ModelRefs / BGE-M3 vs Text Embedding 3 Large
BGE-M3 vs Text Embedding 3 Large
BGE-M3 vs Text Embedding 3 Large: side-by-side benchmarks, pricing, context windows, coding ability and deployment. Pick the right model for your stack.
Overview
BGE-M3: BAAI's 568M-parameter multilingual embedding and retrieval model supporting dense, sparse, and multi-vector representations with inputs up to 8,192 tokens.
Text Embedding 3 Large: Text Embedding 3 Large is OpenAI's larger third-generation embedding model, returning 3,072-dimension vectors from inputs of up to 8,192 tokens. Its dimensions parameter shortens the vector at creation time without, as OpenAI puts it, the embedding losing its concept-representing properties, which lets a retrieval system trade index size against accuracy without changing models. Shortening at creation is preferred to truncating afterwards, which requires re-normalising the vector.
Context window: BGE-M3 accepts up to 8,192 tokens, Text Embedding 3 Large up to 8,191. Confirm the limit for your specific deployment channel before relying on it.
BGE-M3 vs Text Embedding 3 Large at a glance
| Attribute | BGE-M3 | Text Embedding 3 Large |
|---|---|---|
| Provider | BAAI | OpenAI |
| Released | 2024-01-30 | 2024-01-25 |
| Context window | 8,192 tokens | 8,191 tokens |
| Input price | Free / self-hosted | $0.13/M tokens |
| Output price | Free / self-hosted | Free / self-hosted |
| Licence | MIT | Proprietary |
| Self-hostable | Yes, open weights | No, hosted only |
| Modalities | text, Embedding, Open Source, Multilingual, BAAI | text, Embedding |
Reported on different variants
Both models are measured on these benchmarks, but not on the same variant of them, so the two numbers are not a like-for-like comparison. Each side is shown with the variant it was measured on.
| Benchmark | BGE-M3 | Text Embedding 3 Large | Reported |
|---|---|---|---|
| MIRACL | 69.2 (average ndcg@10) | 54.9 (average score) | 2024-07-01 / 2024-01-25 |
A different task set or a different reported metric can move a score by more than the gap between two models. Use these as two separate findings, not as a ranking.
Where they differ most
- Multilingual: BGE-M3 95%, Text Embedding 3 Large 78%. BGE-M3 leads on this dimension.
- RAG Suitability: BGE-M3 95%, Text Embedding 3 Large 86%. BGE-M3 leads on this dimension.
- Cost Efficiency: BGE-M3 100%, Text Embedding 3 Large 99%. BGE-M3 leads on this dimension.
Capability scores are ModelRefs' own derived signals, not vendor claims or benchmark results. Validate against your own workload before relying on them.
Continue your research
Use these connected ModelRefs sections to compare alternatives, inspect implementation paths, and review the evidence and governance boundaries relevant to BGE-M3 vs Text Embedding 3 Large.
Frequently asked questions
Which is better, BGE-M3 or Text Embedding 3 Large?
BGE-M3 and Text Embedding 3 Large target different workloads — see the benchmark and pricing tables for a side-by-side answer.
Is BGE-M3 cheaper than Text Embedding 3 Large?
Compare Free — open weights vs $0.00013 on this page.