Qwen3 32B AWQ

Latest generation LLM with advanced reasoning, instruction-following, and multilingual support.

Qwen3 32B AWQ is the latest large language model in the Qwen series, offering advancements in reasoning, instruction-following, agent capabilities, and multilingual support. It uses AWQ quantization for efficient inference while maintaining high quality.

Try in playground

Test Qwen3 32B AWQ in the Runpod Hub playground.

Endpoint	https://api.runpod.ai/v2/qwen3-32b-awq/runsync
Pricing	$10.00 per 1M tokens
Type	Text generation

This endpoint is fully compatible with the OpenAI API. See the OpenAI compatibility examples below.

Request

All parameters are passed within the input object in the request body.

input.prompt · string · required

Prompt for text generation.

input.max_tokens · integer · default: "512"

Maximum number of tokens to output.

input.temperature · float · default: "0.7"

Randomness of the output. Lower values make output more predictable and deterministic. Range: 0.0-1.0.

input.top_p · float

Nucleus sampling threshold. Samples from the smallest set of words whose cumulative probability exceeds this threshold.

input.top_k · integer

Restricts sampling to the top K most probable words.

input.stop · string

Stops generation if the given string is encountered.

cURL

curl -X POST "https://api.runpod.ai/v2/qwen3-32b-awq/runsync" \
  -H "Authorization: Bearer $RUNPOD_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "input": {
      "prompt": "Write a Python function that checks if a number is prime:",
      "max_tokens": 512,
      "temperature": 0.7
    }
  }'

Response

id · string

Unique identifier for the request.

status · string

Request status. Returns COMPLETED on success, FAILED on error.

delayTime · integer

Time in milliseconds the request spent in queue before processing began.

executionTime · integer

Time in milliseconds the model took to generate the response.

workerId · string

Identifier of the worker that processed the request.

output · object

The generation result containing the text and usage information.

output.choices · array

Array containing the generated text.

output.cost · float

Cost of the generation in USD.

output.usage · object

Token usage information with input and output counts.

200

{
  "delayTime": 25,
  "executionTime": 3153,
  "id": "sync-0f3288b5-58e8-46fd-ba73-53945f5e8982-u2",
  "output": [
    {
      "choices": [
        {
          "tokens": [
            "def is_prime(n):\n    if n <= 1:\n        return False\n    for i in range(2, int(n**0.5) + 1):\n        if n % i == 0:\n            return False\n    return True"
          ]
        }
      ],
      "cost": 0.0001,
      "usage": {
        "input": 10,
        "output": 100
      }
    }
  ],
  "status": "COMPLETED",
  "workerId": "pkej0t9bbyjrgy"
}

OpenAI API compatibility

Qwen3 32B AWQ is fully compatible with the OpenAI API format. You can use the OpenAI Python client to interact with this endpoint.

Python (OpenAI SDK)

from openai import OpenAI

client = OpenAI(
    api_key=RUNPOD_API_KEY,
    base_url="https://api.runpod.ai/v2/qwen3-32b-awq/openai/v1",
)

response = client.chat.completions.create(
    model="Qwen/Qwen3-32B-AWQ",
    messages=[
        {
            "role": "system",
            "content": "You are a helpful coding assistant.",
        },
        {
            "role": "user",
            "content": "Write a Python function that checks if a number is prime.",
        },
    ],
    max_tokens=525,
)

print(response.choices[0].message.content)

For streaming responses, add stream=True:

Python (Streaming)

response = client.chat.completions.create(
    model="Qwen/Qwen3-32B-AWQ",
    messages=[
        {"role": "user", "content": "Explain quantum computing in simple terms."}
    ],
    max_tokens=525,
    stream=True,
)

for chunk in response:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

For more details, see Send vLLM requests and the OpenAI API compatibility guide.

Cost calculation

Qwen3 32B AWQ charges $10.00 per 1M tokens. Example costs:

Tokens	Cost
1,000 tokens	$0.01
10,000 tokens	$0.10
100,000 tokens	$1.00
1,000,000 tokens	$10.00