# Choosing Embeddings in 2026: OpenAI vs Cohere vs Voyage vs Open-Source

> A decision guide for picking an embedding model for retrieval — accuracy, dimensions, cost, multilingual and domain fit, self-hosting, and lock-in.

There's no single best embedding model — choose by retrieval accuracy on your data, dimensions vs. storage cost, multilingual and domain needs, and whether you must self-host. Hosted APIs (OpenAI, Cohere, Voyage) are easiest and Voyage often leads on retrieval; open-source (BGE, Nomic, E5) wins on cost, privacy, and control. Whatever you pick, switching later means re-embedding everything.

The embedding model is the lens your whole retrieval system looks through: it decides which passages count as "similar" to a question. Pick well and retrieval is easy; pick badly and no reranker fully recovers. The catch is that the choice carries **lock-in** — switching models later means re-embedding and re-indexing everything — so it's worth a deliberate decision rather than defaulting to whatever the tutorial used.

This guide gives you a framework and an honest read on the main options as of 2026.

## Read benchmarks, then ignore them

The [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard is the standard reference, and it's useful for building a shortlist. But leaderboard rank is measured on generic academic tasks, not your documents, your jargon, or your users' phrasing. A model that's #1 overall can be mediocre on legal contracts or your internal acronyms. **Use benchmarks to pick 2–3 candidates, then measure them on your own eval set** (the [embedding-set-inspector](/skills/data/embedding-set-inspector) skill and a labeled query set make this concrete). The numbers on your data are the only ones that decide.

## The dimensions that actually matter

- **Retrieval accuracy on your corpus** — the whole point. Measure recall@k, not leaderboard rank.
- **Dimensions vs. cost** — higher-dimensional vectors can be more accurate but cost more to store and are slower to search. Many 2026 models support **Matryoshka** truncation, so you can shorten vectors (e.g. 1024 → 256) and trade a little quality for big storage/speed wins.
- **Multilingual & domain fit** — if your content isn't English-only or lives in a specialized domain (code, finance, law, medicine), prefer a model built for it.
- **Context length** — how much text fits in one embedding call, which interacts with your chunk size.
- **Self-host vs. API** — can your data leave your environment? Do you need offline/air-gapped operation or cost control at scale?
- **Lock-in** — the re-embedding cost if you ever switch. Bigger corpus, bigger commitment.

## The options in 2026

### Hosted APIs — easiest, often most accurate

- **OpenAI (`text-embedding-3` small/large)** — a strong, well-supported default with Matryoshka dimension control. The path of least resistance if you're already in the OpenAI ecosystem.
- **[Voyage AI](/tools/voyage-ai)** — consistently among the top performers on *retrieval* specifically, with domain-specific variants (code, finance, law) and asymmetric document/query embeddings. A common pick when retrieval accuracy is the bottleneck. (Now part of MongoDB.)
- **Cohere Embed** — excellent multilingual and multimodal support and a mature platform; pairs naturally with [Cohere Rerank](/tools/cohere-rerank) for a hosted retrieve-and-rerank stack.

Hosted APIs mean no model infrastructure, easy upgrades, and usage-based cost — at the price of sending your text to a third party and paying per token.

### Open-source — control, privacy, and cost

- **BGE (BAAI), including bge-m3** — strong general-purpose and multilingual models; `bge-m3` notably does dense, sparse, and multi-vector retrieval in one.
- **Nomic Embed** — open, reproducible, long-context, with a permissive stance and good retrieval quality.
- **E5 / GTE / Jina** — competitive families covering multilingual and long-context needs.

Open-source models you run yourself win when you need data to stay in-house (privacy/compliance), want to control cost at scale, or must run offline. The trade is that you operate the model — GPU/throughput, versioning, and uptime are now your problem.

## A decision shortcut

- **Fastest path, great default** → OpenAI `text-embedding-3-large` (truncate dimensions if storage matters).
- **Max retrieval accuracy, hosted** → **Voyage AI** (use a domain variant if you have one).
- **Multilingual / multimodal, hosted** → **Cohere Embed** (+ Cohere Rerank).
- **Privacy, offline, or cost-at-scale** → **BGE / Nomic / E5**, self-hosted.

Then confirm the choice on your own data before you commit — because re-embedding a large corpus to fix a wrong default is the expensive way to learn this.

> [!WARNING]
> Match your distance metric to the model (cosine vs. dot product vs. L2) and use the model's asymmetric input types — document for the corpus, query for the question. A mismatch here silently degrades retrieval and is one of the most common embedding bugs.

For where embeddings sit in the broader system, see [How RAG Actually Works](/guides/concepts/how-rag-works); for handing the build to an agent, the [data-scientist](/agents/data-ai/data-scientist) and [rag-pipeline-engineer](/agents/data-ai/rag-pipeline-engineer) can take it from a shortlist to a measured choice.

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_Source: https://agentscamp.com/guides/concepts/choosing-embeddings-2026 — Guide on AgentsCamp._