Embedding Models Compared 2026: OpenAI vs Voyage vs Cohere vs Gemini vs Nomic

TL;DR

The embedding model you choose shapes retrieval quality more than the vector database you store the vectors in, yet it gets a fraction of the attention. In mid-2026, Voyage (now part of MongoDB) and Google Gemini Embedding sit near the top of public quality benchmarks, OpenAI text-embedding-3 remains the cheap and reliable default, Cohere embed v4 leads on multimodal and enterprise multilingual, and Nomic Embed v2 is the pick when you need a fully open, self-hostable model. This is an editorial comparison built from vendor documentation, public benchmarks, and community reports, not a controlled lab test.

Quick Answer

For most RAG apps, start with OpenAI text-embedding-3-small for cost or text-embedding-3-large for quality. If retrieval relevance is critical, evaluate Voyage voyage-3-large and Google gemini-embedding-001, which lead public benchmarks. Choose Cohere embed v4 for multimodal or heavy multilingual enterprise work, and Nomic Embed v2 when you must self-host. Test on your own data before committing.

Why the Embedding Model Is the Real Decision

Teams obsess over which vector database to use and then reach for whatever embedding model is nearest to hand. That is backwards. The embedding model decides how well semantic similarity maps to your actual queries; the database mostly decides how fast and cheaply you can search those vectors. A weak embedding model cannot be rescued by a great index. If you want the fundamentals first, read our guide to vector embeddings, and when you are ready to choose where the vectors live, see the vector database showdown or, if you would rather stay in SQL, Postgres vector search.

Embeddings are the retrieval half of retrieval-augmented generation. When a RAG system returns irrelevant chunks, the embedding model is one of the first suspects; our walkthrough on debugging irrelevant RAG results treats model choice as a core lever.

How We Compared

This is an editorial synthesis, not a benchmark we ran ourselves. We read each vendor's model documentation and pricing pages, cross-checked public leaderboard standings on the MTEB leaderboard, and folded in community reports. We deliberately avoid inventing precise scores. Where we cite figures they are vendor-reported or drawn from public benchmarks and framed as approximate. We weight the decision across these axes:

• Retrieval quality: where the model sits on MTEB and how it behaves on real corpora.
• Context and dimensions: maximum input length and native vector size.
• Multilingual and multimodal: language coverage and whether images are supported.
• Price: cost per 1M tokens and the storage bill that follows from dimension size.
• Open vs closed: can you self-host, and are weights and training data available.
• Compression: Matryoshka truncation and int8/binary quantization support.

A quick orientation before the detail:

1. OpenAI text-embedding-3 — Best for the low-friction default

Best for: teams that want a dependable, inexpensive embedding model that plugs into an existing OpenAI stack with almost no evaluation overhead.

OpenAI ships two models in this family. text-embedding-3-small outputs 1536 dimensions and costs roughly $0.02 per 1M tokens, which makes it the cheapest credible option for most workloads. text-embedding-3-large outputs 3072 dimensions by default and costs materially more, around $0.13 per 1M tokens, in exchange for better retrieval quality. Both support Matryoshka-style shortening: you can request fewer dimensions (for example 256, 512, or 1024) and receive a truncated vector that still performs well. OpenAI has noted that a shortened large-model vector can beat a full-size vector from the older ada-002 generation.

• Dimensions: 1536 (small), 3072 (large), truncatable via the dimensions parameter.
• Price: roughly $0.02 (small) and $0.13 (large) per 1M tokens, vendor-listed.
• Context: 8191 tokens per input.
• Compression: Matryoshka truncation supported; no native int8/binary output.
• Ecosystem: ubiquitous SDK and integration support.

Limitations: it no longer tops MTEB, offers no multimodal input, and is API-only, so you cannot self-host or inspect the weights. For strict data-residency needs it may not qualify.

2. Voyage AI — Best for top-tier and domain-specific quality

Best for: teams that want leading retrieval quality, or a model tuned to code, finance, or legal text, and are comfortable with a closed API.

