GPT-5 on Azure: Token Limits Explained

8/12/2025

GPT-5 on Azure & Its Token Limits: Are They a Bug or a Feature?

Hey everyone, let's talk about something that's been on the minds of a lot of developers & AI enthusiasts lately: the token limits for GPT-5 on Azure OpenAI. If you've been lucky enough to get your hands on it, you might have noticed some interesting behaviors, especially when you're pushing the context window to its limits. The big question is, are the low token limits we're sometimes seeing a bug, or is this an intentional restriction?

Honestly, the answer is a bit of both, & it’s a pretty fascinating look into the current state of large language models. Let's dive in.

The Official Word on GPT-5 Token Limits on Azure

First things first, let's get the official numbers out of the way. According to Microsoft's own documentation for Azure AI Foundry Models, the GPT-5 models have some seriously impressive context windows. Here's the breakdown:

gpt-5: This is the flagship model, & it boasts a context window of 272,000 tokens with a maximum output of 128,000 tokens.
gpt-5-mini & gpt-5-nano: These smaller, more efficient models surprisingly have the same context window & max output as the full-sized GPT-5.
gpt-5-chat: This model, optimized for conversational AI, has a slightly smaller but still massive context window of 128,000 tokens & a max output of 16,384 tokens.

To put that in perspective, the free version of ChatGPT has an 8K token limit, & the Plus version has a 32K limit. So, we're talking about a HUGE leap in the amount of information these models can handle at once. It's the difference between feeding the model a few pages of a document & giving it a whole book to read.

So, Where's the Confusion Coming From?

If the official limits are so high, why are people talking about low token limits? Well, there are a few things going on here.

First, there's a bit of a precedent for this. The GPT-4.1 series, which has a whopping 1 million token context window, has a known issue where large tool or function calls over 300,000 tokens can just fail. This tells us that even when a model technically has a massive context window, there can be practical limitations to what you can do with it.

Second, the user experience can be a bit inconsistent. One recent review of GPT-5 on a Pro account, which gives you access to the larger context windows, found that a task involving a large PDF produced "shockingly bad" results. So, even if the model doesn't throw an error, the quality of the output can degrade significantly when you're pushing the context to its limits. It’s like trying to have a conversation with someone while they're also trying to read a novel – they might be able to do both, but they're probably not going to do either very well.

And this is where we get into the "is it a bug or a feature" debate.

The "Bug" Argument: When Things Go Wrong

From a user's perspective, if you're promised a massive context window & your requests are failing or giving you garbage output, it feels like a bug. It feels like the model isn't living up to its advertised capabilities. And in a way, you're right. These models are incredibly complex, & it's clear that there are still some kinks to be worked out when it comes to handling truly massive amounts of context.

Think about it this way: when you have a conversation with a person, they don't just remember the last few things you said. They have a lifetime of context to draw from. We're asking these models to do something similar, but with a much more limited & artificial form of memory. It's not surprising that they sometimes struggle.

This is especially true when you start throwing complex tasks at them, like using multiple tools or functions in a single request. Each of those tools has its own set of instructions & parameters, & that all adds to the cognitive load on the model. It's like trying to juggle a dozen balls at once – eventually, you're going to drop one.

The "Intentional Restriction" Argument: A Necessary Evil?

On the other hand, you could argue that these limitations are, to some extent, intentional. OpenAI & Microsoft are constantly trying to balance performance, cost, & accessibility. Running these massive models is incredibly expensive, & the larger the context window, the more computational power it takes to process a request.

By setting these limits, they're essentially creating different tiers of service. If you're a casual user, you probably don't need a million-token context window. A smaller, more efficient model will work just fine for you, & it'll be cheaper for them to run. If you're a power user or a large enterprise, you can pay for access to the larger models, but with the understanding that you might run into some limitations.

It’s also a way of managing expectations. They're not promising you a model that can do everything perfectly all the time. They're giving you a powerful tool, but it's up to you to learn how to use it effectively. And part of that is understanding its limitations.

Making the Most of What We've Got

So, what does this all mean for those of us who are trying to build cool things with these models? It means we need to be smart about how we use them. Here are a few things to keep in mind:

Don't just stuff the context window. Just because you can give the model a 200,000-token document doesn't mean you should. Think about what information is actually relevant to the task at hand & only include that.
Break down complex tasks. If you have a big, complicated problem, try breaking it down into smaller, more manageable steps. This will make it easier for the model to process & will likely give you better results.
Take advantage of the new developer features. GPT-5 on Azure comes with some new tools that can help you get more control over the model's output. The "reasoning_effort" parameter, for example, lets you tell the model whether to prioritize speed or accuracy. The "verbosity" parameter lets you control how much detail you get in the response.
Use the right tool for the job. If you're building a chatbot for your website, you probably don't need the full power of the gpt-5 model. A smaller, more focused model might be a better choice.

This is actually where tools like Arsturn come in handy. If your goal is to provide excellent customer service on your website, you don't necessarily need a model with a massive, all-purpose context window. What you need is an AI chatbot that's trained specifically on your business data. Arsturn helps businesses create custom AI chatbots that can provide instant customer support, answer questions, & engage with website visitors 24/7. It's a great example of using AI in a smart, targeted way to solve a specific business problem. Instead of relying on a generic model that knows a little bit about everything, you can have a specialized AI that knows a lot about your products, services, & customers.

Similarly, if you're focused on lead generation or website optimization, a purpose-built solution is often more effective. Arsturn helps businesses build no-code AI chatbots trained on their own data to boost conversions & provide personalized customer experiences. This approach avoids the "bug or feature" debate entirely by creating a reliable, consistent experience that's tailored to a specific use case.

So, What's the Verdict?

At the end of the day, the token limits on GPT-5 are a bit of a moving target. The technology is evolving so quickly that what's a limitation today might be a standard feature tomorrow. For now, it's best to think of it as a combination of both a bug & an intentional restriction.

The "bug" part is the fact that these models don't always live up to their full potential, especially when you're pushing them to their limits. The "intentional restriction" part is the fact that OpenAI & Microsoft are making a conscious decision to balance performance, cost, & accessibility.

As developers & users, our job is to understand these limitations & work within them. That means being smart about how we design our prompts, breaking down complex tasks, & using the right tools for the job. It also means keeping an eye on the latest developments, because the state of the art is changing all the time.

Hope this was helpful! Let me know what you think. Have you run into any interesting token limit issues with GPT-5? I'd love to hear about your experiences.