NEW OpenRouter Update is INSANE!

New OpenRouter update is insane. OpenRouter just dropped something on April 30th that changes how you use AI tools completely. It's called response caching, and once you understand what it actually does, you'll want to turn this on immediately. Here's the setup.

Every time you send a request to an AI model through OpenRouter, it hits the model, processes everything, and sends back a response. That takes time. For GPT 5.5, that's around 9.1 seconds per request. For KimiK2, it's around 4.6 seconds.

Even Gemini 2.5 Flash, which is fast, still clocks in around 1.3 seconds. OpenRouter plus two. Now multiply that across dozens of requests. Testing.

Retrying. Workflows. That adds up fast. Response caching fixes this.

When you add a single header, xOpenRouterCache, true. To your requests, OpenRouter stores the full response. Every identical request after that comes back instantly. Zero tokens billed.

No charge at all. OpenRouter. How fast? Cached responses return in 80 to 300 milliseconds.

The cache lookup itself averages 4ms OpenRouter. So instead of waiting almost 10 seconds for a GPT 5.5 response every single time, you wait once. Everything after that? Instant.

This is a big shift. And most people aren't talking about it yet. Hey, if we haven't met already, I'm the digital avatar of Julian Goldie, CEO of SEO agency Goldie Agency. Whilst he's helping clients get more leads and customers, I'm here to help you get the latest AI updates.

Julian Goldie reads every comment, so make sure you comment below. Tell me, what's the first thing you'd want AI to do for you automatically? Drop it down there. Let's get into it.

Let me show you exactly how it works under the hood, because that's where it gets really interesting. When you send a request with caching enabled, OpenRouter hashes the request body, the model you're using, your API key, and whether you're streaming or not. That all becomes a cache key. If an identical request was made before and hasn't expired, the cached response comes back immediately.

No provider gets called. No tokens get consumed. No charge. OpenRouter.

Think of it like this. You ask the same question at a restaurant. First time, the chef cooks it fresh. Takes 10 minutes.

But if you order the exact same thing again, they already have it ready. You get it in seconds. That's literally what this does, but for AI. Works for text, images, audio, documents, and tool calls.

Works for streaming and non-streaming requests. The only thing that doesn't get cached is errors and partial outputs. Only successful responses get stored. Now, here's something important.

A lot of people confuse this with prompt caching. They're completely different things. Prompt caching, which already existed, saves on the input side. If you're sending the same system prompt over and over, some providers will cache that prefix, so you're not paying to process it again each time.

That still calls the model. You still get a fresh output. You still pay for the completion. Response caching skips the provider entirely.

The full response comes back from OpenRouter's edge cache. Nothing hits the model at all. These two can actually work together. You can have prompt caching reducing your input costs on the provider side, while response caching eliminates costs entirely for repeated identical requests on OpenRouter's side.

So when does this actually matter in the real world? Let's say you're running the AI Profit Boardroom's onboarding flow. New members come in, get hit with the same welcome automation, same FAQ responses, same intro content. Without caching, every single one of those AI calls is hitting the model fresh.

With response caching turned on, the first member triggers the generation. Everyone after that gets the same response instantly. You built the automation once, now it just runs fast and free. Or think about testing AI workflows.

You're building something. You tweak one part, run it again. Half the steps are identical to last time. Without caching, you pay for everything again.

With caching, subsequent runs are deterministic, and free after the first run populates the cache. So that's huge if you're iterating fast. You can also control how long responses stay cached. Use XOpenRouterCacheTTL to set a time anywhere from 1 second up to 24 hours.

Default is 5 minutes. And if you need a fresh response at any point, just send XOpenRouterCacheClear true to bus the cache for that specific request. There's also a preset option. If you set CacheEnabled true in a preset config, caching automatically applies to every request using that preset.

No header needed on individual requests. That's useful if you're running a consistent AI workflow and you don't want to remember to add the header every time. Response headers tell you exactly what happened too. You'll see XOpenRouterCacheStatus hit or miss plus the age of the cache and the TTL.

So you always know whether you got a cached response or a fresh one. By the way, if you want to go deep on how to actually implement OpenRouter into your business workflows, how to set up automations that run in the background, and how to build AI systems that save you serious time, the AI Profit Boardroom is where to be. We've got step-by-step tutorials specifically on setting up AI automation with tools like OpenRouter, four live coaching calls every week, where you can ask questions about your actual setup, a 30-day roadmap for getting your first automations running, and 2,800 business owners already in their building with these exact tools. We also have prompts for everything and a member map so you can connect with other people near you who are building the same things.

Link in the comments and description or go to AIProfitBoardroom.com. Back to the update. One caveat worth mentioning. If two identical requests arrive at the exact same time before the first one has been written to cache, both result in a cache miss and a build independently.

There's no request coalescing. So if you're hammering requests simultaneously at the exact same millisecond, you might get charged for both. Practice, this is rare, but worth knowing. Also, very large multimodal payloads that get offloaded internally for processing aren't eligible for caching.

Standard size requests cache fine. It's just the massive edge case payloads where this doesn't apply. And this feature is currently in beta. The API and behavior may change.

So keep an eye on the docs. But from what's already live, it's working and it's working well. Let's zoom out for a second. Pemruta already solved one major problem, giving you access to 300 plus AI models through a single API.

You don't have to manage separate keys for Claude, GPT, Gemini, Kimi. One place, one key, everything. Now they're solving the infrastructure layer on top of that. Liability, cost efficiency, response caching is part of that.

And it points to where AI tooling is heading. The raw model performance gap between providers is narrowing. The infrastructure layer, how fast, how reliable, how cheap is becoming the actual differentiator. Pemruta is building that layer.

Response caching is a clear signal they're serious about it. For anyone building AI workflows right now, this is worth turning on today. Because every repeated request you send without this is a wasted call. Your content automation, turn caching on.

Your client onboarding AI flows, turn caching on. Anything where the same input triggers the same output repeatedly, turn caching on. The real unlock here is that it makes AI workflows more predictable. Faster responses, system outputs, and you're only paying for the genuinely new work your automations are doing.

Here's what to do right now. Go to OpenRouter.ai. If you're already using it, add X OpenRouter cache, true to any API request where you're likely to send identical inputs. If you're using presets, flip cache enabled, true in your config.

Check your response headers to confirm you're getting cache hits. If you're not using OpenRouter yet, this is a good reason to start. One unified API, access to every major model, and now a caching layer that makes your AI workflows faster and more efficient with one line of code. For anyone who wants the full step-by-step on how to integrate OpenRouter into real business automations, not just the technical setup, but the actual use cases, the workflow design, the prompts, join us in the AI Profit Boardroom.

We've built out tutorials specifically on OpenRouter, how to connect it to automation tools, how to design workflows that take advantage of caching, and how to scale your AI systems without everything getting slow or expensive. Four coaching calls every week. Library of prompts built around tools like this. Members already automating their businesses with AI.

Roadmap so you know exactly what to set up in your first 30 days. And people in the community 24-7 because there's always someone online. Link in the comments and description or go to AIProfitBoardroom.com. And if you want the full notes from this video, all the links, the exact headers and config settings, plus access to 67,000 members building with AI, join the AI Success Lab.

It's free. Link in the comments and description.