If you're using AI for coding, you're paying per token whether you know it or not. Your $20/month Claude Pro or ChatGPT Plus subscription subsidizes the actual API cost — but that cost varies wildly depending on which model you're using and how you're prompting it.
We compared the current API pricing for the most popular models to help you understand what your AI usage actually costs — and which model gives the best value for different types of work.
The pricing landscape in 2026
| Model | Input / 1M tokens | Output / 1M tokens |
|---|---|---|
| Claude Opus 4 | $15.00 | $75.00 |
| Claude Sonnet 4 | $3.00 | $15.00 |
| Claude Haiku 3.5 | $0.80 | $4.00 |
| GPT-4o | $2.50 | $10.00 |
| GPT-4o mini | $0.15 | $0.60 |
The gap between the cheapest and most expensive option is enormous. Claude Opus 4 output tokens cost 125 times more than GPT-4o mini output tokens. Choosing the right model for the right task isn't just optimization — it's the difference between spending $5 a month and $500.
What this means for a real coding session
A typical 30-message coding conversation uses roughly 50,000–100,000 input tokens (because conversation history accumulates) and 20,000–40,000 output tokens. Here's what that session costs at the API level across models.
On Claude Opus 4, a session like that runs about $3–$5 in API cost. The same conversation on Claude Sonnet 4 drops to roughly $0.60–$1.20. On GPT-4o mini, the entire session might cost $0.02–$0.04. The quality and capability differences are real, but so are the price differences.
The hidden multiplier: Web UI users don't see these costs directly because they pay a flat subscription fee. But the AI providers are eating the difference — which is why you hit message limits on premium models. Understanding the per-token cost explains why Claude limits Opus usage more aggressively than Sonnet.
Which model for which task?
Complex architecture decisions, debugging subtle issues, or working with unfamiliar codebases — this is where premium models like Claude Opus 4 justify their cost. The quality difference on hard problems is substantial, and the cost of AI getting it wrong (you spending hours on a bad approach) far exceeds the token cost.
Everyday coding, writing tests, refactoring, documentation — mid-tier models like Claude Sonnet 4 or GPT-4o handle these well. You get 80% of the intelligence at 20% of the cost.
Boilerplate generation, simple questions, format conversions — budget models like GPT-4o mini are more than capable. Don't pay premium prices for commodity tasks.
The real insight: overhead costs more than your prompts
Here's what most developers miss entirely. When you send a message on Claude or ChatGPT, your visible prompt is typically 5–10% of the actual input tokens. The rest is system prompts, conversation history, tool definitions, and other context the platform attaches invisibly.
This means a short "fix the bug in line 47" message might look cheap, but it's actually sending 10,000+ tokens of accumulated context along with it. In a long conversation, most of your cost comes from this overhead — not from your actual prompts.
Tracking this overhead is exactly what Kontinuity was built to do.
Track your actual AI costs
See exactly what every prompt costs across Claude and ChatGPT, including the overhead you never see.
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