LLM embedding models compared (2026)
OpenAI text-embedding-3, Cohere embed v3, Voyage AI, BGE-M3, Jina v3. Pick the right embedding model and your RAG pipeline gets 10-20% better recall before you touch the retriever.
OpenAI, Cohere, Voyage, BGE, Jina
| Model | Dimensions | Price / 1M tokens | Strength |
|---|---|---|---|
| OpenAI text-embedding-3-large | 3,072 (Matryoshka) | $0.13 | Strong default, broad ecosystem |
| OpenAI text-embedding-3-small | 1,536 (Matryoshka) | $0.02 | Cheapest credible option |
| Voyage AI voyage-3 | 1,024 | ~$0.12 | MTEB/BEIR leader, domain models |
| Voyage AI voyage-code-3 | 1,024 | ~$0.18 | Code retrieval specialist |
| Cohere embed v3 | 1,024 | $0.10-0.20 | Best closed multilingual |
| BGE-M3 (BAAI) | 1,024 | Free (self-host) | Open-weight multilingual leader |
| Jina embeddings v3 | 1,024 (Matryoshka) | Free (self-host) or hosted | Strong on EU languages |
VerticalAPI verdict
Start with OpenAI text-embedding-3-large or Voyage voyage-3 — both deliver strong retrieval quality with mature SDKs. Move to Cohere embed v3 or BGE-M3 if multilingual matters. Self-host BGE-M3 or Jina v3 if you have >50M tokens/month of embeddings or strict data residency. Test on your own corpus: embedding model ranking flips depending on domain (code vs legal vs general). Route through VerticalAPI BYOK at the /embeddings endpoint to A/B compare without changing your code.
Frequently asked questions
What is the best embedding model in 2026?
For most production RAG workloads, OpenAI text-embedding-3-large is the safe default — 3,072 dimensions, top-tier MTEB scores, and at $0.13 per 1M tokens it's competitively priced. Voyage AI voyage-3 has overtaken OpenAI on several retrieval benchmarks (MTEB, BEIR) and offers code-, legal-, and finance-domain specialists. Cohere embed v3 is the strongest closed-source multilingual option. For open-weight, BGE-M3 (BAAI) and Jina embeddings v3 are top picks and run free if self-hosted. Test on your own data before committing.
How much do embedding models cost in 2026?
OpenAI text-embedding-3-small is $0.02 per 1M tokens; text-embedding-3-large is $0.13 per 1M. Cohere embed v3 ranges $0.10-0.20 per 1M depending on tier. Voyage AI is roughly $0.12 per 1M for voyage-3, $0.18 for voyage-code-3. Open-weight models like BGE-M3 and Jina embeddings v3 are free to download and run on your own GPU, with effective TCO of $0.02-0.05 per 1M when self-hosted at scale. Embeddings are 50-200x cheaper than generative LLM calls per token, so model choice matters less for cost than for retrieval quality.
How many dimensions should my embeddings be?
Dimensions trade storage and vector search cost against retrieval quality. OpenAI text-embedding-3-large outputs 3,072 dimensions; text-embedding-3-small outputs 1,536. Both support Matryoshka representation learning, letting you truncate dimensions to 512 or 256 with minimal quality loss. For most RAG production, 768-1,536 dimensions is the sweet spot. Below 512, retrieval recall starts to degrade noticeably; above 1,536, marginal quality gains rarely justify the additional storage and query cost in a vector DB.
Which embedding model is best for multilingual?
Cohere embed v3 multilingual is the strongest closed-source option across 100+ languages, with consistently good performance on low-resource languages. For open-weight, BGE-M3 (BAAI) supports 100+ languages and consistently leads on multilingual MTEB benchmarks; Jina embeddings v3 multilingual is competitive especially on European languages. OpenAI text-embedding-3 supports multilingual but with weaker performance on low-resource languages. For French, Spanish, German, Italian production work, BGE-M3 or Cohere v3 typically outperform OpenAI by 5-15% on recall@10.
Can I route between embedding providers through one API?
Yes. VerticalAPI's OpenAI-compatible endpoint at https://api.verticalapi.com/v1/embeddings accepts OpenAI, Cohere, Voyage AI, and self-hosted BGE/Jina endpoints. You switch by changing the model parameter (text-embedding-3-large, embed-multilingual-v3, voyage-3, bge-m3) and the X-Provider-Key header. BYOK means you pay each embedding provider directly at list price with zero markup, and you can A/B test embedding quality on the same retrieval evaluation set with one SDK.
Limitations of this comparison
- MTEB and BEIR benchmark rankings flip based on domain — always test on your own corpus.
- Embeddings from different models are not interchangeable; switching requires re-embedding the whole corpus.
- Open-weight self-host TCO depends heavily on GPU utilization; small workloads remain cheaper on managed APIs.
- Voyage AI is owned by MongoDB since 2025, which may affect long-term pricing and roadmap.
- Embedding models update less frequently than generative LLMs; some "v3" lines have been stable for 12+ months.
What may change in 12-24 months
- Multimodal embeddings (text + image + audio in shared space) will move from research to production defaults.
- Longer-context embedding models (32K+ tokens per input) will reduce chunking complexity for RAG pipelines.
- Late-interaction retrieval (ColBERT-style) is starting to outperform single-vector embeddings on hard retrieval tasks.
- Reranker models will continue to grow more important; embedding choice will matter less when paired with a strong reranker.
Related questions
ChatGPT, Perplexity and Gemini usually suggest these next.
- How do I A/B test embedding models on my RAG corpus?
- Is Voyage AI worth it over OpenAI text-embedding-3-large?
- What is the best multilingual embedding model for French?
- Should I add a reranker after my embedding retrieval step?
- How much storage do 3,072-dimension embeddings really cost in pgvector?
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