Voyage AI, now part of MongoDB, has been a fixture near the top of public retrieval benchmarks. voyage-3-large is its flagship general-purpose model, enabled by Matryoshka learning and quantization-aware training so it supports smaller output dimensions plus int8 and binary quantization, with output dimensions up to 2048. Vendor blog posts place voyage-3-large at or near the top of MTEB, typically a fraction of a point ahead of the next best models. The voyage-3.5 and voyage-3.5-lite models push quality further at aggressive prices. Where Voyage really separates itself is domain specialization: voyage-code-3 (32K context) for source code, plus voyage-finance-2 and voyage-law-2 for financial and legal text, which can beat larger general models on their niche.

• Quality: consistently near the MTEB top; strong on code and legal retrieval.
• Dimensions: configurable, up to 2048 on voyage-3-large; domain models vary.
• Context: 32K tokens on the voyage-3 series and voyage-code-3.
• Compression: int8 and binary output plus Matryoshka truncation on recent models.
• Price: competitive per-token pricing; check the current Voyage pricing page.

Limitations: closed and API-only, with no self-hosting except via cloud marketplace listings. Availability now runs partly through MongoDB's platform, which some teams will welcome and others will not.

3. Cohere embed v4 — Best for multimodal and enterprise multilingual

Best for: enterprises indexing mixed text and image content, needing 100-plus languages, and wanting quantization built in for cost control at scale.

Cohere's embed-v4.0 is a multimodal model that can embed text, an image, or text and an image together in the same call, which is unusual among the closed leaders. It supports over 100 languages and a large 128K context window, and it exposes configurable output dimensions of 256, 512, 1024, and 1536 via Matryoshka. Crucially for large indexes, it can emit float, int8, uint8, binary, and ubinary vectors natively, so you can shrink storage roughly 4x with int8 or far more with binary while keeping most of the accuracy. That combination of multimodal input, wide language coverage, and native quantization is aimed squarely at enterprise RAG at scale.

• Multimodal: text and image in a single embedding request.
• Multilingual: best-in-class coverage across 100-plus languages.
• Context: up to 128K tokens per input.
• Compression: native int8, uint8, binary, and ubinary output plus Matryoshka dims.
• Deployment: available through major clouds (Bedrock, Azure, Oracle) for enterprises.

Limitations: closed and API-only, and the multimodal payload accepts a single image per call. Pricing and quotas skew enterprise; hobby projects may find OpenAI cheaper to start.

4. Google gemini-embedding-001 — Best for multilingual quality

Best for: teams that need strong multilingual retrieval, already run on Google Cloud or the Gemini API, and want a flexible dimension budget.

Google's gemini-embedding-001 reached general availability in 2025 after topping the MTEB multilingual leaderboard as an experimental model. It outputs 3072 dimensions by default with Matryoshka Representation Learning, so you can truncate to 1536 or 768 without re-embedding, supports over 100 languages, and handles input sequences up to a couple of thousand tokens. On public multilingual benchmarks it posts among the highest average scores, which makes it a natural pick when your corpus or your users are not English-first. Pricing is vendor-listed around $0.15 per 1M input tokens, with a discounted batch tier.

• Multilingual: state-of-the-art class on the MTEB multilingual leaderboard.
• Dimensions: 3072 default, truncatable to 1536 or 768 via Matryoshka.
• Context: input sequences up to roughly 2048 tokens.
• Price: about $0.15 per 1M tokens standard, with a cheaper batch option.
• Ecosystem: available via the Gemini API and Vertex AI.

Limitations: shorter maximum input than Cohere or Voyage, no multimodal text-plus-image embedding in this model, and it is closed and API-only. It fits best where you already live in Google's stack.

5. Nomic Embed Text v2 — Best for open-source and self-hosting

Best for: teams that must keep data on-premises, want full reproducibility, or need to run embeddings offline without per-token API costs.

