Image Understanding
Accepts image inputs alongside text prompts, enabling the model to answer questions about, describe, or extract information from photographs, diagrams, and other visual content.
OpenAI
GPT-4o Vision
GPT-4o Vision is a variant of OpenAI's GPT-4o model that accepts both text and image inputs, allowing it to analyze visual content and respond to questions about it. Developed by OpenAI and added to MindStudio in June 2024, it supports a 128,000-token context window and has a training data cutoff of October 2023. The model addresses a historical limitation of language models, which traditionally processed only text, by enabling multimodal input handling within a single system. GPT-4o Vision is well suited for tasks that require interpreting images alongside text, such as describing visual content, answering questions about photographs or diagrams, extracting information from images, and supporting workflows where visual and textual data appear together. Because it shares the GPT-4o architecture, it handles natural language tasks in addition to vision tasks without requiring a separate model. Developers building applications that involve document analysis, image-based Q&A, or mixed-media content can use this model through the OpenAI API.
Unknown
128,000 context
4,096 tokens output
Image Understanding
Long Context Window
Fast Inference
Multimodal Input
Natural Language Generation
Model Overview
High-signal model metadata in a structured two-column overview table.
Provider
The entity that provides this model.
OpenAI
Input Context Window
The number of tokens supported by the input context window.
128,000
tokens
Maximum Output Tokens
The number of tokens that can be generated by the model in a single request.
4,096 tokens
tokens
Open Source
Whether the model's code is available for public use.
No
Release Date
When the model was first released.
Unknown
Knowledge Cut-off Date
When the model's knowledge was last updated.
Unknown
API Providers
The providers that offer this model. This is not an exhaustive list.
OpenAI API
Modalities
Types of data this model can process.
Text
Image
What is GPT-4o Vision
A fuller summary of positioning, capabilities, and source-specific details for GPT-4o Vision.
GPT-4o Vision is a variant of OpenAI's GPT-4o model that accepts both text and image inputs, allowing it to analyze visual content and respond to questions about it. Developed by OpenAI and added to MindStudio in June 2024, it supports a 128,000-token context window and has a training data cutoff of October 2023. The model addresses a historical limitation of language models, which traditionally processed only text, by enabling multimodal input handling within a single system.
GPT-4o Vision is well suited for tasks that require interpreting images alongside text, such as describing visual content, answering questions about photographs or diagrams, extracting information from images, and supporting workflows where visual and textual data appear together. Because it shares the GPT-4o architecture, it handles natural language tasks in addition to vision tasks without requiring a separate model. Developers building applications that involve document analysis, image-based Q&A, or mixed-media content can use this model through the OpenAI API.
Capabilities
What GPT-4o Vision supports
Long Context Window
Supports up to 128,000 tokens per request, allowing large amounts of text and image data to be included in a single prompt.
Fast Inference
Tagged as FAST in the MindStudio catalog, indicating the model is optimized for lower-latency responses relative to heavier reasoning variants.
Multimodal Input
Processes combined text and image inputs in a single request, removing the need to route visual and textual content through separate models.
Natural Language Generation
Produces fluent text responses to both text-only and image-accompanied prompts, supporting tasks like summarization, Q&A, and content description.
Pricing for GPT-4o Vision
Primary API pricing shown in the same “quick compare” spirit as the reference page.
Input tokens
$2.50
Per million tokens
Output tokens
N/A
Per million tokens
Price Comparison
Additional usage-cost dimensions synced into the project for this model.
maxTemperature
2
maxResponseSize
4,096 tokens
API Access & Providers
Places where this model is available, based on the synced detail-page metadata.
OpenAI API
Configuration & Parameters
The configurable options currently documented for this model.
Temperature
NumberMax Response Tokens
NumberSupported Request Parameters
Parameters currently listed by OpenRouter or the local catalog for this model.
Model Performance
Benchmark scores synced from the current model source and normalized into the local catalog.
| Benchmark | Score |
|---|---|
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AIME 2024
American math olympiad problems
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GPQA Diamond
PhD-level science questions (biology, physics, chemistry)
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HLE
Questions that challenge frontier models across many domains
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LiveCodeBench
Real-world coding tasks from recent competitions
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MATH-500
Undergraduate and competition-level math problems
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MMLU-Pro
Expert knowledge across 14 academic disciplines
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SciCode
Scientific research coding and numerical methods
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Resources & Documentation
Official model cards, release notes, docs, and other references synced from the source page.
Community discussion
What people think about GPT-4o Vision
GPT-4o Vision discussions are most active in r/ChatGPT, r/SideProject, r/shortcuts. Top Reddit threads cluster around benchmark and model-comparison threads.
The strongest match in this snapshot has 52 upvotes and 31 comments.
Checking return requests manually is a soul-crushing task for e-commerce owners. I decided to build an autonomous 'Inspector' to handle it.
**The Workflow:**
1. **Visual Trigger:** Customer uploads a photo via the return form (Webhook).
2. **AI Inspection:** A Vision node analyzes the image. I use a specific prompt to distinguish between 'Actual Damage' and 'Packaging Wear'.
3. **Database Check:** It cross-references the customer's lifetime value in my Postgres DB.
