Nomic nomic-embed-text-v2-moe
Nomic AI
Capabilities
Strengths
- Top open English MTEB scores at the 1B-param scale
- Matryoshka — 64 to 768 dim
- Apache 2.0 licence
- Mixture-of-experts — efficient inference
Weaknesses
- 2k input limit
- English-leaning vs BGE-M3
Pricing
Input / 1M tokens
Free (self-host)
Output
—
Hosting
Open weights
Embedding specs
Output dimension
768
Max input
2,048 tokens
Matryoshka
Supported
Matryoshka representation learning: dimension can be truncated to a smaller size at index time without retraining, trading retrieval quality for index size and speed.
Transparency
Open weights
10.0 / 10
Open training data
5.0 / 10
Open methodology
7.0 / 10
Licence openness
9.0 / 10
Provider disclosure
7.0 / 10
FMTI company score
N/A
Open weights with permissive licence; methodology partially documented.
Sustainability
Inference energy
8.5 / 10
Training footprint
N/A
Provider infrastructure
7.0 / 10
Small enough to run on local hardware — user controls the energy source.
MTEB quality
Grounded in the MTEB (Massive Text Embedding Benchmark) Overall average published by the model authors. Bearing collapses MTEB's retrieval, STS, classification, and clustering categories into a single quality signal because they correlate strongly for embedding models. Methodology.