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Rerank API
Send a query and a list of documents; get them back ordered by relevance with scores. Adding a rerank step after vector search is one of the cheapest ways to improve RAG answers.
- Better RAG context
- Search result ordering
- Matching tickets to answers
- Product search
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Rerank API · 10 models
| Model | Provider | Context | Price |
|---|---|---|---|
| NVIDIA: Nemotron 3.5 Rerank 8B (free)nvidia/nemotron-3.5-rerank-8b:free | NVIDIA | Free | |
| VoyageAI by MongoDB: rerank-3-litevoyageai/rerank-3-lite | Voyage AI | Free | |
| VoyageAI by MongoDB: rerank-3voyageai/rerank-3 | Voyage AI | Free | |
| Qwen3 Reranker 8Bqwen/qwen3-reranker-8b | Qwen | Free | |
| VoyageAI by MongoDB: rerank-2.5-litevoyageai/rerank-2.5-lite | Voyage AI | Free | |
| VoyageAI by MongoDB: rerank-2.5voyageai/rerank-2.5 | Voyage AI | Free | |
| NVIDIA: Llama Nemotron Rerank VL 1B V2 (free)nvidia/llama-nemotron-rerank-vl-1b-v2:free | NVIDIA | Free | |
| Cohere: Rerank 4 Procohere/rerank-4-pro | Cohere | Free | |
| Cohere: Rerank 4 Fastcohere/rerank-4-fast | Cohere | Free | |
| Cohere: Rerank v3.5cohere/rerank-v3.5 | Cohere | Free |
How to call the Rerank API API
- Create an API key in your dashboard.
- Install the official openai SDK for your language.
- Set baseURL to https://api14.com/api/v1 and model to "voyageai/rerank-2.5".
Where does rerank fit?
After a fast first-stage search (keyword or vector), rerank the top 20–100 candidates.
How is rerank priced?
Per search or per token, depending on the model. See the table below.
How many documents should I send?
Usually 20 to 100. More candidates improve recall but cost more and add latency.
import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://api14.com/api/v1",
apiKey: process.env.API14_API_KEY,
});
const result = await client.post("/rerank", { body: {
"model": "voyageai/rerank-2.5",
"query": "What is the capital of France?",
"documents": [
"Paris is the capital of France.",
"Berlin is in Germany."
],
"top_n": 1
} });
console.log(result.results);