qwen/qwen3-vl-235b-a22b

Name: qwen/qwen3-vl-235b-a22b
Price: 0.5830 USD

Deployed here262K contextVisionTools

Qwen3 VL 235B, served on Amazon Bedrock.

qwen/qwen3-vl-235b-a22b

Call it from your code

Any OpenAI client works: point it at backend.alfnrl.io/v1 and send qwen/qwen3-vl-235b-a22b as the model. Add an X-Thread-Id header to keep a conversation going across models; the quickstart shows it.

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Details

Model id: qwen/qwen3-vl-235b-a22b
Vendor: qwen
Reached: Deployed on AlphaNeural GPUs
Context window: 262,144 tokens
Max output: 32,768 tokens
Input: text, image
Output: text
Capabilities: Vision, Tools

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Prices are what AlphaNeural AI bills, in USD, and can change when the upstream provider changes theirs. The catalogue is refreshed from the gateway every few minutes.

curl https://backend.alfnrl.io/v1/chat/completions \
  -H "Authorization: Bearer $ALPHANEURAL_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen/qwen3-vl-235b-a22b",
    "messages": [{"role": "user", "content": "Explain what a context window is, in two sentences."}]
  }'

import os
from openai import OpenAI

client = OpenAI(
    base_url="https://backend.alfnrl.io/v1",
    api_key=os.environ["ALPHANEURAL_API_KEY"],
)

reply = client.chat.completions.create(
    model="qwen/qwen3-vl-235b-a22b",
    messages=[{"role": "user", "content": "Explain what a context window is, in two sentences."}],
)
print(reply.choices[0].message.content)

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://backend.alfnrl.io/v1",
  apiKey: process.env.ALPHANEURAL_API_KEY,
});

const reply = await client.chat.completions.create({
  model: "qwen/qwen3-vl-235b-a22b",
  messages: [{ role: "user", content: "Explain what a context window is, in two sentences." }],
});
console.log(reply.choices[0].message.content);