Model Cost Profile

Qwen2.5 72B Instruct

Developer: qwen

Pricing updated Mar 11, 2026

Input rank: #103Output rank: #97

Live Pricing

Input: $0.1200

Output: $0.3900

Pricing via OpenRouter API ยท Last synced Mar 11, 2026

Qwen2.5 72B Instruct is designed for advanced natural language processing tasks, making it suitable for applications such as chatbots, content generation, and data analysis. With a context window of 32,768 tokens, this model excels in handling extensive dialogues and complex queries, allowing teams to maintain context over longer interactions. Pricing for the API is competitive, with an input cost of $0.12 per million tokens and an output cost of $0.39 per million tokens, making it a cost-effective choice for organizations requiring scalable language solutions.

๐Ÿ”ง Tool Calling๐Ÿ“‹ Structured Output

Context Window

32,768

Tokens

Input Price / 1M

$0.1200

Prompt tokens

Output Price / 1M

$0.3900

Completion tokens

Intelligence (MMLU)

Benchmark Pending

Massive Multitask Language Understanding

Price History

Qwen2.5 72B Instruct Pricing Trend

Input / 1M tokens0.0%Output / 1M tokens0.0%
Mar 7 โ€” Mar 11
$0.1200$0.2550$0.3900Mar 7Mar 8Mar 9Mar 10Mar 11

Current Input / 1M

$0.1200

Current Output / 1M

$0.3900

Cheaper Alternatives to Compare

Quick links for cost-down decisions before production rollout.

FAQ

Common pricing and benchmark questions for Qwen2.5 72B Instruct.

How much does Qwen2.5 72B Instruct cost per 1M input tokens?

Qwen2.5 72B Instruct input pricing is $0.1200 per 1M tokens based on the latest synced provider data.

How much does Qwen2.5 72B Instruct cost per 1M output tokens?

Qwen2.5 72B Instruct output pricing is $0.3900 per 1M tokens based on the latest synced provider data.

What context window does Qwen2.5 72B Instruct support?

Qwen2.5 72B Instruct supports a context window of 32,768 tokens.

How can I compare Qwen2.5 72B Instruct with cheaper alternatives?

Use the comparison links on this page to open direct model-vs-model pricing and benchmark pages, then evaluate monthly spend projections for your workload.