How to choose an embedding model (2026)

The criteria that matter

Choosing an embedding model isn't about the leaderboard #1 — it's about fit. Weigh:

• Quality on your domain — retrieval accuracy on your data beats generic benchmarks.
• Dimension — higher dims can mean better recall but more storage and slower search.
• Context length — can it embed your chunk sizes without truncation?
• Multilinguality — do you need cross-language retrieval?
• Latency & size — local/CPU-friendly vs. large and GPU-hungry.
• License — permissive (MIT/Apache) matters for commercial use.

A simple process

1. Shortlist 2–3 models that fit your language and size constraints.
2. Build a small labeled retrieval set from your own data.
3. Measure recall@k with an eval harness.
4. Pick the best quality-per-latency, not the biggest.

On ai-supply

Embedding models are published with a security score, grade, and license front and center, so you can filter for permissive options fast. Browse the data and NLP categories and compare on the leaderboards.

Don't guess — measure on your data, then pick. Start with vetted models on the catalog.