4. **Decision:** \> - If High-Value Customer + Confirmed Damage: Auto-approve & send shipping label.
* If Low-Value + Unclear Image: Ping me on Slack for a manual check.
**The Result:** I'm now auto-approving 70% of requests without touching a keyboard.
**Question:** How do you guys handle 'image spoofing' or fraud in visual automations? I'm thinking of adding a metadata check to see if the photo was actually taken recently.
"I've been building text-based agents for a while, but this week I experimented with **Vision** workflows for an e-commerce client.
We wanted to automate 'Damaged Product' returns without waiting for a human support agent to look at photos.
**The Workflow:**
1. **Trigger:** Customer uploads a photo to the return form.
2. **Vision Node:** n8n sends the image to GPT-4o with a specific prompt: *"Act as a quality inspector. Is the product in this image damaged? Return JSON: { is\_damaged: boolean, severity\_score: 1-10, description: '...' }"*
3. **Decision:**
* **Score > 8:** Auto-issue refund instantly.
* **Score < 3:** Flag as 'Potential Fraud' for manual review.
**The Result:** It filters out 60% of obvious cases instantly.
**Question:** For those using Vision models in production, do you resize/compress images before sending them to OpenAI to save on tokens? Or do you just send the raw HD file?"
Hey everyone,
I’ve been working on a tool to audit landing pages. Until yesterday, it analyzed text and copy quite well, but it was "blind" to the actual design and UX.
I just pushed an update using **GPT-4o Vision**, so now the AI actually takes a screenshot and analyzes the visual hierarchy, whitespace, and cognitive load.
To test it, I tried it my own product’s landing page (which I thought was decent). **The result was brutal (see the image):**
* ❌ **"Call to Action: Absent":** It said my phrases were vague and lacked clarity.
* ❌ **"Urgency Factors: Absent":** It pointed out I gave no reason for users to act *now*.
* ❌ **"First Impression":** It told me my color contrast was "muted" and failed the blink test.
It was painful to read, but honestly, it was right.
**I want to stress-test the new vision capabilities on different design styles.**
Drop your URL in the comments. I’ll run a full audit (**Visuals + Copy + Trust + Conversion**) for the first 10 people and reply with the report link.
No strings attached. Just looking for feedback on whether the AI's design advice is actually helpful or just hallucinating.
# Update (2024/12/18)
I have turned this shortcut into an iOS app: [https://apps.apple.com/us/app/visual-intent-schedule-by-ai/id6737428676](https://apps.apple.com/us/app/visual-intent-schedule-by-ai/id6737428676)
\------------------------------
I'm tired of manually adding events displayed on the screen to my calendar. So I build this iOS shortcut, Snap2Schedule, to help me do that with GPT-4o vision.
Just double-tap the back of your phone or use the Action button, and Snap2Schedule will:
* Capture a screenshot of your current screen.
* Use the power of GPT-4o vision to intelligently identify any scheduling information.
* Seamlessly add the event to your calendar app.
I've tested it with meeting invitation in Email, movie ticket, doctor's appointment, and it works amazingly great
[Example](https://preview.redd.it/ae1g5hurhkee1.png?width=1280&format=png&auto=webp&s=38fcc073ff585f6ea947628d85d48fd4b423b85a)
Shortcut link (v2): [https://www.icloud.com/shortcuts/d96703a5f15c4fb9b0df0d76ce61775d](https://www.icloud.com/shortcuts/d96703a5f15c4fb9b0df0d76ce61775d)
You can learn more about the shortcut in the Gumroad page: [https://wong2.gumroad.com/l/snap2schedule](https://wong2.gumroad.com/l/snap2schedule)
OpenAI claims that *GPT-4o* is much better at image recognition than *GPT-4-turbo*. I wanted to see how it fares with screenshots of Web UI pages. I see two potential goals here that I'm interested in:
1. Accessibility
2. Automated web testing (to replace frameworks like Selenium or Cypress)
I made a YouTube video showing some of the tests I did: [https://www.youtube.com/watch?v=ZFzBDPpeP04](https://www.youtube.com/watch?v=ZFzBDPpeP04)
However, the conclusion is that both *GPT-4o* and *GPT-4-turbo* are equally bad at this task. I couldn't see any kind of improvement with *GPT-4o*. It's a bit disappointing, because when I test it with regular photos, both models are very good. But they really struggle with details on Web UI screenshots.
I'm curious of other people tried similar use cases and what were your results.
FAQ
Common questions about GPT-4o Vision
What is the context window for GPT-4o Vision?
GPT-4o Vision supports a context window of 128,000 tokens, which can include both text and image content within a single request.
What is the knowledge cutoff date for this model?
The model's training data has a cutoff of October 2023, meaning it does not have knowledge of events or information published after that date.
What types of inputs does GPT-4o Vision accept?
The model accepts both text and image inputs, allowing users to submit images alongside natural language prompts for analysis or Q&A.
Who publishes GPT-4o Vision?
GPT-4o Vision is published by OpenAI and is accessible through the OpenAI API as well as through MindStudio.
What kinds of tasks is GPT-4o Vision suited for?
It is suited for tasks that involve visual content interpretation, such as describing images, answering questions about diagrams or photos, and extracting information from image-based documents.
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