sentence-transformers
Sentence Transformers: multi-qa-mpnet-base-dot-v1
sentence-transformers/multi-qa-mpnet-base-dot-v1
Our take
AI-assisted analysis based on the sources below. Suitability is an editorial assessment; test it on your own workload.
OpenRouter and the model card describe sentence-transformers/multi-qa-mpnet-base-dot-v1 as a text-to-embeddings sentence-transformers model that maps sentences and paragraphs to a 768-dimensional dense vector space for semantic search. The card says it was fine-tuned on about 215M question-answer pairs from diverse sources, uses CLS pooling, and is scored with dot product rather than normalized cosine similarity. No benchmark results are included in this pack.[1][2]
Strengths
- Source-reported: produces 768-dimensional sentence and paragraph embeddings aimed at semantic search and QA retrieval.[1][2]
- The card reports fine-tuning on roughly 215M question-answer pairs drawn from varied datasets.[2]
- Documented usage covers sentence-transformers, Hugging Face Transformers, and Text Embeddings Inference.[2]
Limitations
- Inputs are limited to 512 word pieces and the card notes training used text up to 250 word pieces, so longer passages may degrade.[1][2]
- The card says embeddings are not normalized and recommends dot-product scoring, so cosine-normalized score expectations should not be assumed.[2]
- Evidence is catalog plus model card only: no measured retrieval benchmark results were supplied, so validate quality on the target workload.[1][2]
Where to use it
- Editorial fit: semantic search matching questions or queries to candidate passages, the use stated by the model card.[2]
- Possible fit: FAQ or QA retrieval and duplicate-question style matching, reflecting the QA-pair training mix described by the source.[2]
- Possible fit: similarity scoring for short texts within documented length limits, not long-document retrieval without workload testing.[1][2]
Sources
- [1] OpenRouter catalog · Sentence Transformers: multi-qa-mpnet-base-dot-v1Sources checked: Nov 13, 2026
- [2] Model documentation · Sentence Transformers: multi-qa-mpnet-base-dot-v1Sources checked: Nov 13, 2026
About this model
Description below is the original text supplied by OpenRouter, shown in the source language.
The multi-qa-mpnet-base-dot-v1 embedding model transforms sentences and short paragraphs into a 768-dimensional dense vector space, generating high-quality semantic embeddings optimized for question-and-answer retrieval, semantic search, and similarity-scoring across diverse content.
Specifications
- Model ID
- sentence-transformers/multi-qa-mpnet-base-dot-v1
- Canonical ID
- sentence-transformers/multi-qa-mpnet-base-dot-v1-20251117
- Creator
- sentence-transformers
- Added to OpenRouter
- 18.11.2025
- Context length
- 512
- Modality
- text->embeddings
- Input modalities
- text
- Output modalities
- embeddings
- Token pricing
- USD per 1M tokens: in $0.0050 / out $0
- Knowledge cutoff
- —
- Expiration date
- —
Tasks and capabilities
A grade appears only where the source supplies a matching measurement for that task, the model is active and the cohort holds at least 10 comparable models. The capability column lists facts the source declares — supported formats, reasoning and tool parameters — which are not measured quality.
| Task | Grade | Source-declared capability |
|---|---|---|
| General quality | No measurement | — |
| Coding | No measurement | — |
| Agents | No measurement | — |
| Writing | No measurement | — |
| Reasoning | No measurement | — |
| Document analysis | No measurement | — |
| Vision | No measurement | — |
| Image generation | No measurement | — |
| Audio | No measurement | — |
| Video | No measurement | — |
| Embeddings | No measurement | output: embeddings |
Evaluations
No evaluations are supplied for this model.
How grades work
Each letter grade compares a model only against other models scored by the same source and metric in the current snapshot. S means the top 10%, A the next 20%, B the next 30%, C the rest. Models without a published measurement are shown as not rated — a supported format alone never implies measured quality.
- S — S — top 10% of the cohort
- A — A — next 20% of the cohort
- B — B — next 30% of the cohort
- C — C — below the B band
Providers
Optional live provider statistics from OpenRouter. Missing values are unknown, not zero.
| Provider | Context length | Price in /1M | Price out /1M | Latency p50 (30m, s) | Throughput p50 (30m, tok/s) | Uptime (30m) |
|---|---|---|---|---|---|---|
| DeepInfra | 512 | $0.0050 | $0 | — | — | — |
Provider data retrieved: 13.11.2026
Related articles
No published articles identify this model yet.