Understanding OpenAI’s Models in 2025: What They Do and When to Use Them

OpenAI currently offers several AI models, each built with different infrastructure, capabilities, and target use cases. Choosing the right one depends on what you need: speed, reasoning, cost-efficiency, multimodal input, or task specialization.

Here’s a breakdown of the core models available, how they differ, and how to use them effectively.

1. GPT-4.1 Series (Released April 2025)

Models: GPT-4.1, GPT-4.1 Mini, GPT-4.1 Nano
Architecture: Transformer-based LLM, optimized for multi-step reasoning and long-context understanding.
Max Context Window: Up to 1 million tokens

What’s new:

• Better memory and consistency over long sessions

• Lower latency

• Cheaper inference cost (26% cheaper than GPT-4 Turbo)

When to use:

• GPT-4.1: Research, legal reasoning, technical writing, advanced coding.

• Mini: Business logic, chatbots with moderate reasoning needs.

• Nano: Fast response apps, budget tools, low-compute environments.

Why it’s different:
Each tier balances capability vs. speed and cost. All use improved optimization from OpenAI's custom inference stack, likely built on Triton and Nvidia H100 clusters.

2. o-Series Reasoning Models (o1, o3, o4-mini)

Models: o1, o3, o4-mini
Purpose: Advanced reasoning, complex workflows, multi-agent systems.

What makes them different:

• These models are fine-tuned for logic-heavy tasks like scientific research, multi-step planning, and tool use (e.g., code execution, web browsing).

• Optimized for chaining thoughts and building agent-like behaviors.

When to use:

• o1: Lightweight agent tasks, decision trees, and academic support.

• o3: Full-stack AI agents (e.g., customer service bots with memory).

• o4-mini: Cost-effective version of o3 for medium-complexity workflows.

Example use case:
Automating insurance claims review or deploying a multi-agent assistant that handles scheduling, summarizing documents, and checking policies.

3. GPT-4o Series (Multimodal)

Models: GPT-4o, GPT-4o Mini
Key Feature: Multimodal, meaning it understands text, images, and audio.

Use it when:

• You need to process multiple input types (e.g., screenshots + text)

• Building tools for accessibility (e.g., voice-to-text interfaces)

• Real-time digital assistants (GPT-4o Mini powers ChatGPT free tier)

Why it matters:
These models are trained to unify input modalities into a single processing stream. Infrastructure likely involves joint embedding layers and cross-modal attention modules.

Example:

• GPT-4o: A doctor uploads a scan, asks the model to interpret it, then queries the next steps.

• GPT-4o Mini: A voice assistant that schedules meetings and reads news aloud.

4. GPT-3.5 Series (Legacy)

Models: gpt-3.5-turbo, text-DaVinci-003

Why it's still used:

• Extremely fast and cheap

• Still viable for basic summarization, Q&A, and prototyping

When to use:

• MVPs

• Budget chatbots

• Lightweight code suggestions

Note: These models lack advanced context management and often hallucinate more than newer models.

5. Specialized Models

DALL·E 3 (Image generation)

• Text-to-image model

• Best for realistic scenes and concept art

• Built with diffusion-based transformer layers

Whisper (Speech-to-text)

• Converts spoken audio into text

• Ideal for transcriptions, subtitles, and accessibility tools

Sora (Video generation)

• Converts text prompts into short video clips

• Experimental, slower, requires high compute

CLIP (Vision-language model)

• Matches text and image content

• Useful for captioning, search, and classification

Infrastructure Differences

How to Choose the Right Model