all-mpnet-base-v2
all-mpnet-base-v2 is a 109M-parameter sentence-transformers embedder based on Microsoft's MPNet, producing 768-dim vectors with a 384-token context. Trained on the same 1B+ sentence pairs as all-MiniLM-L6-v2 but with a stronger backbone, it sits at the quality-tier above MiniLM-L6 and remains the most-cited 'general-purpose' sentence-transformers baseline.
The 'I want 768 dims, I want sentence-transformers, I want Apache-2.0' default. all-mpnet-base-v2 has been the reference English embedder since 2021 and remains a credible baseline today. For new 2026 projects, gte-modernbert-base or nomic-embed-text-v1.5 deliver better quality with longer context at lower parameter count — but the mpnet line is so embedded in production codebases that it earns a row regardless.
Overview
all-mpnet-base-v2 is a 109M-parameter sentence-transformers embedder based on Microsoft's MPNet, producing 768-dim vectors with a 384-token context. Trained on the same 1B+ sentence pairs as all-MiniLM-L6-v2 but with a stronger backbone, it sits at the quality-tier above MiniLM-L6 and remains the most-cited 'general-purpose' sentence-transformers baseline.
Strengths
- MTEB English score ~57.8 — meaningfully above MiniLM-L6 at ~5x the params
- 768-dim output matches the standard for vector DB schemas (Postgres pgvector default)
- Apache-2.0, no usage restrictions
- Mature ONNX/CoreML/safetensors/GGUF support across runtimes
- Reference baseline in countless sentence-transformers tutorials and papers
Weaknesses
- Loses to modern 100-150M embedders (gte-modernbert-base, nomic-v1.5) on MTEB by 5-8 points
- 384-token context window is short by 2026 standards
- English-only — no multilingual variant under the all-mpnet name
- Symmetric similarity model; no query/document role distinction
Quantization variants
Each quantization trades model quality for file size and VRAM. Q4_K_M is the most popular starting point.
| Quantization | File size | VRAM required |
|---|---|---|
| Q4_K_M | 0.1 GB | 1 GB |
Get the model
HuggingFace
Original weights
Source repository — direct quantization required.
Hardware that runs this
Cards with enough VRAM for at least one quantization of all-mpnet-base-v2.
Models worth comparing
Same parameter band, plus what's one tier above and below — so you can decide what actually fits your hardware.
Frequently asked
What's the minimum VRAM to run all-mpnet-base-v2?
Can I use all-mpnet-base-v2 commercially?
What's the context length of all-mpnet-base-v2?
Source: huggingface.co/sentence-transformers/all-mpnet-base-v2
Reviewed by RunLocalAI Editorial. See our editorial policy for how we research and verify model claims.
Related — keep moving
Verify all-mpnet-base-v2 runs on your specific hardware before committing money.