sentence-transformers
Sentence Transformers: all-MiniLM-L12-v2
sentence-transformers/all-minilm-l12-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-l12-v2 is a text-to-embedding encoder documented as converting sentences and short paragraphs into 384-dimensional dense vectors. Its model card describes a MiniLM-L12-H384 base fine-tuned with a contrastive objective on more than one billion sentence pairs, while the OpenRouter catalog lists a 512-token context and embedding output. The documentation presents semantic information capture as an intended capability, not a measured result in this evidence pack. Inputs beyond 256 word pieces are truncated by default; independent workload validation remains necessary.[1][2]
Strengths
- Produces 384-dimensional sentence and short-paragraph embeddings, according to the model card and catalog.[1][2]
- Fine-tuned from MiniLM-L12-H384 on over one billion sentence pairs with a contrastive objective.[2]
- Documented for semantic search, clustering, information retrieval, and sentence similarity.[1][2]
Limitations
- Evidence supplies catalog and model-card claims, but no benchmark results or independent evaluations.[1][2]
- Inputs longer than 256 word pieces are truncated by default, despite the catalog's 512-token context listing.[1][2]
- The model card identifies language as English; multilingual performance is not established by the supplied evidence.[2]
Where to use it
- Prototype semantic search over short English passages, with retrieval quality tested on your corpus.[1][2]
- Cluster or deduplicate sentences and short paragraphs where 384-dimensional vectors fit the index.[2]
- Build sentence-similarity features after validating truncation behavior for representative inputs.[2]
Sources
- [1] OpenRouter catalog · Sentence Transformers: all-MiniLM-L12-v2Sources checked: Nov 13, 2026
- [2] Model documentation · Sentence Transformers: all-MiniLM-L12-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-L12-v2 embedding model maps sentences and short paragraphs into a 384-dimensional dense vector space, producing efficient and high-quality semantic embeddings optimized for tasks such as semantic search, clustering, and...
Specifications
- Model ID
- sentence-transformers/all-minilm-l12-v2
- Canonical ID
- sentence-transformers/all-minilm-l12-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 | — | — | 100% |
Provider data retrieved: 13.11.2026
Related articles
No published articles identify this model yet.