Nomic Embed Text v2 is the standout open model here. It is described as the first general-purpose text embedding model to use a Mixture-of-Experts architecture, with roughly 475M total parameters but only about 305M active at inference thanks to top-2 routing across 8 experts. It is Apache-2.0 licensed and fully reproducible: weights, training data, and training code are all open, which is rare and valuable for audit-heavy environments. It covers around 100 languages, was trained on over 1.6 billion contrastive pairs, and supports Matryoshka dimensions from 768 down to 256 for cheaper storage. You can run it via Hugging Face, Ollama, or Docker.

• License: Apache-2.0 with open weights, data, and code (fully reproducible).
• Architecture: Mixture-of-Experts, roughly 475M total and 305M active params.
• Multilingual: trained across about 100 languages.
• Dimensions: 768 default, truncatable to 256 via Matryoshka.
• Self-host: runs locally via Hugging Face, Ollama, or Docker at no per-token cost.

Limitations: on raw English retrieval it typically trails the closed leaders like Voyage and Gemini, and you own the operational burden of serving it. Its smaller dimension ceiling may cap quality for the most demanding search tasks.

Decision Axes That Actually Matter

Retrieval quality and MTEB

MTEB is the standard yardstick, but treat it as a starting point, not gospel. Leaders such as Voyage voyage-3-large, gemini-embedding-001, and large open models like Qwen3-Embedding are often separated by a point or two, and MTEB v2 scores are not directly comparable to v1. Always re-rank a shortlist on your own queries and documents.

Context and dimensions

Longer context (Cohere at 128K, Voyage at 32K) lets you embed bigger chunks, though very long chunks can dilute relevance. Larger native dimensions can help quality but cost more to store and search. Matryoshka lets you decouple the two by truncating after the fact.

Multilingual and multimodal

If your corpus spans many languages, Gemini and Cohere are the safest closed picks and Nomic is the open one. If you must embed images alongside text, Cohere embed v4 is the clearest single-model answer today.

Price and quantization

Token price is only half the bill; vector storage is the other half. Dropping from 3072 to 768 dimensions via Matryoshka cuts storage roughly 4x, and int8 or binary quantization (Cohere, Voyage) cuts it several times more. For large indexes those savings can dwarf the embedding API cost.

Open vs closed

Closed models lead on convenience and often on quality, but only open models like Nomic let you self-host, inspect weights, and avoid per-token fees and data-egress concerns. This is frequently a compliance decision, not a quality one.

Which Should You Choose

• Best overall quality: Voyage voyage-3-large or Google gemini-embedding-001. Shortlist both and test on your data; the winner depends on your language mix and domain.
• Best value and easiest default: OpenAI text-embedding-3-small at roughly $0.02 per 1M tokens, upgrading to text-embedding-3-large only when relevance demands it.
• Best open-source and self-host: Nomic Embed Text v2, Apache-2.0, Mixture-of-Experts, and fully reproducible.
• Best multilingual: Google gemini-embedding-001 for closed, Cohere embed v4 for enterprise multilingual plus multimodal, Nomic Embed v2 for open.
• Best domain-specific: Voyage voyage-code-3 for code, voyage-finance-2 for finance, voyage-law-2 for legal text.
• Best multimodal: Cohere embed v4, which embeds text and images in one call.

Migration and Operational Notes

Query and document vectors must come from the same model and dimension setting, so switching models or changing the Matryoshka dimension means re-embedding your whole corpus and rebuilding the index. Record the model name and dimension alongside your vectors so a future migration is not a guessing game. When you test candidates, evaluate retrieval on real queries with a held-out set of relevant documents rather than trusting a single leaderboard number.

Conclusion

There is no single best embedding model in 2026, only the best fit for your axis. Reach for OpenAI text-embedding-3 when you want a cheap, reliable default; Voyage or Gemini when retrieval quality is the priority; Cohere when you need multimodal or heavy multilingual enterprise support; and Nomic when open weights and self-hosting are non-negotiable. Whatever you shortlist, use Matryoshka truncation and int8/binary quantization to keep the storage bill in check, and always validate on your own data before you commit an entire index to one model.

This is an editorial synthesis of vendor documentation, public benchmarks, and community reports; see our [methodology](/methodology). Verify current details with each vendor.