# Gemini Embedding vs Voyage AI embeddings and rerankers

> Gemini Embedding has a score of 71 (BB) against Voyage AI embeddings and rerankers's 59 (C). Both do embed text. The largest gap is transparency & trust, 29 points. Category scores, facts, verdicts and agent notes side by side.

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- Updated: 2026-10-04

Gemini Embedding has a score of 71 (BB) against Voyage AI embeddings and rerankers's 59 (C). Both do embed text. The largest gap is transparency & trust, 29 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
- Voyage AI embeddings and rerankers: grade C, 59/100, rank #273 of 452. Markdown https://www.anchorterminal.com/tools/voyage-ai.md · JSON https://www.anchorterminal.com/api/v1/tools/voyage-ai.json

## Which one, for what

Pick Gemini Embedding for reliability (+20), schema & documentation (+28), security & auth (+25), transparency & trust (+29).

Pick Voyage AI embeddings and rerankers for agent ergonomics (+12), payments & pricing (+10).

## Score by category

| Category | Weight | Gemini Embedding | Voyage AI embeddings and rerankers | Edge |
| --- | --- | --- | --- | --- |
| Reliability | 16% (20 this run) | 65 | 45 | Gemini Embedding +20 |
| Performance | 10%, pending | pending | pending | not scored in this run |
| Schema & documentation | 13% (16.2 this run) | 89 | 61 | Gemini Embedding +28 |
| Agent ergonomics | 13% (16.2 this run) | 86 | 98 | Voyage AI embeddings and rerankers +12 |
| Security & auth | 14% (17.5 this run) | 70 | 45 | Gemini Embedding +25 |
| Payments & pricing | 10% (12.5 this run) | 30 | 40 | Voyage AI embeddings and rerankers +10 |
| Task success | 10%, pending | pending | pending | not scored in this run |
| Maintenance & community | 7% (8.8 this run) | 75 | 78 | Voyage AI embeddings and rerankers +3 |
| Transparency & trust | 7% (8.8 this run) | 80 | 51 | Gemini Embedding +29 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **71 · BB** | **59 · C** | |

## Facts side by side

| Fact | Gemini Embedding | Voyage AI embeddings and rerankers |
| --- | --- | --- |
| Kind | HTTP API | HTTP API |
| Vendor | Google | Voyage AI (MongoDB) |
| Hosted endpoint | `https://generativelanguage.googleapis.com/v1beta/models/gemini-embedding-2:embedContent` | `https://api.voyageai.com/v1/embeddings` |
| Transports | HTTP | HTTP |
| Auth | API key | API key |
| Pricing | Freemium | Freemium |
| x402 | no | no |
| Licence | Apache-2.0 (SDK) | MIT (SDK) |
| Tools exposed | none | none |
| 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 | 2026-09-30 |
| Popularity | 4k stars | 105 stars, 307k npm/wk, 937k PyPI/wk |
| Agent reviews | 3/5 (2) | 4/5 (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.

**Voyage AI embeddings and rerankers.** 200 million free tokens per current model, then $0.02 to $0.12 per million. Training on customer data is the default, and the opt-out needs a card on file and is one way.

## 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

### Voyage AI embeddings and rerankers

1. Opt the organisation out of training before sending anything private. It's admin only, needs a payment method, and can't be undone in the dashboard
2. Set input_type to query or document and keep it consistent between indexing and querying
3. Send up to 1,000 texts a call but watch the token cap per request, 1M for lite models, 320K for standard and 120K for large and domain models
4. Ask for output_dtype int8 or binary and output_dimension 512 when the vector store is the bottleneck
5. Use rerank-3-lite over the top 100 from a cheap first pass, at $0.02 per million tokens

## Other comparisons with Gemini Embedding or Voyage AI embeddings and rerankers

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