The confusion around OpenAI’s model naming conventions has reached comical proportions. At least the terminators had simple names.
If you’ve found yourself puzzled by the seemingly random combination of letters and numbers in OpenAI’s latest model lineup, you’re not alone. With multiple models across three distinct families, it’s becoming increasingly difficult to determine which model to use for specific tasks – and more importantly, how much each will cost you.
Let’s clarify what you actually need to know about the current OpenAI model ecosystem.
Release Timeline
OpenAI’s model releases have come in waves, creating the current complex landscape. Understanding the timeline helps put the different families into perspective.
OpenAI Model Release Dates
| Model/Family | Release Date |
|---|---|
| GPT‑4o | May 2024 |
| GPT‑4o‑mini | July 2024 |
| GPT‑4.1 family | April 2025 |
| o3 & o4‑mini | April 16, 2025 |
Understanding the Three Model Families
OpenAI’s lineup is currently structured around three distinct families, each with unique characteristics and intended uses. Ignoring this structure is a recipe for confusion and wasted money.
GPT‑4.1 Family: Code, Context, and Cost Efficiency
The GPT‑4.1 family includes the flagship GPT‑4.1 and its smaller siblings, GPT‑4.1 mini and GPT‑4.1 nano. These models are built for tasks requiring a large memory and cost optimization.
With a 1 million token context window, these models excel at handling extensive codebases, analyzing long documents, and maintaining conversational history over prolonged interactions. They are also positioned as a more cost-effective alternative to the GPT‑4o family for text-based tasks, coming in 26% cheaper than the base GPT‑4o model.
- Key features: Function-calling capabilities for integrating with external tools, reliable JSON mode for structured outputs, and full compatibility with the Assistants API for building stateful applications.
- Ideal Use Cases: Code generation and analysis, processing long documents, complex instruction following, and applications where cost per token is a primary concern.
GPT‑4o Family: Speed and Multimodal Native
The GPT‑4o family, comprising GPT‑4o and GPT‑4o-mini, is designed for speed and native multimodal processing. If you need fast responses within the ChatGPT interface, these are your models.
These models offer the lowest latency in the ChatGPT model selector, making them suitable for real-time applications like chatbots and interactive assistants. Their native multimodal architecture means they can process and generate responses from both text and image inputs seamlessly. Voice applications need these models.
- Key features: Native image understanding and generation, rapid response times, optimized for conversational AI and multimodal workflows.
- Ideal Use Cases: Real-time interaction, image captioning and analysis, generating creative content from visual prompts, and applications where audio latency is paramount.
o‑Series: Autonomous Agents with Deep Reasoning
The o-series, including o3 and o4-mini, represents OpenAI’s foray into more autonomous and deeply reasoning models. These are not your everyday chat models; they are built for complex problem-solving and independent action.
These models feature a “hidden chain‑of‑thought” process, allowing them to break down complex problems into smaller steps and exhibit more sophisticated reasoning. Crucially, they come equipped with a full autonomous tool kit, enabling them to browse the web, execute Python code, and manage memory independently. This makes them powerful for tasks requiring research, data analysis, or multi-step automation.
- Key features: Advanced reasoning capabilities, autonomous web browsing, Python code execution, and memory management for stateful interactions.
- Ideal Use Cases: Complex research tasks, automated data analysis, building AI agents, and applications requiring deep logical deduction.
Model Specification Sheet
To further clarify the differences between the models, here’s a specification sheet comparing key features and pricing.
OpenAI Model Specifications (April 2025)
| Model | Context Window | Modalities | Tool Access | Price ($/1M Input | Output) |
|---|---|---|---|---|
| GPT‑4.1 | 1M tokens | Text | Function-calling, JSON, Assistants API | $2 | $8 |
| GPT‑4.1 mini | 1M tokens | Text | Function-calling, JSON, Assistants API | $0.40 | $1.60 |
| GPT‑4.1 nano | 1M tokens | Text | Function-calling, JSON, Assistants API | $0.10 | $0.40 |
| GPT‑4o | Variable | Text, Image | Function-calling, JSON, Assistants API, Image | $2.50 | $10 |
| GPT‑4o‑mini | Variable | Text, Image | Function-calling, JSON, Assistants API | $0.15 | $0.60 |
| o3 | 200k | Text | Web, Python, Memory, Image | $10 | $40 |
| o4‑mini | 200k | Text | Web, Python, Memory, Image | $1.10 | $4.40 |
Pricing Breakdown: Cost Per Million Tokens
Understanding the cost structure is critical when choosing which model to use for your applications. Here’s the current pricing as of April 2025:
OpenAI Model Specifications (April 2025)
| Model | Context Window | Modalities | Tool Access | Price ($/1M Input | Output) |
|---|---|---|---|---|
| GPT‑4.1 | 1M tokens | Text, Image | Function-calling, JSON, Assistants API | $2 | $8 |
| GPT‑4.1 mini | 1M tokens | Text, Image | Function-calling, JSON, Assistants API | $0.40 | $1.60 |
| GPT‑4.1 nano | 1M tokens | Text, Image | Function-calling, JSON, Assistants API | $0.10 | $0.40 |
| GPT‑4o | Variable | Text, Image, Audio | Function-calling, JSON, Assistants API, Image | $2.50 | $10 |
| GPT‑4o‑mini | Variable | Text, Image, Audio | Function-calling, JSON, Assistants API | $0.15 | $0.60 |
| o3 | 200k | Text, Image | Web, Python, Memory, Image | $10 | $40 |
| o4‑mini | 200k | Text, Image | Web, Python, Memory, Image | $1.10 | $4.40 |
Looking at the chart above, a few pricing patterns become clear:
- o3 is by far the most expensive model at $10 per million input tokens and a whopping $40 per million output tokens – making it suitable only for specialized tasks where its unique reasoning capabilities are essential.
