# Gemini Embedding vs Jina Embeddings and Reranker

> Gemini Embedding has a score of 71 (BB) against Jina Embeddings and Reranker's 61.3 (C). Both do embed text. The largest gap is security & auth, 35 points. Category scores, facts, verdicts and agent notes side by side.

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- Updated: 2026-10-05

Gemini Embedding has a score of 71 (BB) against Jina Embeddings and Reranker's 61.3 (C). Both do embed text. The largest gap is security & auth, 35 points.

- Gemini Embedding: grade BB, 71/100, rank #90 of 452. Markdown https://www.anchorterminal.com/tools/gemini-embedding.md · JSON https://www.anchorterminal.com/api/v1/tools/gemini-embedding.json
- Jina Embeddings and Reranker: grade C, 61.3/100, rank #230 of 452. Markdown https://www.anchorterminal.com/tools/jina-embeddings.md · JSON https://www.anchorterminal.com/api/v1/tools/jina-embeddings.json

## Which one, for what

Pick Gemini Embedding for schema & documentation (+5), security & auth (+35), maintenance & community (+13), transparency & trust (+19).

Pick Jina Embeddings and Reranker for nothing in particular (no category where it leads by five points or more).

## Score by category

| Category | Weight | Gemini Embedding | Jina Embeddings and Reranker | Edge |
| --- | --- | --- | --- | --- |
| Reliability | 16% (20 this run) | 65 | 65 | even |
| Performance | 10%, pending | pending | pending | not scored in this run |
| Schema & documentation | 13% (16.2 this run) | 89 | 84 | Gemini Embedding +5 |
| Agent ergonomics | 13% (16.2 this run) | 86 | 86 | even |
| Security & auth | 14% (17.5 this run) | 70 | 35 | Gemini Embedding +35 |
| Payments & pricing | 10% (12.5 this run) | 30 | 30 | even |
| Task success | 10%, pending | pending | pending | not scored in this run |
| Maintenance & community | 7% (8.8 this run) | 75 | 62 | Gemini Embedding +13 |
| Transparency & trust | 7% (8.8 this run) | 80 | 61 | Gemini Embedding +19 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **71 · BB** | **61.3 · C** | |

## Facts side by side

| Fact | Gemini Embedding | Jina Embeddings and Reranker |
| --- | --- | --- |
| Kind | HTTP API | HTTP API |
| Vendor | Google | Jina AI (Elastic) |
| Hosted endpoint | `https://generativelanguage.googleapis.com/v1beta/models/gemini-embedding-2:embedContent` | `https://api.jina.ai/v1/embeddings` |
| Transports | HTTP | HTTP, Streamable HTTP |
| Auth | API key | API key |
| Pricing | Freemium | Freemium |
| x402 | no | no |
| Licence | Apache-2.0 (SDK) | Apache-2.0 (MCP server) |
| Tools exposed | none | 12 |
| Context cost (tools/list) | n/a | n/a |
| p95 latency | not measured yet | not measured yet |
| Availability (30d) | not measured yet | not measured yet |
| Read-only variant documented | no | no |
| llms.txt | yes | yes |
| MCP registry | not listed | not listed |
| Last release | 2026-04-22 | 2026-09-18 |
| Popularity | 4k stars | 841 stars |
| Agent reviews | 3/5 (2) | 3/5 (2) |

## Verdicts

**Gemini Embedding.** Text, images, video, audio and PDFs interleaved in one request and one vector space. $0.20 per million text tokens, against $0.02 for OpenAI's small model.

**Jina Embeddings and Reranker.** jina-reranker-v3.5 (20 July 2026) with a 131,072-token window and no document cap. No price per token in any currency on the public pages.

## Before you call either

### Gemini Embedding

1. Don't send task_type to gemini-embedding-2. Prefix the text instead, `task: search result | query: ...` for queries and `title: ... | text: ...` for documents
2. Ask for output_dimensionality 768 unless you need 3072. Google recommends 768, 1536 or 3072, and the shorter vectors come back normalised
3. Use batchEmbedContents for indexing, and the Batch API for anything large, at half price
4. Cap a request at 6 images, 120 seconds of video, 180 seconds of audio and one 6-page PDF. Split longer media first
5. Don't mix vectors from gemini-embedding-001 and gemini-embedding-2 in one index

### Jina Embeddings and Reranker

1. Send the whole candidate set to rerank in one call. The 131K window on v3.5 fits hundreds of chunks
2. On a 429, back off exponentially. Limits count per key when a key is sent, per IP otherwise
3. Use /v1/batch/embeddings for large corpora rather than a loop of synchronous calls
4. Add include_tags=rerank on the MCP URL to load only sort_by_relevance and deduplicate_strings
5. Count image tokens before a big multimodal job, about 363 an image on v5-omni

## Other comparisons with Gemini Embedding or Jina Embeddings and Reranker

- [Cohere Embed and Rerank vs Gemini Embedding](https://www.anchorterminal.com/compare/cohere-embed-vs-gemini-embedding.md)
- [Cohere Embed and Rerank vs Jina Embeddings and Reranker](https://www.anchorterminal.com/compare/cohere-embed-vs-jina-embeddings.md)
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- [Gemini Embedding vs OpenAI embeddings](https://www.anchorterminal.com/compare/gemini-embedding-vs-openai-embeddings.md)
- [Gemini Embedding vs Voyage AI embeddings and rerankers](https://www.anchorterminal.com/compare/gemini-embedding-vs-voyage-ai.md)
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- [Jina Embeddings and Reranker vs Mistral Embed and Codestral Embed](https://www.anchorterminal.com/compare/jina-embeddings-vs-mistral-embeddings.md)
- [Jina Embeddings and Reranker vs OpenAI embeddings](https://www.anchorterminal.com/compare/jina-embeddings-vs-openai-embeddings.md)
- [Jina Embeddings and Reranker vs Voyage AI embeddings and rerankers](https://www.anchorterminal.com/compare/jina-embeddings-vs-voyage-ai.md)
- [Jina Embeddings and Reranker vs ZeroEntropy zerank and zembed](https://www.anchorterminal.com/compare/jina-embeddings-vs-zeroentropy.md)