Best Embedding Models in 2026: OpenAI vs Gemini vs Open

There is no single "best" embedding model — there is the best one for your retrieval quality, latency, cost, and data-residency constraints. This roundup compares the three that matter in 2026: OpenAI's text-embedding-3-small and text-embedding-3-large, Google's gemini-embedding-001, and the leading open-weight models you can self-host — Qwen3-Embedding-8B, BAAI's BGE-M3, and multilingual-E5. We line them up by output dimensions, price per million tokens, max input length, MTEB score, and licence, then give you a decision framework for picking one. Every number below is sourced to a primary doc or model card and dated.

The short answer

Pick by constraint, not by hype. If you want a hosted API and the best price-to-quality on mostly-English retrieval, OpenAI text-embedding-3-large is the default — 3072 dimensions, $0.13 per 1M tokens. If your corpus is heavily multilingual and you want a hosted API, Google gemini-embedding-001 has consistently held a top spot on the MTEB Multilingual leaderboard since March 2025 (Google-reported mean multilingual score around 68.3). If you must self-host for cost, latency, or data residency, Qwen3-Embedding-8B was ranked No.1 on MTEB Multilingual at 70.58 as of June 5 2025 per its model card, and BGE-M3 is the pragmatic all-rounder. The rest of this guide shows the numbers behind each call.

The comparison table

Six models, five axes that decide the pick: dimensions, price, max input, MTEB score, and licence. Every figure is sourced below the table and dated July 2026.

^* Gemini MTEB 67.99 is at the 768-dimension setting per Google's own benchmark table (ai.google.dev); Google separately reports a top MTEB Multilingual spot with a mean around 68.3. ^† Qwen3-Embedding-8B 70.58 is the No.1 MTEB Multilingual score as of June 5 2025 per its model card. MTEB is a third-party benchmark, but these specific numbers were read off vendor cards/docs, not the live leaderboard — treat them as dated snapshots and re-check the live leaderboard before deciding. OpenAI does not publish an MTEB figure in the sources used here, so those cells are left blank rather than guessed.

OpenAI: text-embedding-3-small and 3-large

The safe default for English-first retrieval, with a small/large tradeoff you tune with one parameter. Both models let you shorten the output vector via the dimensions parameter, so you can trade a little recall for a lot of storage savings without re-embedding on a different model.

• text-embedding-3-small — default 1536 dimensions, reducible down to 256 via the dimensions parameter. Priced at $0.02 per 1M tokens standard, $0.01 batch. This is the cheapest hosted option in the roundup and the right starting point for most RAG builds. We have a dedicated deep-dive on it in the text-embedding-3-small guide.
• text-embedding-3-large — default 3072 dimensions, also shortenable via dimensions. Priced at $0.13 per 1M tokens standard, $0.065 batch. You pay 6.5× more than small for higher retrieval quality; whether that is worth it depends on your eval, not a leaderboard.

Dimensions, the reduction behaviour, and pricing above are from OpenAI's embeddings guide and model pages (verified July 2026; prices are marked medium-confidence — re-confirm on the live pricing page before you budget).

Google gemini-embedding-001

The multilingual specialist: 100+ languages, Matryoshka dimensions, and a long-held top MTEB Multilingual spot — but a short input window. Google's single production embedding model uses Matryoshka Representation Learning (MRL): a default output of 3072 dimensions with recommended settings of 3072, 1536 or 768 and a flexible range of 128–3072. It supports over 100 languages and has a maximum input of 2048 tokens — notably shorter than OpenAI's ~8k window, which matters if you embed long chunks.

On quality, Google reports the model has consistently held a top spot on the MTEB Multilingual leaderboard since its experimental launch in March 2025, with a reported mean multilingual task score around 68.3 (vendor-reported). Its own benchmark table lists 67.99 MTEB at the 768-dimension setting, which shows how little quality MRL truncation costs. Pricing is $0.15 per 1M input tokens, with a batch option at $0.075 (50% off). All figures from the Gemini embeddings docs and the Google Developers Blog. If you already hold a Gemini key, our Gemini API key guide covers setup.

The best open embedding models

When you self-host — for data residency, latency, or zero per-token cost — three open models lead. All three run on your own GPU with no API fee; you trade an inference bill for infra you operate.

• Qwen3-Embedding-8B — Apache-2.0, 8B parameters, embedding dimensions user-definable from 32 to 4096 via MRL, and a 32k-token context length. It ranked No.1 on the MTEB Multilingual leaderboard at 70.58 as of June 5 2025 per its model card. Highest quality here, but an 8B model needs real GPU memory — plan capacity the way you would for any large open model (see our best open-source LLM guide for the hosting math).
• BAAI/bge-m3 — MIT licensed, 1024-dimension dense embeddings, up to 8192-token inputs, 100+ languages, and uniquely it offers dense, sparse (lexical), and multi-vector (ColBERT) retrieval in one model. The pragmatic all-rounder: one model covers hybrid search without bolting on a separate BM25 stage. Source: BGE-M3 model card.
• intfloat/multilingual-e5-large-instruct — MIT licensed, 1024-dimension embeddings, a 24-layer model initialised from XLM-RoBERTa-large, supporting 100 languages. A lighter, well-understood baseline when 8B is too heavy. Source: E5 model card.

Dimensions, MRL and storage cost

More dimensions can mean better recall but strictly more storage and slower search — MRL lets you dial it down with graceful loss. A vector index cost scales with dimension count: a 3072-dim vector is 4× the storage and roughly the search cost of a 768-dim one. Models trained with Matryoshka Representation Learning — gemini-embedding-001 and Qwen3-Embedding-8B here — pack the most information into the leading dimensions, so you can truncate the tail and keep most of the quality. Gemini scoring 67.99 at 768 dims versus its full-length top-spot mean of ~68.3 is the proof: cutting to a quarter of the dimensions barely moves the score. OpenAI's dimensions parameter (down to 256 on small, reducible on large) is the same lever under a different name.

A decision framework: pick which, when

Map your dominant constraint to a model, then validate on your own data. This synthesised framework turns the numbers above into a choice:

Three rules that override the table: (1) Always run your own retrieval eval — a leaderboard rank is not your recall@k on your corpus. (2) Never mix embedding models in one index — vectors from different models are not comparable, so switching models means a full re-embed. (3) Re-check the live MTEB leaderboard before committing; the scores quoted here are dated snapshots from vendor cards, and ranks shift as new models land.

Embeddings and the DataLLM Lab gateway

DataLLM Lab is an OpenAI-compatible gateway focused on chat and completion models — and it exposes a /v1/embeddings endpoint at the same base URL. That means OpenAI embedding client code points at the gateway with only a base-URL and key change, the same way your chat calls do (the pattern is covered in our OpenAI-compatible API guide):

from openai import OpenAI

client = OpenAI(
    base_url="https://www.datallmlab.com/v1",
    api_key="YOUR_DATALLMLAB_KEY",
)

resp = client.embeddings.create(
    model="text-embedding-3-small",   # check the models list for current availability
    input="the quick brown fox",
)
print(len(resp.data[0].embedding))

To be clear about what we serve: DataLLM Lab does not claim any specific embedding model is hosted on the gateway — the endpoint is OpenAI-compatible, and current model availability is on the models list. Where the gateway earns its keep is chat, coding and agent workloads: one key across 300+ models with routing and failover. For those, start with our best LLM API roundup or the agent-model guide.

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