There's a TON of chatter about GPT-5. The hype is real, & you can't scroll through tech Twitter or Reddit without seeing some new benchmark or hot take. It's supposed to be the next big leap, the model that changes everything. But here's the thing... while everyone's looking at GPT-5, Anthropic's Claude Sonnet 4 has quietly become the AI coding partner that developers ACTUALLY need.

Honestly, I've been digging into this, looking at what people are saying & how these models perform in the real world. & a pretty clear picture is starting to emerge. GPT-5 is like that brilliant, wildly creative developer who wants to refactor the entire codebase for a simple bug fix. Impressive? Sure. Practical? Not so much. Sonnet 4, on the other hand, is like the seasoned senior dev who comes in, makes a precise, surgical fix, & gets the job done without a fuss.

So, let's break down why Sonnet 4 is looking like the true, tangible future for those of us who write code for a living, & why GPT-5, for all its promises, is still a bit of a mirage.

One of the most consistent things you hear from developers using both models is the difference in their approach to coding tasks. GPT-5 has a tendency to be "proactively verbose" & undertake large refactors, even for small requests. This can be cool, but it can also be a nightmare for maintaining a clean, predictable codebase. It might change random, unrelated stuff or get stuck in loops on complex logic. One user on Reddit even called it a "wild card."

Claude Sonnet 4, however, is praised for being more "conservative with edits." It delivers "sharper, 'surgical' patches," which is often exactly what a developer wants. You're looking for a partner, not a replacement who goes rogue. Sonnet 4 understands the scope of a project better & is less likely to introduce unnecessary changes. This focus on maintainability & precision is a HUGE deal in a professional development environment.

& then there's the context window. This is a BIG one. Anthropic recently updated Sonnet 4 to support a 1 million token context window. That's roughly 750,000 words. For a developer, this means you can feed it an enormous amount of your codebase, & it can reason over all of it. This is a game-changer for understanding complex, multi-file projects & providing relevant, context-aware suggestions. GPT-5, in comparison, has a context window of around 200K to 400K tokens. That's still a lot, but it's a significant difference when you're working on large-scale applications.

This ability to hold so much information in its "mind" at once makes Sonnet 4 incredibly powerful for tasks like:

Another area where Sonnet 4 is pulling ahead is its integration into the tools developers already use. Through "Claude Code," Sonnet 4 has native integrations with VS Code & JetBrains IDEs. This means it can display its edits directly in your files, creating a seamless pair-programming experience. This tight integration makes it feel less like a separate tool you have to consult & more like a true extension of your development environment.

While GPT-5 certainly has API access & can be integrated into various tools, the focus of Claude Code on a native, in-IDE experience shows a deep understanding of the developer workflow. It's not just about providing a powerful model; it's about making that model as easy & intuitive to use as possible for its target audience.

Now, let's talk about money. At first glance, GPT-5 looks like the cheaper option. It costs less per million tokens for both input & output compared to Sonnet 4. For example, one source quotes GPT-5 at $1.25 per million input tokens & $10 per million output tokens, while Sonnet 4 is $3 & $15, respectively.

But here's the catch: GPT-5 is consistently described as being more "token-intensive." Because it's more verbose & tends to provide longer, more detailed responses, you often end up using more tokens for the same task. So, while the per-token price is lower, your overall cost could end up being higher.

Sonnet 4, with its more concise & focused responses, can be more cost-efficient for many common development tasks. It's a classic case of "you get what you pay for," but in a way that might surprise you. The more expensive per-token model could actually save you money in the long run by being more efficient.

When you boil it all down, the choice between these two models comes down to a fundamental difference in philosophy. GPT-5 is all about raw, untamed power. It has incredibly high benchmark scores & excels at complex reasoning. But as many developers are discovering, that power can be unpredictable. One user on a discussion forum shared a "horrible experience" with GPT-5, where it repeatedly provided incomplete & inaccurate code, leading to over 30 tracked mistakes.

Sonnet 4, on the other hand, is built for reliability & predictability. It might have a slightly lower score on some benchmarks, but its performance in real-world coding scenarios is often preferred by developers who value consistency & maintainability. It’s a tool designed for professionals who need to ship reliable code, not just experiment with the outer limits of AI.

This is a trend we're seeing across the AI landscape. It's not just about having the most powerful model; it's about creating AI that can be effectively & reliably integrated into real business workflows. It's a bit like what we're focused on at Arsturn. We believe in the power of AI, but we know that for businesses to truly benefit from it, it has to be accessible, easy to use, & tailored to specific needs. That's why Arsturn helps businesses create custom AI chatbots trained on their own data. These chatbots can provide instant customer support, answer questions, & engage with website visitors 24/7, all without the need for a team of AI researchers. It's about taking the power of AI & making it a practical, everyday tool for business growth.

The same principle applies to AI-assisted development. A model that is 99% accurate & easy to work with is often more valuable than a model that is 99.9% accurate but unpredictable & difficult to manage.

Here's my take: GPT-5 is an amazing piece of technology & a glimpse into the incredible potential of large language models. It will continue to push the boundaries of what's possible. But it's not the "endgame" for AI-assisted development, at least not yet. Its "false promises" are not in its capabilities but in the assumption that raw power is all that matters.

The real, immediate future of AI in development looks a lot more like Claude Sonnet 4. It's a future where AI tools are seamlessly integrated into our workflows, where they provide precise, reliable assistance, & where they help us write better, more maintainable code without getting in the way. It's a future that's less about hype & more about practical, everyday value.

Sonnet 4's massive context window, its focus on surgical precision, & its deep integration into developer tools make it the more practical & productive choice for most developers today. It's the AI partner that's ready to help you get your work done, right now.

Hope this was helpful! Let me know what you think in the comments. Have you tried coding with both? What's your experience been like?