Qwen3-VL-30B-A3B-Instruct

Confidential TEE

Qwen/Qwen3-VL-30B-A3B-Instruct

Qwen3-VL-30B-A3B-Instruct is a vision-language model supporting text and image inputs.

Input / 1M tokens: $0.15
Output / 1M tokens: $0.55
Cache read / 1M tokens: $0.03
Context window: 16K tokens

Privacy & execution

This model supports confidential 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/chat/completions over HTTPS.

Get an API key and set the NEARAI_API_KEY environment variable.

curl --fail-with-body "https://cloud-api.near.ai/v1/chat/completions" \
  -H "Authorization: Bearer $NEARAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "model": "Qwen/Qwen3-VL-30B-A3B-Instruct",
  "messages": [
    {
      "role": "user",
      "content": "Hello!"
    }
  ]
}'

This HTTP example uses HTTPS. Use the SDK below to add field-level end-to-end encryption and attestation checks.

Build and Verify with the SDK

Keep the OpenAI SDK interface. Let the inference client handle verified transport and message encryption.

Verification guide

NEAR AI Inference SDK is available for JavaScript (Node.js 24+) and Python (3.12+). Set NEARAI_API_KEY in your environment before running these examples.

npm install @nearai/inference-sdk openai

import OpenAI from "openai";
import { InferenceClient } from "@nearai/inference-sdk/node";

const apiKey = process.env.NEARAI_API_KEY;
if (!apiKey) throw new Error("Set NEARAI_API_KEY first.");

// Verify Gateway and model attestations, and encrypt supported fields to the model.
const inferenceClient = new InferenceClient({
  apiKey,
  baseUrl: "https://cloud-api.near.ai/v1",
  e2ee: true,
});

const openai = new OpenAI({
  apiKey,
  baseURL: inferenceClient.getBaseUrl(),
  fetch: inferenceClient.fetch,
});

const completion = await openai.chat.completions.create({
  model: "Qwen/Qwen3-VL-30B-A3B-Instruct",
  messages: [{ role: "user", content: "Hello!" }],
});

// Verify the exact request/response bytes before displaying the answer.
const verified = await inferenceClient.verifyResponse(completion.id);
console.log(completion.choices[0]?.message.content ?? "");
console.log("Verified signer:", verified.signatureKind);

See the JavaScript SDK guide or the Python SDK guide.