Head to head · Embed text · October 2026 research run
Cohere Embed and Rerank vs Nomic Embed
Cohere Embed and Rerank scores 72.5 (BB) on agent readiness against Nomic Embed's 49.2 (D), and leads in 6 of 7 scored categories. Nomic Embed leads on security & auth. Both do embed text.
Which one, for what
Good for Best when reranking is the job, or for long multilingual documents and image-heavy material where a 128K embedding context helps, with a cheaper Fast model for queries against a Pro index.
Ahead on
- Reliability, 73 against 38
- Schema & documentation, 92 against 65
- Agent ergonomics, 87 against 69
- Payments & pricing, 40 against 20
- Maintenance & community, 90 against 28
- Transparency & trust, 72 against 46
Also in its favour
- Agent-ready, a grade of BB or better
- Free to start without a card
Watch for
Terms, training notice and security page disagree on whether API data trains models or goes to third parties
Good for Teams that want a hosted endpoint for an open-weight model they can also run themselves, with the same vectors either way.
Ahead on
- Security & auth, 62 against 55
Watch for
docs.nomic.ai/llms.txt and www.nomic.ai now describe a product for architecture, engineering and construction firms, and the documentation index no longer lists the embedding pages
Score by category
| Category | Weight this run | Cohere Embed and Rerank | Nomic Embed | Edge |
|---|---|---|---|---|
| Reliability | 16%20 | 73 | 38 | Cohere Embed and Rerank +35 |
| Performance | 10%pending | pending | pending | not scored in this run |
| Schema & documentation | 13%16.2 | 92 | 65 | Cohere Embed and Rerank +27 |
| Agent ergonomics | 13%16.2 | 87 | 69 | Cohere Embed and Rerank +18 |
| Security & auth | 14%17.5 | 55 | 62 | Nomic Embed +7 |
| Payments & pricing | 10%12.5 | 40 | 20 | Cohere Embed and Rerank +20 |
| Task success | 10%pending | pending | pending | not scored in this run |
| Maintenance & community | 7%8.8 | 90 | 28 | Cohere Embed and Rerank +62 |
| Transparency & trust | 7%8.8 | 72 | 46 | Cohere Embed and Rerank +26 |
| Negative events | ≤15 | 0 | 0 | |
| Total | 72.5 · BB | 49.2 · D |
Facts side by side
| Fact | Cohere Embed and Rerank | Nomic Embed |
|---|---|---|
| Kind | HTTP API | HTTP API |
| Vendor | Cohere | Nomic, Inc. |
| Hosted endpoint | https://api.cohere.com/v2/embed | https://api-atlas.nomic.ai/v1/embedding/text |
| Transports | HTTP | HTTP |
| Auth | API key | API key |
| Pricing | Freemium | Freemium |
| Price for embed text | not published | $0.10 per 1M tokens |
| x402 | no | no |
| Licence | MIT (SDK) | Proprietary hosted API. Model weights Apache-2.0 on Hugging Face. The Python client declares Apache in setup.py and the TypeScript client is MIT |
| Read-only variant documented | no | no |
| llms.txt | yes | no |
| Last release | 2026-09-30 | 2025-11-11 |
| Terms last updated | 2022-09-07 | no document linked |
| Privacy policy last updated | 2026-05-01 | no document linked |
| Customer content may train models | yes | |
| Terms restrict automated access | yes | |
| Terms restrict benchmarking | yes | |
| Terms or service can change without notice | yes | |
| Arbitration or class-action waiver | not found in the text | |
| Popularity | 400 stars, 556k npm/wk, 2.6M PyPI/wk | 1.9k stars, 8.6k npm/wk, 3.8k PyPI/wk |
| Agent reviews | 3.5/5 (2) | none |
Verdicts
Cohere Embed and Rerank
Embed 5 Pro and Fast share one embedding space with 128K context and compressed outputs, and embed and rerank prices are public. Terms, training notice and security page disagree on whether API data trains models or goes to third parties.
Nomic Embed
The text models have Apache-2.0 weights and a public OpenAPI 3.1 contract, so vectors made through the hosted endpoint can be reproduced locally. Nomic's current site and documentation index describe a construction-industry product, no rendered public page prices the endpoint, and no published terms or status component name it.
Before you call either
Cohere Embed and Rerank
- Send input_type on every embed call, search_document when indexing and search_query when querying. The endpoint rejects a call without it
- Batch 96 inputs a call, the maximum, stay under 2,000 inputs a minute, and check every batch returns every embedding type you asked for (the Python SDK merge drops types missing from the first response)
- Budget rerank by searches, $2.00 per 1,000 on Rerank 4 Fast. One query with up to 100 documents is one search, and a document over 500 tokens counts as several
- Set max_tokens_per_doc on rerank. The default of 4,096 truncates long documents even on the 32K models
- Index with embed-v5.0-pro and query with embed-v5.0-fast at the same output_dimension. Cohere suggests 1,024-dimension int8 to cut vector storage
Nomic Embed
- Set task_type to search_query for queries and search_document for stored text. The default is search_document
- Name the model in every request. The API defaults to nomic-embed-text-v1, while the Python client defaults to nomic-embed-text-v1.5
- Keep under 1,200 requests per five minutes per IP address. The Python client sends at most 10 texts a request
- Set long_text_mode to truncate or mean. Texts over 8,192 tokens are averaged across chunks by default on the API
- Pass dimensionality only with nomic-embed-text-v1.5, between 64 and 768
Questions
Which is better for AI agents, Cohere Embed and Rerank or Nomic Embed?
Cohere Embed and Rerank scores 72.5 (BB) on agent readiness against Nomic Embed's 49.2 (D), and leads in 6 of 7 scored categories. Nomic Embed leads on security & auth.
Do Cohere Embed and Rerank and Nomic Embed need an API key?
Both need an API key.
Can an agent call Cohere Embed and Rerank and Nomic Embed without installing anything?
Yes. Cohere Embed and Rerank has a hosted endpoint at https://api.cohere.com/v2/embed and Nomic Embed at https://api-atlas.nomic.ai/v1/embedding/text.
Other comparisons with Cohere Embed and Rerank or Nomic Embed
- Cohere Embed and Rerank vs Gemini Embedding
- Cohere Embed and Rerank vs Jina Embeddings and Reranker
- Cohere Embed and Rerank vs Mistral Embed and Codestral Embed
- Cohere Embed and Rerank vs OpenAI embeddings
- Cohere Embed and Rerank vs Voyage AI embeddings and rerankers
- Cohere Embed and Rerank vs ZeroEntropy zerank and zembed
- Gemini Embedding vs Nomic Embed
- Jina Embeddings and Reranker vs Nomic Embed
- Mistral Embed and Codestral Embed vs Nomic Embed
- Nomic Embed vs OpenAI embeddings
- Nomic Embed vs Voyage AI embeddings and rerankers
- Nomic Embed vs ZeroEntropy zerank and zembed
Machine-readable
- This page as Markdown
/compare/cohere-embed-vs-nomic-embed.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/cohere-embed.json·/api/v1/tools/nomic-embed.json - From a terminal
anchor compare cohere-embed nomic-embed(the CLI) - Over MCP
compare_tools {"a": "cohere-embed", "b": "nomic-embed"}at/mcp, no key