Model Cost Profile

Qwen: Qwen2.5-VL 7B Instruct

Developer: qwen

Pricing updated Mar 11, 2026

Input rank: #144Output rank: #73

Live Pricing

Input: $0.2000

Output: $0.2000

Pricing via OpenRouter API ยท Last synced Mar 11, 2026

Qwen2.5-VL 7B Instruct is designed for applications requiring advanced natural language understanding and generation, making it suitable for chatbots, content creation, and data analysis. With a context window of 32,768 tokens, this model can handle extensive conversations and complex documents, providing teams with the ability to maintain context over long interactions. At a competitive pricing of $0.20 per million tokens for both input and output, organizations can effectively manage costs while leveraging the model's capabilities for scalable AI solutions.

๐Ÿ‘ Vision

Context Window

32,768

Tokens

Input Price / 1M

$0.2000

Prompt tokens

Output Price / 1M

$0.2000

Completion tokens

Intelligence (MMLU)

Benchmark Pending

Massive Multitask Language Understanding

Price History

Qwen: Qwen2.5-VL 7B Instruct Pricing Trend

Input / 1M tokens0.0%Output / 1M tokens0.0%
Mar 7 โ€” Mar 11
$0.2000$0.2000$0.2000Mar 7Mar 8Mar 9Mar 10Mar 11

Current Input / 1M

$0.2000

Current Output / 1M

$0.2000

Cheaper Alternatives to Compare

Quick links for cost-down decisions before production rollout.

FAQ

Common pricing and benchmark questions for Qwen: Qwen2.5-VL 7B Instruct.

How much does Qwen: Qwen2.5-VL 7B Instruct cost per 1M input tokens?

Qwen: Qwen2.5-VL 7B Instruct input pricing is $0.2000 per 1M tokens based on the latest synced provider data.

How much does Qwen: Qwen2.5-VL 7B Instruct cost per 1M output tokens?

Qwen: Qwen2.5-VL 7B Instruct output pricing is $0.2000 per 1M tokens based on the latest synced provider data.

What context window does Qwen: Qwen2.5-VL 7B Instruct support?

Qwen: Qwen2.5-VL 7B Instruct supports a context window of 32,768 tokens.

How can I compare Qwen: Qwen2.5-VL 7B 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.