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, similarity scoring, and text ranking.
Modalities
Price
$0.005per 1M tokens
Context
8K
Weekly Tokens
1.19B
Released
Nov 17, 2025
Create an API key from your OpenRouter dashboard and set it as an environment variable:
Use sentence-transformers/all-minilm-l6-v2 with the OpenRouter API:
OpenRouter provides an OpenAI-compatible embeddings API that you can call directly, or using the OpenAI SDK.
In the examples below, the OpenRouter-specific headers are optional. Setting them allows your app to appear on the OpenRouter leaderboards.
For information about using third-party SDKs and frameworks with OpenRouter, please see our frameworks documentation.
https://openrouter.ai/api/v1/embeddingsBearer $OPENROUTER_API_KEYapplication/jsonoptional — your site URL, for rankingsoptional — your site name, for rankingssentence-transformers/all-minilm-l6-v2| Name | Type | Default | Description |
|---|---|---|---|
max_tokens | integer | — | This sets the upper limit for the number of tokens the model can generate in response. |
temperature | float | 1 | This setting influences the variety in the model's responses. |
top_p | float | 1 | This setting limits the model's choices to a percentage of likely tokens: only the top tokens whose probabilities add up to P. |
stop | array | — | Stop generation immediately if the model encounter any token specified in the stop array. |
frequency_penalty | float | 0 | This setting aims to control the repetition of tokens based on how often they appear in the input. |
presence_penalty | float | 0 | Adjusts how often the model repeats specific tokens already used in the input. |
repetition_penalty | float | 1 | Helps to reduce the repetition of tokens from the input. |
top_k | integer | 0 | This limits the model's choice of tokens at each step, making it choose from a smaller set. |
seed | integer | — | If specified, the inferencing will sample deterministically, such that repeated requests with the same seed and parameters should return the same result. |
min_p | float | 0 | Represents the minimum probability for a token to be considered, relative to the probability of the most likely token. |
response_format | map | — | Forces the model to produce specific output format. |