sentence-transformers
Sentence Transformers: paraphrase-MiniLM-L6-v2
sentence-transformers/paraphrase-minilm-l6-v2
Our take
AI-assisted analysis based on the sources below. Suitability is an editorial assessment; test it on your own workload.
sentence-transformers/paraphrase-minilm-l6-v2 is a text-to-embeddings model in the sentence-transformers family. Its model card says it maps sentences and paragraphs to 384-dimensional dense vectors for clustering and semantic search, while the OpenRouter catalog describes the same embedding space as suited to paraphrase detection, semantic similarity, and lightweight retrieval. It supports text input and embedding output. The card documents Apache-2.0 licensing and sentence-transformers or Hugging Face Transformers usage; no measured benchmark results are supplied.[1][2]
Strengths
Limitations
- The supplied sources contain no benchmark scores or measured quality, latency, or throughput results.[1][2]
- OpenRouter lists a 512-token context length, but the model card architecture reports max_seq_length 128; the effective input limit needs verification.[1][2]
- It outputs embeddings rather than text, so it does not by itself perform generation or return an answer.[1][2]
Where to use it
Sources
- [1] OpenRouter catalog · Sentence Transformers: paraphrase-MiniLM-L6-v2Sources checked: Nov 13, 2026
- [2] Model documentation · Sentence Transformers: paraphrase-MiniLM-L6-v2Sources checked: Nov 13, 2026
About this model
Description below is the original text supplied by OpenRouter, shown in the source language.
The paraphrase-MiniLM-L6-v2 embedding model converts sentences and short paragraphs into a 384-dimensional dense vector space, producing high-quality semantic embeddings optimized for paraphrase detection, semantic similarity scoring, clustering, and lightweight retrieval...
Specifications
- Model ID
- sentence-transformers/paraphrase-minilm-l6-v2
- Canonical ID
- sentence-transformers/paraphrase-minilm-l6-v2-20251117
- 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.