pplx-embed-v1 -4B is one of Perplexity's state-of-the-art text embedding models built for real-world, web-scale retrieval. pplx-embed-v1 is optimized for standard dense text retrieval with the 4B parameter model maximizing retrieval quality.
Modalities
Price
$0.03/M tokens
Context
32K
Weekly Tokens
7.34B
Released
Mar 16, 2026
Create an API key from your OpenRouter dashboard and set it as an environment variable:
Use perplexity/pplx-embed-v1-4b 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.
Submits an embedding request to the embeddings router
https://openrouter.ai/api/v1/embeddingsBearer $OPENROUTER_API_KEYapplication/jsonoptional — your site URL, for rankingsoptional — your site name, for rankingsperplexity/pplx-embed-v1-4b| 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. |
top_k | integer | 0 | This limits the model's choice of tokens at each step, making it choose from a smaller set. |
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. |
web_search_options | map | — | Configures native web search options for models and providers that support web-connected answers. |