LLM Reference
DeepInfra

Using DeepInfra Qwen1.5-72B-Chat on DeepInfra

Implementation guide · Qwen1.5 · Alibaba

ServerlessOpen Weights

DeepInfra exposes DeepInfra Qwen1.5-72B-Chat through model ID deepinfra-qwen1.5-72b-chat. Use the setup steps, sourced pricing, capabilities, and official provider links below to validate this route before deployment.

Last refreshed 2026-06-15. Next refresh: weekly.

Quick Start

  1. 1
    Create an account at DeepInfra and generate an API key.
  2. 2
    Use the DeepInfra SDK or REST API to call deepinfra-qwen1.5-72b-chat — see the documentation for request format.
  3. 3
    You'll be billed $0.45/1M input, $0.65/1M output tokens. See full pricing.
API PortalDocumentationPricing

Code Examples

Install
pip install openai
API key
DEEPINFRA_API_KEY
Model ID
deepinfra-qwen1.5-72b-chat

DeepInfra uses "organization/model-name" format, e.g. "meta-llama/Meta-Llama-3-8B-Instruct" or "mistralai/Mistral-7B-Instruct-v0.3". See the DeepInfra model catalog for exact IDs.

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["DEEPINFRA_API_KEY"],
    base_url="https://api.deepinfra.com/v1/openai"
)
response = client.chat.completions.create(
    model="deepinfra-qwen1.5-72b-chat",
    messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)

Pricing on DeepInfra

TypePrice (per 1M)
Input tokens$0.45
Output tokens$0.65

Capabilities

Structured Outputs

About DeepInfra Qwen1.5-72B-Chat

DeepInfra Qwen1.5-72B-Chat is Alibaba's Qwen1.5 model. It offers a 32K-token context window with weights openly available for self-hosting.

Full model details →

Model Specs

Released2024-02-04
Parameters72B
Context33k
ArchitectureDecoder Only

Provider

DeepInfra
DeepInfra

San Francisco, California, United States