sentence-transformers
Sentence Transformers: all-MiniLM-L6-v2
sentence-transformers/all-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/all-minilm-l6-v2 is an English text-embedding model that maps sentences and short paragraphs into 384-dimensional dense vectors, according to its documentation and the OpenRouter catalog. It was fine-tuned from MiniLM-L6-H384-uncased on more than one billion sentence pairs using a contrastive objective, and is released under the Apache-2.0 license. The sources describe it for semantic search, clustering, and sentence-similarity work, but this pack contains no measured benchmark results, so reported capabilities are not independently verified here. The OpenRouter listing reports a 512-token context length.[1][2]
Strengths
- Produces compact 384-dimensional sentence embeddings, which the documentation presents as capturing semantic information for downstream tasks.[1][2]
- Fine-tuned at scale: the documentation reports contrastive training on over one billion sentence pairs drawn from many public datasets.[2]
- Documented usage paths via the sentence-transformers library or standard HuggingFace Transformers with mean pooling, easing integration.[2]
Limitations
- The model card tags it as English only; behavior on other languages is undocumented in this evidence pack.[2]
- Length handling needs checking: the documentation says inputs beyond 256 word pieces are truncated by default and training used 128-token sequences, while the catalog lists a 512 context length.[1][2]
- Evidence is limited to a catalog entry and model documentation with no evaluation scores; validate quality and latency on your own workload before adopting.[1][2]
Where to use it
- A reasonable candidate for semantic search and information retrieval over short English texts, matching the intended uses stated in its documentation.[1][2]
- Potentially suitable for clustering or sentence-similarity features where compact vectors and English-only input are acceptable.[2]
- Could serve as a lightweight prototyping encoder for retrieval pipelines, subject to task-specific evaluation.[2]
Sources
- [1] OpenRouter catalog · Sentence Transformers: all-MiniLM-L6-v2Sources checked: Nov 13, 2026
- [2] Model documentation · Sentence Transformers: all-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 all-MiniLM-L6-v2 embedding model maps sentences and short paragraphs into a 384-dimensional dense vector space, enabling high-quality semantic representations that are ideal for downstream tasks such as information retrieval, clustering,...
Specifications
- Model ID
- sentence-transformers/all-minilm-l6-v2
- Canonical ID
- sentence-transformers/all-minilm-l6-v2-20251117
- Creator
- sentence-transformers
- Added to OpenRouter
- 17.11.2025
- 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 | — | — | 100% |
Provider data retrieved: 13.11.2026
Related articles
No published articles identify this model yet.