When it comes to naming AI models, it feels like we’re in a bad sitcom. There’s bad naming, and then there’s really bad naming. OpenAI’s model names are so confusing they make you want to pull your hair out. You see names like GPT-4o, o4-mini, and o3, and you’d naturally think the numbers indicate progression: o3 is worse than o4-mini, and o4-mini is worse than GPT-4o, with GPT-4o being the best. If you thought that, you’d be dead wrong. The actual hierarchy is the exact opposite: o3 is the strongest, o4-mini is the mid-tier, and GPT-4o is, in fact, the least capable of the three. A normal person is never going to figure that out from the names alone. It’s a complete mess.
Just when you think OpenAI has cornered the market on confusing nomenclature, along comes Qwen with their latest model: Qwen3-235B-A22B-Thinking-2507. That one name is longer than all the current OpenAI model names combined. If you already understand what that name means without looking it up, you probably need to get a life. This is a systemic problem that creates unnecessary barriers to understanding and adoption in the AI space. It’s a clear sign that some companies are missing the point when it comes to user experience beyond the raw tech.
The Intentional Opacity of OpenAI’s Naming
OpenAIs model naming conventions have drawn widespread criticism, not just from users, but from within the AI industry itself. Industry experts and branding specialists have called it deliberately opaque and willfully obfuscatory. Anthony Shore pointed out that using arbitrary letters and numbers just “shows they don’t care if the names are understandable to users.” The names lack distinctiveness and power. This makes them hard to find, hard to remember, and impossible to tell apart.
Its not like OpenAI is unaware of this. CEO Sam Altman and Chief Product Officer Kevin Weil have publicly admitted their model names are absolutely atrocious and acknowledged the need for a revamp. But heres the kicker: they havent prioritized fixing it. Altman even joked about the randomness of names like o3 mini high and said that while ChatGPT is a terrible name, its probably too established to change now. This internal acknowledgment, coupled with a lack of action, shows a disconnect between understanding the problem and being willing to invest in a solution that benefits the user base. Its the kind of thing that makes you wonder if theyre just too busy chasing the next big model to care about basic usability.
| Model Type | Assumed Naming Logic | Actual Naming Implications | Actual Capability (Relative) |
|---|---|---|---|
| GPT-4o | Newest, Most Advanced | “o” for Omni, a general purpose model | Least capable of the ‘o’ series |
| o4-mini | Smaller, Less Capable | Third iteration (they skipped o2), ‘mini’ for smaller/faster | Medium capability |
| o3 | Older, Less Advanced | Second iteration, general purpose (no ‘mini’) | Most capable of the ‘o’ series |
Community and User Confusion: A Recurring Theme
The names actively mislead about hierarchy and intended use. Users logically assume “o3” is less advanced than “o4-mini,” and “o4-mini” less than “GPT-4o.” This is wrong. The real capabilities are reversed. Community discussions show widespread confusion. Some users have pointed out that a version number like “4.5” doesn’t mean it’s an improvement over “4o,” despite how software versioning usually works. It just creates a headache.
Its a peculiar thing, too, because OpenAI has shown they can name things well. Concepts like “Sora” (their video generation model) and “DALL-E” (their old image generation model) are distinctive, memorable, and even thematic. They bridge the gap between technical innovation and public accessibility. Yet, their core language model names remain a technical, uninspiring alphanumeric soup. Its like they put all their creative energy into branding the ancillary products and left the most important ones the LLMswith names generated by a random string algorithm. Even their new image generation has lost all sense of good naming. It’s called “”. That’s not a mistake There’s no official name for it. It’s GPT-4o native image generation, but doesn’t have it’s own name. In the API it’s GPT-Image-1, but that’s not he name in ChatGPT. This inconsistency just adds to the frustration for anyone trying to keep up.
The Psychological Impact of Misleading Names
Beyond mere confusion, misleading model names can have a subtle but significant psychological impact on users. When a user consistently finds that their logical assumptions about a product’s hierarchy are incorrect, it erodes trust in the provider. It’s a form of cognitive dissonance the mental discomfort of holding contradictory beliefs. Users expect that a company’s product naming reflects a coherent, logical progression of features and capabilities. When it doesn’t, it creates a feeling of being manipulated or, at best, disregarded. This isn’t just about picking the wrong model; it’s about the feeling that the company isn’t speaking a language you can understand, fostering a sense of alienation.
This is particularly problematic in the AI space, where the technology itself can feel opaque to many. Clear, intuitive naming could serve as a bridge, making AI more approachable. Instead, it acts as another barrier. It’s akin to having a map where north isn’t always north, or where street numbers jump randomly. You can still get where you’re going, but the journey is needlessly frustrating and filled with doubt. This frustration can lead to users sticking with older, familiar models even if newer, more appropriate ones exist simply because the effort to decode the naming scheme feels too high. This stifles adoption of newer, potentially more efficient models, which is counterproductive for both users and the companies developing them. It also puts undue pressure on external documentation and community forums to demystify what should be transparent from the start.
