OpenRouter Vision: AI Large Language Models

Analyze images and answer visual questions with OpenRouter Vision — a multimodal LLM endpoint that processes both text and image inputs. Ideal for image understanding, OCR, and visual reasoning tasks. Try free — pay per generation, no subscription.

Interactive model controls

Any LLM is a versatile large language model for text generation, comprehension, and diverse NLP tasks such as chat and summarization. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

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Overview

About this model

Introducing openrouter-vision, a versatile large language model engineered to excel in a broad spectrum of natural language processing tasks. Designed for seamless text generation, chat, summarization, and comprehensive text-to-text transformation, openrouter-vision leverages best-in-class REST inference APIs to deliver exceptional performance without coldstarts. This model is built with user-centric efficiency in mind, enabling developers and enterprises to deploy robust NLP capabilities swiftly and cost-effectively.

Powered by cutting-edge technology and optimized for ultra-low latency, openrouter-vision stands out for its balance of speed, accuracy, and affordability. Its flexible API structure simplifies integration, while its competitive pricing of $0.025 per generation ensures that businesses can scale their operations without compromising on performance. Whether you need dynamic content creation, detailed summarizations, or interactive chat functionalities, openrouter-vision provides a reliable and scalable solution that meets modern digital demands.

1Automated customer support chatbots that provide real-time, context-aware assistance.

2Content creation for blogs, articles, and social media leveraging natural language understanding.

3Text summarization for long technical documents or news articles to ensure quick comprehension.

4Interactive virtual assistants that help users navigate websites or applications.

5Generating dynamic responses in conversational AI applications, including Q&A and dialogue systems.

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Pricing & Value

Cost analysis

Provider	Cost	Notes
muapiapp	$0.025	muapiapp is 20-50% more affordable than its competitors while delivering comparable or superior quality.
Fal.ai	$0.04	Pricing is nearly identical to Replicate, but muapiapp offers a 20-50% lower cost, making it a more cost-effective choice.
Replicate	$0.04	With pricing similar to Fal.ai, muapiapp stands out by providing significant cost savings while maintaining high performance.

muapiapp$0.025

muapiapp is 20-50% more affordable than its competitors while delivering comparable or superior quality.

Fal.ai$0.04

Pricing is nearly identical to Replicate, but muapiapp offers a 20-50% lower cost, making it a more cost-effective choice.

Replicate$0.04

With pricing similar to Fal.ai, muapiapp stands out by providing significant cost savings while maintaining high performance.

** Competitor pricing is estimated based on similar model architectures and usage tiers.

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Technical Details

Configuration schema

Parameter	Type	Description	Default
Prompt	string	The prompt to generate the response	`Caption this image for a text-to-image model with as much detail as possible.`
Image URLs	array	Upload or provide image urls. Used for image-to-video generation.	`https://d3adwkbyhxyrtq.cloudfront.net/webassets/videomodels/openrouter-vision.jpg`
System Prompt	string	System prompt to provide context or instructions to the model.	`Only answer the question, do not provide any additional information or add any prefix/suffix other than the answer of the original question. Don't use markdown.`
Model	Enum (5 options)	Name of the model to use. Premium models are charged at 10x the rate of standard models, they include: deepseek/deepseek-r1, google/gemini-pro-1.5, openai/gpt-4.1, anthropic/claude-3-5-haiku, openai/gpt-4o, anthropic/claude-3.5-sonnet, openai/o3, meta-llama/llama-3.2-90b-vision-instruct, anthropic/claude-3.7-sonnet, openai/gpt-5-chat.	`google/gemini-2.5-flash`
Reasoning	boolean	Should reasoning be the part of the final answer.	`false`
Temperature	int	This setting influences the variety in the model's responses. Lower values lead to more predictable and typical responses, while higher values encourage more diverse and less common responses. At 0, the model always gives the same response for a given input.	`1`
Max Tokens	int	This sets the upper limit for the number of tokens the model can generate in response. It won’t produce more than this limit. The maximum value is the context length minus the prompt length.	`null`

Promptstring

The prompt to generate the response

Default ValueCaption this image for a text-to-image model with as much detail as possible.

Image URLsarray

Upload or provide image urls. Used for image-to-video generation.

Default Valuehttps://d3adwkbyhxyrtq.cloudfront.net/webassets/videomodels/openrouter-vision.jpg

System Promptstring

System prompt to provide context or instructions to the model.

Default Value

Only answer the question, do not provide any additional information or add any prefix/suffix other than the answer of the original question. Don't use markdown.

ModelEnum (5 options)

Name of the model to use. Premium models are charged at 10x the rate of standard models, they include: deepseek/deepseek-r1, google/gemini-pro-1.5, openai/gpt-4.1, anthropic/claude-3-5-haiku, openai/gpt-4o, anthropic/claude-3.5-sonnet, openai/o3, meta-llama/llama-3.2-90b-vision-instruct, anthropic/claude-3.7-sonnet, openai/gpt-5-chat.

Default Valuegoogle/gemini-2.5-flash

Reasoningboolean

Should reasoning be the part of the final answer.

Default Valuefalse

Temperatureint

This setting influences the variety in the model's responses. Lower values lead to more predictable and typical responses, while higher values encourage more diverse and less common responses. At 0, the model always gives the same response for a given input.

Default Value1

Max Tokensint

This sets the upper limit for the number of tokens the model can generate in response. It won’t produce more than this limit. The maximum value is the context length minus the prompt length.

Default Valuenull

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Implementation Guide

Developer documentation

How to Use openrouter-vision

Step 1: Prepare Your Input

Construct a JSON payload with the required fields such as prompt and images_list.
Optionally include fields like system_prompt, model, reasoning, temperature, and max_tokens to fine-tune the response.

Step 2: Send the Request

Use the provided REST inference API endpoint by making an HTTP POST request with your JSON payload.
Ensure that your API call adheres to the defined schema for seamless interaction.

Step 3: Interpret the Results

Once the API responds, parse the JSON output which will include the generated text.
Review the output to ensure it meets your application needs and adjust the input parameters if necessary.

Step 4: Iterate and Optimize

Experiment with different prompts and configuration settings (such as temperature and max tokens) to achieve the best results.
Continuously monitor performance for quality assurance and make iterative improvements as needed.

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Common Questions

Frequently asked

What makes openrouter-vision unique compared to other LLMs?

openrouter-vision is optimized for a variety of NLP tasks with a focus on speed and cost-efficiency. Its ready-to-use REST inference API and no coldstart performance make it an attractive option for developers needing reliable and scalable solutions.

How do I format my input for the API?

Inputs should be structured in a JSON format as specified by the technical input schema. Key fields include 'prompt' and 'images_list', while you can also provide optional parameters like 'system_prompt', 'model', 'reasoning', 'temperature', and 'max_tokens' to tailor the response.

What is the pricing model for openrouter-vision?

The model is competitively priced at $0.025 per generation, making it a cost-effective solution compared to similar offerings in the market.

Can openrouter-vision handle real-time applications?

Absolutely. Its architecture is designed to provide swift responses with minimal latency, making it suitable for real-time applications such as chatbots and interactive assistants.