OpenAI API review: what it costs and what it delivers
Filed under Review
By Gerald · 5 July 2026
I have been building with the OpenAI API since GPT-3.5. I have shipped features that worked beautifully and features that failed embarrassingly. I have watched the pricing change, the models improve, and the competition catch up.
The OpenAI API is the default choice for adding AI to an application. It is not the only choice, and it is not always the best choice. But it is the one most developers reach for first.
Here is an honest review of what it costs, what it does well, and where you should look elsewhere.
The current model lineup
OpenAI's API offers several models with different strengths and prices. These were current in June 2026.
GPT-4o is the flagship model. It handles text, code, and reasoning well. It is fast and cheaper than the older GPT-4 Turbo. For most applications, this is the model to start with.
o3 is the reasoning model. It thinks longer before responding, which makes it better at complex math, coding problems, and multi-step reasoning. It is slower and more expensive.
GPT-4o-mini is the budget option. It is fast and very cheap, but less capable on complex tasks. Good for simple classification, formatting, or high-volume applications where quality can be slightly lower.
There are also image and audio models, but this review focuses on text because that is what most SaaS applications use.
Pricing breakdown
OpenAI charges per token, which is roughly a word or part of a word. You pay for the input you send and the output you receive.
GPT-4o costs $2.50 per million input tokens and $10 per million output tokens. GPT-4o-mini costs $0.15 per million input tokens and $0.60 per million output tokens. o3 costs $10 per million input tokens and $40 per million output tokens.
To put that in practical terms: a typical chat message of 500 tokens in and 300 tokens out using GPT-4o costs about $0.00225. A thousand such messages cost $2.25. Ten thousand cost $22.50.
These prices were current in June 2026. OpenAI changes pricing frequently, so verify before you budget.
Rate limits
Rate limits depend on your account tier. New accounts start at low limits and increase as you spend more and wait longer.
At the entry tier, you might get 3 requests per minute for GPT-4o. At higher tiers, you can get thousands per minute. The exact numbers change, and OpenAI does not publish a fixed table.
For a prototype, rate limits are not a problem. For a production application with real users, they matter. If you hit a limit, requests fail. You need backoff logic and request queuing.
I have had to implement request batching and caching for Flow's AI features to stay under limits during testing. It is not difficult, but it is extra work you do not need with every provider.
What the API does well
The OpenAI API is reliable. Uptime is high. The latency is predictable. When you call it at 3 AM on a Sunday, it responds.
The function calling feature is well-designed. You define tools as JSON schemas, and the model decides when to use them. This makes it practical to build agents and structured workflows.
The JSON mode ensures the model outputs valid JSON, which is essential for applications that parse responses programmatically. It is not perfect, but the failure rate is low.
The documentation is clear. The community is large. When you run into a problem, someone else has already solved it.
Where it falls short
The biggest issue is cost at scale. A popular feature that calls GPT-4o thousands of times per day can add hundreds of dollars to your monthly bill. I have seen AI features cost more than the hosting infrastructure they run on.
Hallucinations are still a problem. GPT-4o is better than earlier models, but it still makes things up. It invents facts, misquotes sources, and produces confident nonsense. You cannot trust its output without verification.
The rate limits can bite you unexpectedly. A sudden traffic spike or a popular social media post about your AI feature can exhaust your quota fast.
Support is minimal unless you are on an enterprise plan. For most developers, the help center and community forums are your only options.
When to use Anthropic instead
Anthropic's Claude models are the main alternative. Claude 3.5 Sonnet is roughly comparable to GPT-4o. Claude 3 Opus competes with o3 on reasoning tasks.
Claude tends to be more careful with factual claims. It hallucinates less on some tasks, particularly long document analysis. Its writing style is often more natural.
Claude's API has its own pricing and rate limits. As of June 2026, Claude 3.5 Sonnet costs $3 per million input tokens and $15 per million output tokens. That is slightly more expensive than GPT-4o.
I use Anthropic for tasks where factual accuracy matters most: summarizing long notes, analyzing user content, and generating explanations. I use OpenAI for tasks where speed and cost matter more: formatting, classification, and simple generation.
The Vercel AI SDK makes it easy to switch between them, which is how I run most of my AI experiments.
Other alternatives
Google's Gemini API is cheaper and competitive on some tasks. It is worth testing, though the developer experience is not as polished as OpenAI's.
Local models through Ollama or LM Studio are free to run but require GPU hardware. They are good for prototyping and for applications where data cannot leave your servers.
Cohere and Mistral offer APIs with different pricing and capabilities. They are worth exploring if OpenAI's terms or pricing do not suit you.
Managing costs
Here is what I have learned about keeping OpenAI API costs under control.
Cache responses when possible. If users ask the same questions repeatedly, store the answers instead of calling the API every time.
Use the cheapest model that can handle the task. GPT-4o-mini is fine for formatting and simple classification. Reserve GPT-4o for harder tasks.
Set hard spending caps in your OpenAI dashboard. This prevents a runaway feature from generating a surprise bill.
Monitor token usage per feature. Some features cost far more than others. You need to know which ones to optimize.
How Flow uses the OpenAI API
Flow uses the OpenAI API for AI-assisted writing and smart search suggestions. The writing assistant helps users expand bullet points into paragraphs. The search feature generates better queries from natural language input.
These are optional features. Users can turn them off. The core product works without them.
I keep costs low by using GPT-4o-mini for simple tasks and GPT-4o only when the output quality matters. Caching reduces duplicate calls by about thirty percent.
Frequently asked questions
How much does OpenAI API cost? OpenAI charges per token. GPT-4o costs $2.50 per million input tokens and $10 per million output tokens. GPT-4o-mini costs $0.15 per million input tokens and $0.60 per million output tokens. A typical application usage ranges from a few dollars to hundreds per month depending on volume.
Is GPT-4o worth it? GPT-4o is worth it for tasks that need high-quality reasoning, coding assistance, or nuanced writing. For simple formatting, classification, or high-volume applications, GPT-4o-mini is usually sufficient and much cheaper.
What are OpenAI API rate limits? Rate limits depend on your account tier. New accounts start with low limits and gain higher quotas over time and with increased spending. Production applications need request queuing and backoff logic.
Is OpenAI API safe for production? Yes, for most use cases. The API is reliable and well-documented. The main risks are cost overruns, hallucinations in generated content, and rate limit failures during traffic spikes. Plan for all three.
Should I use OpenAI or Anthropic? Use OpenAI if you want the most mature API, broadest ecosystem, and lowest cost for most tasks. Use Anthropic if factual accuracy, careful reasoning, and long document analysis are your priorities.
Related reading
My verdict
The OpenAI API is the safe default for adding AI to your application. It is reliable, well-documented, and capable. It is also expensive at scale and prone to hallucinations like every other LLM.
Start with GPT-4o-mini for simple tasks. Upgrade to GPT-4o when you need quality. Test Anthropic for tasks where accuracy matters most. Cache everything you can. Set spending caps.
The OpenAI API is a tool, not a solution. Use it where it fits. Do not force AI into features that do not need it. That is the approach I take with Flow, and it has kept costs reasonable while still shipping useful AI features.