Replacing Junior Devs with GPT-4 Turbo? I Ran the Numbers So You Don’t Have To

From token costs to capability gaps, here’s what AI gets right — and where you’ll still need real people.



“Will AI take my job?”

I get this question a lot — especially from college students, fresh grads, and junior developers trying to make sense of the current tech chaos. And I get it. With AI tools seemingly writing full apps, generating tests, and even fixing bugs, it’s fair to wonder where humans fit in.

I usually tell them this: The industry is changing — faster than ever. But we’re not just replacing coders with machines. We’re shifting from “who can type the fastest” to “who can think the clearest.”

Still, there’s a popular narrative floating around that AI is already cheaper than hiring juniors — especially in geographies like India. So I decided to put that to the test.

This article breaks down what it actually costs to use GPT-4 Turbo as a dev replacement, what it can do well, where it fails hard — and why, for now, hiring a junior dev might still be the smarter (and cheaper) choice.

Scroll down to the sidebar in the article to understand why I choose GPT-4 Turbo over GPT-3.5 Turbo or a hybrid usage of 3.5 & 4.0

What does GPT-4 actually cost?

If you’re using GPT-4 Turbo via OpenRouter:

That’s ₹400–₹600 a day, or about ₹15K–₹18K a month. That’s not pocket change. It’s in the same ballpark as hiring a fresher or junior dev in India (₹20K–₹40K/month range).



Where AI shines

Where AI still falls short



So… is AI cheaper than a junior dev?

Only if:

For most startups? A junior dev is still cheaper and easier to manage. Unless:



Side Note #1: why GPT-4 Turbo and no other LLMs

Before settling on GPT-4 Turbo, we did experiment with a few cheaper paths. Spoiler: they didn’t quite work out. But here’s what we learned — in case you’re thinking the same.

Costing Table for various options

Option 1: “Just Use GPT-3.5 Turbo, It’s Cheaper!”

Yup, it is cheaper.

But here’s the catch: GPT-3.5 is great for quick tasks — like writing boilerplate, filling out functions, or summarizing files. But the moment your prompt gets complex, or spans multiple files, or needs subtle logic — it stumbles. Hard.

You end up rewriting, prompting again, or worse, switching models mid-way.

Option 2: “Use GPT-3.5 Most of the Time, Escalate to GPT-4 When Needed”

This sounded perfect on paper.

Why it backfired: You’re not just paying for compute. You’re paying for time. By the time GPT-3.5 fails and you realize you need GPT-4, you’ve already lost context, time, and tokens. And often, you re-prompt from scratch.

Net result? You spend more, and your flow breaks.

What Actually Worked: Full GPT-4 Turbo



Side Note #2: What if we consider US / UK market instead of India

Let’s run the same math for US and UK junior devs, using the same token usage assumptions (1.5M–2M tokens/day) and current GPT-4 Turbo pricing via OpenRouter as of June 2025.

Estimated Monthly Cost for GPT-4 Turbo as Dev

Junior Dev Salaries — USA

Junior Dev Salaries — UK



Final Thoughts: It’s Not AI vs Junior Devs

It’s tempting to line up token costs next to salaries and do the math. But honestly, that’s the wrong comparison to focus on.

AI isn’t here to replace junior developers. AI is best thought of as a force multiplier. It helps the people who already know what they’re doing move faster, build better, and think clearer. It’s not a magic wand. It’s a power tool. So the real productivity unlock is pairing smart dev (like you) with:



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