Every week someone asks me: Claude or ChatGPT?
It's the wrong question.
Not because both aren't good. They're both excellent. It's the wrong question because it treats AI like a single utility — one tool, one job, one right answer. And that's not how any of this works.
The model you pick should be boring
When I build automations for businesses, model selection is one of the last decisions I make. And it's usually not exciting.
Cold email at volume? GPT-4.1-mini. Done. Costs about $0.002 per lead, runs 24/7, never has a bad day.
Agentic workflow that needs to stay on task across 12 steps? Claude Sonnet. Not because it's "smarter" — because its instruction-following is tighter across long chains and it's less likely to drift. That's why we use it to power automations like our AI content factory.
Processing a 90,000-word contract? Gemini 2.5 Pro. Two million token context. Nothing else comes close for a reasonable price.
The right model is boring. It's just the one that fits.
What 1M tokens actually buys you
Most people have no mental model for what AI actually costs to run. They hear "$15 per million output tokens" and either panic or shrug.
Here's what it actually means:
At $0.30 per million tokens (what you'd pay with Gemini Flash or GPT-4.1-mini), you're getting:
- About 1,600 cold emails written
- About 1,500 customer support replies handled
- About 2,800 lead records classified
- About 600 sales calls summarized
That's not a month of work. That's a busy Tuesday.
The catch most people miss: input counts too. Summarizing a transcript costs more than writing an email — not because the output is longer, but because the transcript itself burns tokens before you get a single word back. A 1,500-token transcript going in plus a 200-token summary coming out = 1,700 tokens per call. The math matters when you're doing this at scale.
Where the "which model is best" debate actually lives
It lives in demos and Twitter threads. Not in production.
In production, you're asking: what does this specific task need?
Speed? Haiku. Flash. Mini. Something cheap and fast.
Reasoning quality over long inputs? Claude. The 200K context window and retention across long prompts is real.
Vision, images, screenshots? GPT-4o. Best in class, not close.
Real-time internet data? Grok. It's the only model with live X access baked in. Nothing else has that — which makes it ideal for something like AI-powered market research.
Regulated or high-stakes output? Claude Opus. Anthropic's safety training is the most serious in the industry. When being wrong has real consequences, don't cheap out.
None of this is tribal loyalty. It's just match-the-tool-to-the-job.
The expensive mistake
The most common mistake I see: running everything through GPT-4o (or Claude Sonnet) because it feels safe.
Those are $5–15/M input models. If you're classifying leads, summarizing short emails, or generating templated copy — you're paying 10–50x more than you need to.
The models at $0.15–$0.40/M input handle the majority of real-world GTM tasks just fine. The premium models earn their cost on the tasks that genuinely need them.
The operator move is to map your stack by task, not by preference.
So what should you actually do?
Start with the task. What does it need?
- Simple extraction or classification → cheap model, fast, done
- Customer-facing writing that needs to be good → step up to Sonnet or GPT-4.1
- Long documents or multi-step agents → Claude, use the context window
- Anything with images → GPT-4o
- Social/live data research → Grok
If you want an interactive version of this decision, we built a free tool that maps tasks to model recommendations with real pricing and honest operator notes. It's at gtm.garden/tools/llm-model-guide/ — no gate, no email, just use it.
The model war is a distraction. The real question is: how much of your business is still running manually that doesn't need to be? See how we put these models to work in our production automations.