Best Embedding Models for RAG: Open and Hosted Options Compared

The best embedding models for RAG compared on dimensions, context, price and scores: OpenAI, Voyage, Cohere, BGE-M3 and Qwen3-Embedding, with sources.

By Galactic News Editorial · Published 2026-11-07T19:31:54+00:00 · Updated 2026-11-07T19:31:54+00:00

Best Embedding Models for RAG: Open and Hosted Options Compared

The best embedding models for RAG split into two camps. Hosted APIs from OpenAI, Voyage AI and Cohere charge per million tokens. Open models from BAAI and Alibaba's Qwen team cost only the compute to run them. The right choice depends on four numbers. Those numbers are dimensions, context length, price and benchmark score. This comparison uses vendor documentation and model cards only. Every figure below traces to a cited source.

What actually matters when you pick from the best embedding models

Dimensions decide your storage bill. A 3,072-dimension vector takes three times the space of a 1,024-dimension one. Context length decides your chunking strategy. A 512-token limit forces small chunks and more of them. Price decides your re-indexing cost. Changing models later means embedding the whole corpus again. Benchmarks help, but only as a first filter. The MTEB suite is the common yardstick here. OpenAI publishes MTEB averages in its own embeddings guide. Treat vendor scores as claims to test, not verdicts.

The shortlist, compared

Prices are vendor list prices checked in October and November 2026. Voyage's current generation is the voyage-4 family. Its pricing page lists $0.02, $0.06 and $0.12 across the lite, standard and large tiers. Each tier includes 200 million free tokens per account. The older voyage-3-large now sits in the legacy list at $0.18. OpenAI remains the simple default. Its large model costs $0.13 per million tokens. Its small model costs $0.02.

Hosted options in detail

OpenAI text-embedding-3-large is the quality benchmark among hosted models. OpenAI's guide reports a 64.6% average on MTEB English. The small model reports 62.3% at a sixth of the price. Both accept up to 8,191 tokens per input. The large model can shorten its 3,072 dimensions on request. That helps control vector storage costs.

Voyage AI targets retrieval quality per dollar. The voyage-4 family keeps vectors at 1,024 dimensions. That is a third of OpenAI large's storage footprint. The 200 million free tokens make serious evaluation cheap. Voyage also sells domain-tuned variants, including code models. Check the current docs before designing around context limits.

Cohere splits its range across two generations. Embed v3 is the established workhorse at $0.10. It produces fixed 1,024-dimension vectors. Its 512-token input limit is the tightest on this list. Embed v4 raises the ceiling sharply. It defaults to 1,536 dimensions, accepts roughly 128k-token documents, and costs $0.12. Cohere's separate multilingual v3 model covers more than 100 languages.

Open models in detail

BGE-M3 from BAAI is the flexible open option. One model produces dense, sparse and ColBERT vectors. That enables hybrid retrieval from a single index pass. It handles inputs up to 8,192 tokens. It supports more than 100 languages. The model has about 568 million parameters. It runs comfortably on a single modest GPU. The MIT licence allows commercial use.

Qwen3-Embedding-8B is the quality-first open option. Its model card reports a 70.58 mean task score on MTEB Multilingual. That leads the multilingual table among open models. Output dimensions are user-defined from 32 to 4,096. Context reaches 32k tokens. The cost is hardware, not tokens. Eight billion parameters need roughly 16 GB of memory at half precision. Budget a 24 GB GPU for comfortable serving.

How to evaluate on your own data

1. Sample 100 real questions from your users.
2. Write down the correct passage for each question.
3. Embed the same corpus with three candidate models.
4. Measure how often the correct passage ranks in the top five.
5. Weigh the winner against its token or hardware cost.

This test takes an afternoon. It beats any leaderboard for your decision. Retrieval failures usually come from chunking, not the model. Fix chunking before you pay for a bigger model.

Bottom line

Start with OpenAI text-embedding-3-small if you want zero operational burden. Move to voyage-4 or Embed v4 if evaluation shows misses. Choose BGE-M3 when data cannot leave your network. Choose Qwen3-Embedding-8B when multilingual quality justifies the GPU. In every case, let your own retrieval test decide. This comparison is research-based; we did not run these models firsthand.

Multilingual and long-document needs change the answer

Language coverage can override every other factor. Cohere sells a separate multilingual v3 model for cross-language search. It covers more than 100 languages. BGE-M3 was built for the same problem in open weights. It also supports more than 100 languages. Qwen3-Embedding-8B posts its strongest scores on the multilingual benchmark. English-only leaderboards will not predict your results here. Test with real queries in your users' languages. Long documents push the decision too. Cohere v3's 512-token ceiling forces fine-grained chunking. Embed v4 accepts documents of roughly 128k tokens. OpenAI and BGE-M3 sit between those extremes at about 8k tokens. Match the limit to your document shape before comparing scores.

The hidden costs: storage and re-embedding

Token price is only part of the bill. Vector storage scales with dimensions. One million vectors at 3,072 dimensions occupy about 12.3 GB as raw floats. The same million at 1,024 dimensions occupy about 4.1 GB. hosted index pricing usually follows that ratio. Shortenable dimensions reduce the gap. OpenAI's large model can return fewer dimensions on request. Qwen3-Embedding lets you pick any width from 32 to 4,096. Re-embedding is the second hidden cost. Switching models invalidates every stored vector. A large corpus can take hours to rebuild. It also burns a full pass of token fees. So run the evaluation before the first big index. A cheap model chosen twice costs more than a good model chosen once.

Hosted versus open: the operational trade

Hosted APIs trade money for simplicity. There is nothing to deploy, patch or scale. Rate limits and per-token fees are the constraints. Open models trade effort for control. Your text never leaves your infrastructure. That matters for legal, medical and internal documents. The ongoing cost is a GPU and its maintenance. Throughput also becomes your problem. A single 24 GB card serves BGE-M3 easily. Qwen3-Embedding-8B at half precision fills most of that card alone. Neither route is universally better. Regulated data points one way. A two-person startup usually points the other.