Key Takeaways
- 1GPT-4o is your daily driver, but Claude 3.5 Sonnet destroys it at deep logic.
- 2GPT-4o-mini completely replaces GPT-3.5 and saves you massive amounts of money on grunt work.
- 3Hardcoding all your tasks through a single, expensive model is a lazy and expensive mistake.
- 4The new OpenAI Responses API wipes out hundreds of lines of technical debt.
- 5Match the intelligence of the model to the complexity of the task to scale profitably.
If your code still calls `gpt-4` or `gpt-3.5-turbo`, you are actively burning cash.
I see it every week. A startup in Medellin asks us to scale their support desk. We look at the codebase, and they are routing simple ticket updates through `gpt-4`.
They are bleeding margin for zero added value. That is like hiring a senior corporate lawyer to sort your daily mail.
Using GPT-4 for simple data classification is like hiring a senior lawyer to sort your mail.
The only three models that matter right now
Ignore the Twitter hype cycle. If you are building automations today, 99% of your workflows should rely on exactly three models.
- GPT-4o is your daily driver. It is fast, handles voice and vision natively, and costs half of what the old GPT-4 did. Use this for customer-facing chatbots and extracting data from invoices.
- Claude 3.5 Sonnet is the senior developer. If you need an AI to write a Python script, fix a nested JSON bug, or dissect a 50-page vendor contract, Anthropic wins. It destroys GPT-4o at deep logic.
- GPT-4o-mini is the grunt worker. It completely replaced GPT-3.5. It is dirt cheap and incredibly fast. Use it for text classification, routing support tickets, and summarizing short emails.
Kill your complex API wrappers
OpenAI's old chat completions endpoint was a bloated mess for simple tasks. They finally fixed it.
The new Responses API is ruthlessly simple. Pass `model="gpt-4o"`, drop in your prompt, and hand over the input. Delete those custom wrappers clogging up your GitHub repo.
Vision is built-in now
If you are processing images, the Responses API handles base64-encoded files natively. Stop writing custom parsers for every image format. Just encode it and pass it to GPT-4o.
Stop hardcoding one model for everything
Hardcoding `gpt-4o` across your entire application is a lazy, expensive mistake.
Last month, we audited a mid-sized logistics firm in Bogota. They were paying OpenAI $3,000 a month just to parse addresses from shipping labels using GPT-4.
We swapped it to GPT-4o-mini. Their bill dropped to $120 a month. The accuracy stayed at 99.8%. That is a 96% cost reduction just by matching the model to the task.
- Audit the IQ required: Separate the dumb tasks (tagging, routing, keyword extraction) from the smart tasks (drafting nuanced email replies).
- Assign the right brain: Route the grunt work to GPT-4o-mini. Save GPT-4o and Claude 3.5 Sonnet exclusively for heavy reasoning.
- Refactor your endpoints: Switch your Python scripts to the Responses API today. It takes ten minutes and wipes out hundreds of lines of technical debt.
Stop funding OpenAI's yacht
If your AI bills are climbing but your automation is not improving, you have an architecture problem. Let's fix it.
Audit my workflowsFrequently Asked Questions
Should I just use GPT-4o for everything?
Absolutely not. GPT-4o is fast and cheap, but for writing complex code or deep logical reasoning, Claude 3.5 Sonnet is objectively better. Match the model to the task.
What happened to GPT-3.5?
It is dead. OpenAI replaced it with GPT-4o-mini. If your scripts still call gpt-3.5-turbo, you are paying more for worse results. Refactor your code today.
Kyto
AI & Automation Firm
We design and build AI automations and business operating systems. Agency results + Academy sovereignty.
