The Model Landscape | Getting Started with the OpenAI API

Choosing the right model is the single highest-leverage decision you make with the OpenAI API. Pick a model that is too powerful and you burn money. Pick one that is too weak and you get bad results. Pick one that is just right and you get great results at a fraction of the cost.

As of mid-2025, OpenAI offers three main model families, each designed for different use cases.

10-50x

cost difference between the cheapest and most expensive models for the same prompt

The GPT Family

These are the workhorse models -- fast, capable, and available at multiple price points.

GPT-4.1 Family

The GPT-4.1 family is optimized for coding, instruction following, and long-context tasks.

Model	Context Window	Input Price	Output Price	Best For
gpt-4.1	1M tokens	$3.00/M	$12.00/M	Complex coding, detailed analysis, long documents
gpt-4.1-mini	1M tokens	$0.80/M	$3.20/M	Everyday tasks, good quality at low cost
gpt-4.1-nano	1M tokens	$0.20/M	$0.80/M	Classification, extraction, high-volume simple tasks

Start with gpt-4.1-mini

For most applications, gpt-4.1-mini is the right starting point. It handles 90% of tasks well at roughly 1/4 the cost of the full gpt-4.1. Only upgrade when you can demonstrate -- through testing, not assumption -- that the larger model produces meaningfully better results for your specific use case.

GPT-5.4

The newest and most capable model in the GPT family.

Model	Context Window	Input Price	Output Price	Best For
gpt-5.4	1M tokens	$2.50/M	$15.00/M	Most capable model, complex reasoning, nuanced tasks
gpt-5.4-mini	400K tokens	$0.75/M	$4.50/M	Strong general-purpose model at moderate cost
gpt-5.4-nano	400K tokens	$0.20/M	$1.25/M	Fastest, cheapest GPT-5.4 variant for high-volume simple tasks

GPT-5.4 represents a significant capability jump. It can use reasoning when needed (configurable via the reasoning parameter), has built-in tool use, and handles complex multi-step tasks better than any previous GPT model.

The o-Series: Reasoning Models

The o-series models are fundamentally different from GPT models. They "think" before answering -- spending extra compute time on an internal chain of thought before producing a response.

Model	Context Window	Input Price	Output Price	Best For
o3	200K tokens	$10.00/M	$40.00/M	Hard reasoning, math, science, complex analysis
o4-mini	200K tokens	$1.10/M	$4.40/M	Cost-effective reasoning, coding, STEM tasks

Before

GPT models: Fast responses, lower cost, great for straightforward tasks. Generate tokens in a single pass -- the first word of output comes back in milliseconds. Think of them as a brilliant colleague who answers immediately.

After

o-series models: Slower responses, higher cost, superior for hard problems. Spend time reasoning before generating output -- you may wait seconds or even minutes. Think of them as a brilliant colleague who pauses, thinks carefully, then gives a more thorough answer.

When to Use Reasoning Models

Reasoning models shine on tasks where thinking step-by-step genuinely improves the answer:

Math and logic: Multi-step calculations, proofs, constraint satisfaction
Complex coding: Algorithm design, debugging subtle bugs, architecture decisions
Science: Research analysis, experimental design, data interpretation
Strategic planning: Business analysis, risk assessment, decision frameworks

They are overkill for:

Simple text generation or summarization
Data extraction and formatting
Translation
Classification

Use reasoning models when the task genuinely requires multi-step logical deduction. Test with GPT-4.1-mini first -- if it gives good results, you do not need reasoning.

Don't

Default to o3 for everything because it is the most powerful. You will burn budget on tasks that do not benefit from extended thinking, and your latency will increase by 5-20x.

The Decision Framework

Here is how to choose a model for any given task:

1Is the task simple? (classification, extraction, formatting)

2YES: Use gpt-4.1-nano or gpt-5.4-nano

3NO: Does it require multi-step reasoning?

4YES: Use o4-mini (or o3 for hardest problems)

5NO: Is it a complex but straightforward task?

6YES: Use gpt-4.1-mini or gpt-5.4-mini

7Still not good enough? Use gpt-4.1 or gpt-5.4

Pricing Reality Check

Let's make costs concrete. Assume you are building a customer support bot that handles 10,000 conversations per day, averaging 500 input tokens and 300 output tokens per conversation.

Model	Daily Input Cost	Daily Output Cost	Daily Total	Monthly Total
gpt-4.1-nano	$1.00	$2.40	$3.40	$102
gpt-4.1-mini	$4.00	$9.60	$13.60	$408
gpt-4.1	$15.00	$36.00	$51.00	$1,530
gpt-5.4	$12.50	$45.00	$57.50	$1,725
o4-mini	$5.50	$13.20	$18.70	$561

The $50K Mistake

Model Routing Is a Production Skill

Mature AI applications do not use a single model. They route requests to different models based on task complexity. Simple classification goes to nano. Standard conversations go to mini. Complex analysis goes to the full model. This is called model routing, and it is one of the most impactful optimizations you can make.

Staying Current

OpenAI releases new models regularly. The naming convention tells you what to expect:

Date suffixes (e.g., gpt-4.1-mini-2025-04-14): A specific snapshot. Pinned, will not change.
Without date (e.g., gpt-4.1-mini): Points to the latest stable version. May be updated.

For production systems, pin to a specific dated version so model updates do not break your application unexpectedly. For development and testing, use the latest version to access improvements.

Model Comparison Challenge

beginner

Concept Card