AI companies are notoriously bad at naming things. It started simple with GPT-1, 2, 3, and 4. Then 3.5 and the ‘Turbo’ variants muddied it up.
Many GPT-4 updates? Nameless dates like ‘1114.’ Pure confusion.
GPT-4V for vision made sense. GPT-4o? Disaster.
Speech-to-text hears it as ‘4.0.’ If dictation botches your product name, you blew it. Plus, that lowercase ‘o’—endless corrections.
Worse with o1-preview and o1-mini. Reasoning models with ‘effort’ params (low/medium/high). Is o1-mini high better than o1-preview low? No clue.
Shipmas 2024 skipped o2 (copyright snag), jumped to o3. Dropped o3-mini first. Comparing to o1? Chaos.
o4-mini next—generation ahead of o3, but ‘mini.’ When to pick what?
GPT-5 (Sept 2025) simplified: GPT-5, GPT-5 mini, GPT-5 nano. Reasoning effort still tricky—stick to max power.
Then GPT-5 Codex for devs, 5.1, 5.2. 5.2 costs more, regresses in spots, shines elsewhere.
For $20 plan: GPT-5.2 standard for quick work, extended thinking for tough stuff.
Suffixes don’t help: mini/nano = fast/cheap, less precise. Preview = beta, not always best. Turbo = tuned context.
| Generational Era | Naming Approach | Confusion Level |
|---|---|---|
| GPT-1 to GPT-4 | Numeric sequence | Low – Simple and intuitive |
| GPT-4.x / 4o / o1 | Letters, dates, and lowercase variants | High – Multiple formats colliding |
| o1 / o3 / o4 | Preview/mini suffixes + effort params | Severe – Requires mental spreadsheet |
| GPT-5 era | Core/mini/nano + decimal updates | Moderate – Better structure, decimal confusion |
The evolution of OpenAI naming from simple to needlessly complex.
This is a real problem for users who just want to get work done without having to memorize a complex taxonomy of model variants. When speech-to-text systems can’t even get your product name right, calling it ‘4.0’ instead of ‘4o,’ that’s a clear sign something went wrong in branding.
The lowercase o issue alone probably cost users countless hours of corrections and confusion. Was it a design choice or just laziness? Probably the latter.
The reasoning models introduced another layer of complexity with effort parameters. Understanding whether o1-mini with high effort outperforms o1-preview with low effort shouldn’t require a spreadsheet. It’s like comparing apples to slightly different apples.
Then came the copyright issue with o2, forcing a jump straight to o3. If you’re counting on ‘2’ as part of your naming scheme, skipping it breaks continuity in the worst way. Releasing o3-mini before the base o3 model made things even messier.
Now with o4-mini released before any regular o4, we have a model that’s technically a generation ahead of o3 but marketed as ‘mini.’ When to pick what becomes a guessing game.
Thankfully, GPT-5 in September 2025 brought some sanity back: GPT-5, GPT-5 mini, and GPT-5 nano. Three tiers, clear hierarchy. The reasoning effort parameter still complicates things, so the best advice is just to use the most powerful one and move on.
Then came GPT-5 Codex for developers, followed by versions 5.1 and 5.2. The 5.2 release is particularly confusing—it’s more expensive than its predecessor, actually regresses in some areas while improving in others. Not a straightforward upgrade path.
For the $20/month plan, the current recommendation is to use GPT-5.2 with standard reasoning for quick iterations and switch to extended thinking for more difficult tasks. Simple enough guidance, but getting there required navigating a maze of options.
OpenAI forums are filled with users begging for a better naming scheme. Some suggest approaches like OS codenames or persona-based aliases—anything to make this more digestible.
Here’s the confusing part: the ‘o’ in GPT-4o supposedly stands for ‘omni’ (multimodal capabilities). But the reasoning models (o1, o3, o4) use ‘o’ for a completely different reason. Same letter, different meanings—perfect for confusion.
The result of all this is endless second-guessing. Users spend more time pondering model selection than actually using the tools. The practical advice has become simple: just pick the most powerful option available and ignore the confusing labels.
Model companies have been notoriously terrible at naming their products. They could arguably let the models name themselves at this point—they’d probably do a better job than random letters and numbers.
This isn’t rocket science. Clear, hierarchical naming would make everyone’s life easier. When your naming scheme requires a flowchart to understand, you’ve already failed.
