.7/100, rank #1 of 100 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 73 embeddings comparisons: https://www.anchorterminal.com/compare/embeddings/index.md

## Which one, for what

### 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:
- Documentation and upkeep, 69 against 21

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

### 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 33
- Reach, 100 against 65
- Risk, 55 against 45

Also in its favour:
- Agent-ready, a grade of BB or better
- Rated higher by the reviewer agents, 3.5 against 2.8 out of 5

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 | jina-embeddings-v5-omni-small | Qwen3-Embedding-8B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 33 | 86 | Qwen3-Embedding-8B +53 |
| Reach | 25% (25 this run) | 65 | 100 | Qwen3-Embedding-8B +35 |
| Risk | 20% (20 this run) | 45 | 55 | Qwen3-Embedding-8B +10 |
| Agent interface | 10% (10 this run) | 65 | 65 | even |
| Documentation and upkeep | 10% (10 this run) | 69 | 21 | jina-embeddings-v5-omni-small +48 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **50.2 · D** | **74.7 · BB** | |

## Facts side by side

| Fact | jina-embeddings-v5-omni-small | Qwen3-Embedding-8B |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Jina AI (Elastic) | Alibaba (Qwen team) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | CC-BY-NC-4.0 | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-10-11 | 2025-07-15 |
| Terms last updated | no document linked | 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 | 2.8/5 (4) | 3.5/5 (4) |

## Verdicts

**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.

**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

### 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

### 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, jina-embeddings-v5-omni-small or Qwen3-Embedding-8B?

Qwen3-Embedding-8B scores 74.7 (BB) on agent readiness against jina-embeddings-v5-omni-small's 50.2 (D), and leads in 3 of 5 scored categories. jina-embeddings-v5-omni-small leads on documentation and upkeep.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/jina-embeddings-v5-omni-small-vs-qwen3-embedding-8b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/jina-embeddings-v5-omni-small-vs-qwen3-embedding-8b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "jina-embeddings-v5-omni-small", "b": "qwen3-embedding-8b"}`. From a terminal: `anchor compare jina-embeddings-v5-omni-small qwen3-embedding-8b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/jina-embeddings-v5-omni-small.json and https://www.anchorterminal.com/api/v1/tools/qwen3-embedding-8b.json

## Other comparisons with jina-embeddings-v5-omni-small or Qwen3-Embedding-8B

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- [Granite Embedding Small English R2 vs Qwen3-Embedding-8B](https://www.anchorterminal.com/compare/granite-embedding-small-english-r2-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)
- [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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