Qwen3-Reranker-0.6B

Private TEE

Qwen/Qwen3-Reranker-0.6B

The Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks.

Input / 1M tokens: $0.01
Output / 1M tokens: $0.01
Cache read / 1M tokens: —
Context window: 40K tokens

Privacy & execution

This model supports private inference inside a hardware-isolated Trusted Execution Environment (TEE), protecting data while it is processed. HTTPS protects data in transit; client-to-model end-to-end encryption depends on the model and integration.

Attestation provides cryptographic evidence of the execution environment’s identity and configuration. Where response signatures are available, you can also verify the integrity of the request and response.

Verification documentation

Make your first request

Call /v1/rerank over HTTPS.

Get an API key and replace the API key placeholder below.

curl --fail-with-body "https://cloud-api.near.ai/v1/rerank" \
  -H "Authorization: Bearer <YOUR-NEAR-AI-CLOUD-API-KEY>" \
  -H "Content-Type: application/json" \
  -d '{
  "model": "Qwen/Qwen3-Reranker-0.6B",
  "query": "What is confidential computing?",
  "documents": [
    "Processing data inside a trusted execution environment.",
    "A guide to mountain lakes."
  ],
  "top_n": 1
}'

Use the API

Use the dedicated endpoint for this model’s modality.

Example

const response = await fetch("https://cloud-api.near.ai/v1/rerank", {
  method: "POST",
  headers: {
    Authorization: "Bearer <YOUR-NEAR-AI-CLOUD-API-KEY>",
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
  "model": "Qwen/Qwen3-Reranker-0.6B",
  "query": "What is confidential computing?",
  "documents": [
    "Processing data inside a trusted execution environment.",
    "A guide to mountain lakes."
  ],
  "top_n": 1
}),
});
if (!response.ok) throw new Error(await response.text());
console.log(await response.json());