The Qwen Escalation: When Naming Becomes Absurdist
The trend of convoluted, lengthy names isn’t just an OpenAI problem. Qwens Qwen3-235B-A22B-Thinking-2507 is a prime example of this reaching an extreme. This name is so long and complex that only core insiders could hope to decode it. For the average user, its impenetrable, further alienating them and emphasizing exclusionary branding. It’s almost as if these companies are actively trying to make their products less accessible. It’s a classic example of prioritizing internal technical identifiers over external user experience. When a name needs a user manual to understand, you’ve failed at branding.
The underlying issue is that OpenAIs model names are often tied to internal architecture or deployment variants, not any user-facing logic or progression. This creates a recurring disconnect between the technical rationale and the broader user base’s expectations. Users expect that higher numbers or newer versions mean better or more capable models, something thats built into decades of software updates. When the reverse is true, or the names are simply meaningless, it causes confusion and distrust. This problem isn’t unique to OpenAI; it’s a symptom of a broader challenge in tech branding: how to balance scientific accuracy with user-friendly communication. Its something I think about a lot, especially when looking at the intricacies of AI agents and their security, as seen in my discussion on MCP security.
Comparing Approaches: The Good, The Bad, and The Unintelligible
While OpenAIs general-purpose model names are a mess, they have shown a knack for creative, distinct names for other projects. Sora evokes aerial wonder for video generation, and DALL-E has a surreal, artistic quality that fits an AI image generator. These names are praised for their distinctiveness and thematic relevance, making them memorable and marketable. Why this thoughtfulness doesnt extend to their core language models is baffling.
Then you have Qwens approach. If OpenAIs names are confusing, Qwens are a full-blown cryptic code. Qwen3-235B-A22B-Thinking-2507 isn’t just a model name; its practically an internal project ID. It includes details like parameter count (235B), possibly architecture specifics (A22B), and even a date stamp (2507, perhaps for July 25th). While technically informative for an engineer, its completely unhelpful for anyone trying to select a model based on its capabilities or position in the product lineup. It alienates ordinary users and reinforces an exclusionary branding strategy. This extreme complexity undermines any attempts at broad adoption or clear market positioning.
The Impact on Users and the Industry
The problem goes beyond mere branding. Confusing names create real friction for users attempting to select the right model for their needs. If you’re a developer or a business trying to integrate one of these models, you shouldn’t have to consult a secret decoder ring to figure out which version is actually better or cheaper. This often leads to trial and error, wasted resources, and frustration. When I talk about AI-assisted SEO, I always stress that delivering value is the main thing, and confusing names just add unnecessary hurdles. The ease of use and clarity are paramount, even for technically complex products.
For instance, lets consider the implications for those developing with these models. If identifying the correct model for a specific task is a guessing game, it slows down development and increases the chances of using a suboptimal solution. Suppose a developer thinks GPT-4o is the pinnacle. They might build an application around it, only to find later that o3 would have delivered superior performance for the same or less cost. That’s rework, budget issues, and a headache that could have been avoided with a clear naming strategy. This is not some abstract marketing concern; it directly impacts developer efficiency and project success. It also adds a layer of complexity to competitive analysis, as seen when I evaluated Qwen3-Coder against other models.
Furthermore, this naming chaos creates a barrier to broader adoption. As AI models become more integral to various industries, the ability for non-specialists to understand and discuss them becomes crucial. Clear names facilitate this. Opaque names impede it. Its hard enough to get businesses to adopt new tech; making the entry point confusing only makes it harder. It prevents the kind of widespread understanding and integration that will drive the next phase of AI development.
The Economic Cost of Naming Chaos
Beyond the frustration and lost productivity, there’s a tangible economic cost to this naming chaos. Businesses investing in AI solutions need to make informed decisions. If the names don’t clearly signify capability or cost-efficiency, it leads to suboptimal choices. Companies might pay for a more powerful model than they need, or conversely, undershoot their requirements, leading to performance issues and subsequent overhauls. This directly impacts budgets and resource allocation. For startups and smaller businesses, every dollar counts, and misinterpreting a model’s capabilities due to a bad name can be a costly mistake.
Moreover, the need for extensive internal training and external documentation to clarify these names adds another layer of expense. Technical writers, trainers, and support staff spend countless hours trying to explain what should be self-evident. This is a drain on resources that could be better spent on innovation, feature development, or improving core AI capabilities. In a competitive market, such inefficiencies can put companies at a disadvantage. It’s a hidden tax on the AI ecosystem, levied by poor branding decisions.
