forkaltEmbeddingGemma 2 vs BGE-M3: Which to Use?
Verdict. Use EmbeddingGemma 2 when you need an open multimodal embedding model (text, code, image, video, audio). Use BGE-M3 when you want a longer-listed multilingual embedding model under MIT. We quote no bake-off scores.
Compare EmbeddingGemma 2 and BGE-M3.
| Dimension | EmbeddingGemma 2 | BGE-M3 | Source |
|---|---|---|---|
| Developer | Google DeepMind | BAAI | Hugging Face cards |
| License | Apache-2.0 | MIT | Cards, 2026-10-13 |
| Type | Multimodal embedding | Multilingual multi-function embedding | Cards |
| Modalities | text, code, image, video, audio | text (seed description; other modalities not in seed) | EmbeddingGemma 2 card; BGE-M3 seed blurb |
| Parameters | ~0.74B | Not in seed extract | EmbeddingGemma 2 card |
| Hugging Face likes | 1,889 as of 2026-10-13 | 3,866 as of 2026-10-13 | Cards |
| First listed | 2026-09-14 | 2024-01-27 | Cards |
| Performance | Not independently reproduced | Not independently reproduced | n/a |
When to choose which
- Multimodal retrieval (image/audio/video as well as text): EmbeddingGemma 2 — Modalities are the sourced difference.
- Established multilingual text embeddings: BGE-M3 — Older listing (2024) and MIT license; quality untested here.
Entity pages: EmbeddingGemma 2 and BGE-M3. Hub: open embedding API alternatives.
Other options
FAQ
Which scores higher on MTEB?
We do not quote MTEB (or other) numbers that are missing from the seed and verification files.
Can I run either on a Mac?
DigUp is a verified Mac app built on EmbeddingGemma 2. That does not by itself prove BGE-M3 on-device performance. Our run guide for EmbeddingGemma 2 is not yet tested.
Are both free?
Both cards list OSI-style licenses (Apache-2.0 and MIT) as of 2026-10-13. Not legal advice.
Sources & verification
- https://huggingface.co/google/embeddinggemma-2
- https://huggingface.co/BAAI/bge-m3
- https://github.com/ARahim3/DigUp
License notes are not legal advice. Read the original LICENSE. Trademark & disclaimer.