Qwen2.5 VL 72B Instruct via OpenRouter
Specifications
Context Window
32,768 tokens
Release Date
2025-02-01
Capabilities
AttachmentsStructured outputTemperatureImage input
Availability
Open Weights
Model Overview
OpenRouter is a unified API gateway that provides access to hundreds of AI models from dozens of providers. It offers automatic fallback, price comparison, and a single API key for all models.
Qwen2.5 VL 72B Instruct is a qwen-family model by OpenRouter with a 33k token context window and up to 8k output tokens. It is priced at $0.00/1M input tokens and $0.00/1M output tokens.
Key capabilities include: attachments, structured output, temperature, image input.
Details
ProviderOpenRouter
Model IDqwen/qwen2.5-vl-72b-instruct
Familyqwen
Release Date2025-02-01
Last Updated2025-02-01
Knowledge Cutoff2024-10
Context Window32,768 tokens
Max Output8,192 tokens
Input Cost / 1M$0.00
Output Cost / 1M$0.00
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Frequently Asked Questions
How much does Qwen2.5 VL 72B Instruct cost to use?
Qwen2.5 VL 72B Instruct is priced at $0.00/1M input tokens and $0.00/1M output tokens. Use the cost estimator on this page to calculate your expected spend based on your usage pattern.
What is a token and how does it relate to pricing?
A token is a chunk of text — roughly ¾ of a word in English. For example, "chatbot" is two tokens. LLM API pricing is based on the number of tokens you send (input) and receive (output). Input tokens include your prompts, uploaded documents, and images, while output tokens are the model's generated responses.
Why are input and output tokens priced differently?
LLM providers charge separately for input and output tokens. Output tokens are typically more expensive because generating each token requires more compute — the model must run a full forward pass for every token it produces, while input tokens are processed in parallel.
What is the context window of Qwen2.5 VL 72B Instruct?
Qwen2.5 VL 72B Instruct supports a context window of 32,768 tokens. This is the maximum number of tokens (input + output combined) the model can process in a single request. Larger context windows let you send longer documents or maintain longer conversation histories.
How accurate is this cost estimation?
This tool provides a ballpark estimate based on per-token pricing. Actual costs may differ due to prompt caching, batched API calls, volume discounts, reasoning token overhead, and provider-specific billing rules. Use it for budgeting and comparison, not as an invoice prediction.
How does Qwen2.5 VL 72B Instruct pricing compare to other models?
You can compare Qwen2.5 VL 72B Instruct with other models on our LLM API pricing calculator. Use the cost estimator to see side-by-side cost breakdowns across different providers and models to find the best fit for your budget and requirements.
What factors affect my total API cost?
Your total cost depends on several factors: the number of API calls you make, the length of your prompts (input tokens), the length of generated responses (output tokens), whether you use features like image or document uploads (which add input tokens), and any provider-specific charges for caching or batch processing.
How can my team use Qwen2.5 VL 72B Instruct via API?
You can connect your own OpenRouter API key and give your entire team access to Qwen2.5 VL 72B Instruct through TypingMind Teams. It lets you build a unified AI workspace where team members can use Qwen2.5 VL 72B Instruct and other models — without needing their own API keys. You stay in control of usage limits, costs, and permissions, all from a single dashboard.