What Could Be Done: A Call for Clarity
So, what’s the solution? Its not rocket science. AI companies need to adopt naming conventions that are:
- Intuitive: Names should reflect capability or progression (e.g., higher numbers mean better performance or newer versions).
- Distinctive: Each model should have a unique, memorable name that avoids confusing overlap.
- Communicative: The name should offer some hint about the models primary use case, size, or core feature (e.g., “-vision” for multimodal, “-fast” for lighter versions).
- Consistent: Apply a logical system across the entire model family instead of a random “letters-and-numbers salad.”
For OpenAI, this might mean a complete overhaul of their “o” series to align with conventional versioning. Perhaps something like “GPT-Omni-Pro,” “GPT-Omni-Standard,” and “GPT-Omni-Lite.” For Qwen, it means moving away from internal code numbers and towards actual product names. It’s about prioritizing the user’s journey from discovery to implementation. This isn’t just about good aesthetics; it’s about good product management. Ive often said that model companies are terrible at naming their products, claiming they could just let the models name themselves and theyd honestly do a better job. This sentiment holds true here. If youre building a powerful tool, dont make it harder for people to find and use it because of a bad name.
The Role of Industry Standards and Best Practices
The challenge of AI model naming isn’t unique to individual companies; it’s an industry-wide issue that could benefit from some form of standardization or best practices. While formal regulation might be overkill, industry consortia or leading AI research institutions could propose guidelines. These guidelines could cover aspects like:
- Semantic Consistency: Ensuring that suffixes (like ‘-mini’, ‘-turbo’) consistently denote specific attributes (cost, speed, size) across different models and providers.
- Version Clarity: Establishing clear rules for numerical progression, so higher numbers reliably indicate newer or more capable models, aligning with decades of software versioning norms.
- Purpose-Driven Names: Encouraging names that hint at a model’s primary use case (e.g., ‘CodeGen’, ‘VisionPro’) rather than purely internal technical identifiers.
- Multimodal Indicators: Developing clear, consistent ways to indicate multimodal capabilities without resorting to ambiguous letters like ‘o’.
Such guidelines wouldn’t stifle innovation in naming creative projects like Sora or DALL-E, but they would bring much-needed order to the core foundational models that businesses and developers rely on. Its about creating a shared language for the AI community, much like how APIs and programming languages adhere to certain conventions to facilitate interoperability and understanding. Without this, we risk fragmenting the user experience and hindering the broader adoption of AI. It’s a collective responsibility to make AI accessible, not just technically, but conceptually.
The Urgency for Change: Why Now?
The need for clear, intuitive naming is more urgent than ever because the AI landscape is rapidly expanding. We’re not just talking about a handful of models anymore. The proliferation of specialized models, fine-tuned versions, and different deployment strategies means users are faced with an overwhelming array of choices. If each new model comes with a name that requires a decoder ring, the situation will quickly become untenable. As I’ve discussed in relation to the incredible capabilities of the ‘Summit’ AI model and the strategic importance of projects like OpenAI’s Stargate, the scale of AI development is massive. This scale demands clarity, not obfuscation.
Moreover, as AI becomes integrated into more critical applications, the stakes of misinterpreting a model’s capability increase. Imagine a financial institution or a healthcare provider inadvertently using a less capable model due to a confusing name, leading to flawed analysis or incorrect diagnoses. While this might seem like an extreme example, it highlights the real-world consequences of poor product identification. Its not just about developer convenience; it’s about reliability and accountability.
The current approach also risks alienating a broader audience. As AI moves from niche technical circles into mainstream business and consumer applications, the barrier to entry needs to be lowered. User-friendly names are a fundamental part of this. They signal that the company cares about its users’ experience and wants to make its technology accessible. Failing to address this basic usability issue can slow down public acceptance and adoption, ultimately hindering the very progress these companies are trying to achieve. Its a self-inflicted wound that, while seemingly minor, has far-reaching implications for the future of AI. The time to fix this is now, before the “letters-and-numbers salad” becomes an insurmountable linguistic barrier.
The criticism of OpenAI’s and other AI companies model naming is not just whining; it’s a valid concern backed by industry commentary, internal admissions, and user feedback. The current approach creates unnecessary barriers to understanding and adoption. The names are either misleading or impenetrable. As AI models and their uses continue to expand, the need for clear, intuitive, and accessible naming is more urgent than ever. It’s a fundamental aspect of product usability that remains woefully unmet by the current trend. It’s time for these companies to stop treating their product names like internal serial numbers and start treating them like real brands that users actually interact with.