- GPT‑4o‑mini offers exceptional value at just $0.15/$0.60 per million input/output tokens, making it the go-to choice for high-volume, budget-sensitive applications.
- The “mini” variants consistently offer significantly better pricing than their full-sized counterparts – often with only modest performance trade-offs for many common tasks.
How to Choose the Right Model
With so many options available, selecting the appropriate model for your specific use case becomes critical. Here’s a straightforward approach:
- Need heavy tool use? Go with an o‑series model. The autonomous capabilities make them ideal for tasks requiring web browsing, code execution, or complex research.
- Working with long-context code? GPT‑4.1 is your best bet with its 1M token window and code optimization.
- Need fast multimodal drafts? GPT‑4o provides the best balance of speed and quality for tasks involving text and images.
For more budget-conscious applications, the mini variants offer substantial cost savings while maintaining much of the core functionality of their larger counterparts.
The Competitive Landscape
OpenAI’s models don’t exist in a vacuum. Several strong competitors offer alternatives that may better suit specific needs:
- Claude 3.7 Sonnet ($3/$15 per million tokens) excels with its hybrid reasoning approach and impressive performance on coding benchmarks like SWE‑Bench where it scores over 70%.
- Gemini 2.5 Pro/Flash offers twice the speed of GPT-4o with particularly strong code generation capabilities. The Flash variant provides an excellent balance of speed and quality for many applications. Read more about Gemini 2.5 Flash’s hybrid reasoning capabilities.
- Llama 4 Maverick provides an open-source alternative at just $0.20/$0.82 per million tokens, though with some performance trade-offs compared to closed-source options. Check out my analysis of why Llama 4 models fall short of expectations.
- DeepSeek R1 offers a 128k context window at approximately half the cost of o4-mini, making it worth consideration for cheap reasoning.
This competitive landscape means you’re not locked into OpenAI’s ecosystem. Depending on your specific requirements, exploring alternatives may yield better performance or cost efficiencies.
For a detailed look at OpenAI’s o3 and o4‑mini models and their autonomous tool capabilities, check out this analysis of whether they’re truly must-have additions to your AI toolkit.
Key Considerations for Model Selection
When choosing from OpenAI’s lineup, keep these factors in mind:
Match Task to Reasoning Depth vs. Latency vs. Multimodal Need
Different models optimize for different priorities. For example, the o‑series prioritizes reasoning depth, GPT‑4o focuses on multimodal capabilities and low latency, while GPT‑4.1 excels with long-form content and code. Aligning your model choice with your specific task requirements will yield the best results.
Budget Considerations
For high-volume applications, the mini variants like GPT‑4o‑mini and GPT‑4.1‑nano can provide substantial cost savings. The price differential becomes particularly significant at scale, so carefully assess your expected usage patterns before committing to a specific model.
Tool Stack Requirements
Only the o‑series models provide direct access to web browsing and Python execution within their chain‑of‑thought processes. If your application requires these capabilities, you’ll need to use one of these models despite their higher costs.
Migration Timeline
Be aware that GPT‑4 is scheduled to sunset on April 30, 2025. If you’re still using this model in your applications, now is the time to migrate to one of the newer options. This sunset deadline means testing alternatives sooner rather than later is essential to ensure a smooth transition.
If you’d like a detailed comparison between GPT‑4.1 and other models, I’ve published hands-on test results comparing GPT‑4.1 against Claude 3.7 Sonnet.
Making Sense of OpenAI’s Naming Strategy
The confusion surrounding OpenAI’s model naming isn’t accidental – it reflects the company’s rapid development pace and somewhat disjointed product strategy. While the technical capabilities continue to advance impressively, the naming and positioning of these models create unnecessary complexity for users.
The split between the GPT‑4.1, GPT‑4o, and o‑series families doesn’t always make intuitive sense, and the addition of “mini” and “nano” variants further complicates the decision-making process. This fragmentation can lead to analysis paralysis, where users spend excessive time trying to determine which model best suits their needs.
For a broader perspective on whether OpenAI’s expanding model lineup is actually beneficial, see my thoughts on whether more options are actually better in the AI model space.
Looking Forward
OpenAI’s model lineup will continue to change as new research breakthroughs occur and competitive pressures mount. The current three-family structure may not persist long-term, but understanding the core differences and use cases for each model type will help you navigate future changes.
The migration away from GPT‑4 signals that OpenAI is willing to sunset older models rather than maintain backward compatibility indefinitely. This approach keeps their offerings more current but requires users to actively manage their model dependencies.
As we move forward, expect to see continued specialization in model capabilities, with more task-specific variants likely to emerge. This trend toward specialization means the days of a single “best” model for all applications are effectively over.
Conclusion
OpenAI’s current model lineup might seem confusing at first glance, but breaking it down into the three main families – GPT‑4.1, GPT‑4o, and the o‑series – helps clarify the landscape. Each family serves different needs, from code generation to multimodal tasks to autonomous tool use.
The pricing structure provides clear guidance on budget considerations, with mini and nano variants offering substantial cost savings for many applications. Understanding the competitive landscape also opens up possibilities beyond the OpenAI ecosystem.
By matching your specific task requirements to the appropriate model family and considering factors like budget, tool needs, and migration timelines, you can navigate OpenAI’s complex model ecosystem more effectively. The days of a one-size-fits-all AI model are behind us – embracing the specialized nature of these tools is key to maximizing their value for your specific applications.
