Artificial Intelligence is accelerating at an unprecedented pace, but so too are the costs and scaling challenges of running these powerful systems.
In early 2025, OpenAI faced one of the most unexpected performance stress tests: the viral Studio Ghibli prompt trend. Millions of users flooded the system, asking ChatGPT to generate dreamy, whimsical images styled like a Ghibli movie.
Sam Altman, the CEO of OpenAI tweeted about the load on their GPUs. But rather than buckle under the load, OpenAI handled it flawlessly by building a feature called Prompt Caching.
Combined with the new, highly optimized GPT-4.1 and GPT-4o models, Prompt Caching isn’t just a technical upgrade but it’s a transformative shift that reduces cost, latency, and stress on infrastructure. If you’re an AI engineer, product builder, or startup founder, understanding this innovation is essential.
What is Prompt Caching?
Prompt Caching is OpenAI’s newly introduced mechanism that reuses previously encoded segments of prompts, especially static parts like system messages or examples.
Instead of reprocessing the full prompt from scratch each time, OpenAI checks if the beginning of a new prompt (called the prefix) matches one it has recently seen. If it does, it loads a cached encoding instead of computing it again.
Benefits of Prompt Caching:
- Reduces latency by up to 80%
- Cuts costs by up to 50% for long prompts
- Works out-of-the-box, no changes required to your code
- Supports messages, tools, image inputs, and structured outputs
- Enabled automatically on GPT-4.1, GPT-4o, and newer models
How Prompt Caching Works
When you send a big prompt (1024 tokens or more), OpenAI tries to save time by remembering parts of it. Here’s what happens:
- Check the Memory (Cache Lookup)
OpenAI checks if it has seen the start of your prompt recently. - Found It! (Cache Hit)
If it’s already stored, it skips reprocessing and uses the saved version. - Not Found (Cache Miss)
If it’s new, it processes the full prompt and saves the start part for next time.
Prompt prefix usually stays cached for 5 to 10 minutes. However, during times when the system isn’t very busy (also called “quiet hours”), the cached data might stay around for up to 1 hour.
Prompt caching kicks in only if your prompt is 1024 tokens or more. After that, the cache system tracks in steps so the next caching point would be 1152 tokens, then 1280 tokens, then 1408, and so on. Each step increases by 128 tokens.
Caching works for more than just plain text. It also supports images, tool calls, system-level instructions, and even structured responses that follow a defined schema.
The Ghibli Prompt Craze: How Prompt Caching Prevented a Meltdown
During a few wild weeks in 2024, AI users became obsessed with generating Studio Ghibli-style images using ChatGPT and DALL·E. The prompts were long, stylized, and highly similar—”a cat riding a bicycle through a forest in Ghibli style,” “a Ghibli house by the lake at sunset,” and so on.
This surge pushed OpenAI’s systems to their limits, creating a scenario ripe for server overload. But instead of outages, users experienced fast responses and uninterrupted access.
What Worked:
- The repeated structure in “Ghibli-style” prompts made them perfect candidates for caching.
- OpenAI reused encoded prefix data across millions of requests.
- This reduced server load, avoided rate spikes, and cut costs across the board.
- Developers embedding ChatGPT in image generation tools reported cost reductions exceeding 50%.
The viral Ghibli trend served as a high-stress demo of caching done right, keeping latency low, and absorbing demand spikes with ease.
Does OpenAI API Cache Responses?
No, The OpenAI API does not cache or reuse generated responses. Every time you send a prompt, the model processes it and produces a new response, even if the input is exactly the same as a previous request. This ensures that responses can vary, reflect the latest model behavior, and support dynamic generation.
Does ChatGPT Use Cache?
Yes. While end-users don’t see it directly, ChatGPT internally uses the same caching layer. If you write similar prompts repeatedly, your experience will likely be faster because Prompts are being Cached on OpenAI Servers.
GPT-4.1 + Prompt Caching = Up to 75% Cost Reduction
If you’re building applications with OpenAI’s GPT-4.1, you’re already benefiting from a newer, more cost-efficient version of GPT-4. But when you combine this with OpenAI’s prompt caching feature, the savings multiply
Pricing for GPT-4.1 models
Pricing for GPT 4o models
GPT-4.1 is the latest iteration of OpenAI’s large language model, designed to be faster and less expensive to use compared to previous versions like GPT-4 Turbo. This means that for every thousand tokens (which roughly correspond to words or parts of words) that you send as input or receive as output, GPT-4.1 charges you roughly half the price of GPT-4 Turbo.
Now, on its own, GPT-4.1’s cheaper token prices are a big win. But OpenAI also introduced prompt caching support with GPT-4.1 models. If you’re sending a prompt with 2,000 tokens. Without caching and on an older model, you pay for all 2,000 tokens. With GPT-4.1’s new pricing, you pay roughly half. Now, if 80% of that prompt is repeated prefix text, prompt caching means you only pay for the remaining 400 tokens of new input. Together, this can reduce your input costs to about one-fifth or even one-quarter of what they were before. And since the output tokens are also cheaper on GPT-4.1, the overall bill for each request drops dramatically.
Who Benefits Most from Prompt Caching?
Prompt caching offers significant efficiency and cost savings, especially in applications where parts of the prompt remain consistent across multiple requests. Here are some scenarios and types of applications that benefit the most:
1. Standardized Instructions
- Examples: Chatbots, virtual assistants, automated agents
These applications often begin interactions with the same system prompt or follow consistent style guidelines. Because this introductory content doesn’t change, prompt caching can reuse the encoding of this repeated text, reducing redundant processing and cost.
2. High Volume Applications
- Examples: Public-facing apps, viral tools, large user-base services
Many users send prompts that share the same starting instructions or context. Prompt caching cuts down computational work and lowers costs by reusing these common parts, which is crucial for scaling to millions of requests.
3. Multi-Turn Conversations
- Examples: Customer support bots, conversational AI
Conversations often rely on stable system instructions or context that remain constant over multiple exchanges. Prompt caching reuses these fixed parts, speeding up responses and reducing token encoding costs.
4. AI-Powered Design and Image Generation Tools
- Examples: Image generation, style transfer tools
These tools frequently rely on consistent style or formatting instructions. Caching these repeated instructions improves efficiency by avoiding repeated processing.
Final Thoughts
The combination of Prompt Caching and GPT-4.1 changed how OpenAI handles demand surges and viral trends. The Ghibli prompt wave could have caused widespread slowdowns but instead, it validated a smarter approach to AI scaling.
From viral art prompts to enterprise-scale deployments, caching is no longer optional; it’s the key to making AI affordable and performant at scale.
