BGE-M3

BAAI's multi-functionality + multilingual (170+ languages) + multi-granularity embedding. The default "just use it" RAG embedding since early 2024. As of 2026 it is no longer the top-quality pick — `Qwen3-Embedding` (0.6B / 4B / 8B, Apache 2.0) now leads MTEB overall — but BGE-M3 remains the sharpest pick for cheap, broad multilingual breadth at 568M.

License: MIT · Context: 8192 tokens · Released: February 2024

The decision in five lines

The call: Consider — runnable locally, family reference
Best for: Local evaluation and family reference
Runs on: 23 hardware picks fit (cheapest: Intel Arc B580 12 GB · $310)
Watch out: Max-quality general/English retrieval — Qwen3-Embedding-8B (Apache 2.0) ranks #1 on MTEB and is the better default when you have the VRAM.
Evidence: Estimated · last verified September 2026

568M (XLM-RoBERTa-large base): PARAMETERS
EMBEDDING: TYPE
8192: CONTEXT
~1–2 GB: VRAM AT Q4

Where we recommend this

This model isn’t currently in an active planner slot. See the runner notes below if you’re running it anyway.

The call

BAAI's multi-functionality + multilingual (170+ languages) + multi-granularity embedding. The default "just use it" RAG embedding since early 2024. As of 2026 it is no longer the top-quality pick — `Qwen3-Embedding` (0.6B / 4B / 8B, Apache 2.0) now leads MTEB overall — but BGE-M3 remains the sharpest pick for cheap, broad multilingual breadth at 568M.
When not to use: Max-quality general/English retrieval — Qwen3-Embedding-8B (Apache 2.0) ranks #1 on MTEB and is the better default when you have the VRAM. Use BGE-M3 when you want the smallest model that still covers 170+ languages, or a CPU-friendly 568M retriever.

Runner notes

Ollama tag `bge-m3`. Also natively in FlagEmbedding, sentence-transformers, llama.cpp. 568M params run fine on CPU for small corpora. For top quality step up to `Qwen/Qwen3-Embedding-8B` (or the 0.6B/4B for lighter rigs).

License: MIT
Released: February 2024
Maker: BAAI
Model card: huggingface.co/BAAI/bge-m3 →

Hardware that fits

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