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Since GPT-4, large language models have become more varied in what they can process and how people can use them. Model families now distinguish fast, general-purpose, and deeper-reasoning options; multimodal support has expanded; and availability can differ between an app and an API. That is a change in product design as well as capability—not proof that every newer model is better at every task.
GPT-4 was a multimodal baseline, not a text-only model
When OpenAI announced GPT-4 on March 14, 2023, it described a model that accepted image and text inputs and produced text outputs. Its technical report also described Transformer next-token pretraining followed by post-training alignment. So the shift since GPT-4 is not simply from text to multimodality: GPT-4 already had image input, but newer model families have made modality and model choice more prominent parts of their design.
OpenAI reported that GPT-4 was 82% less likely than GPT-3.5 to respond to disallowed requests and 40% more likely to produce factual responses on the company’s internal evaluations. Its technical report also said GPT-4 scored around the top 10% of test takers on a simulated bar exam. These are company-reported results on particular evaluations, not guarantees about responses in everyday use or a shared comparison with other providers.
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Model families made the choice less one-size-fits-all
Tiered families appeared alongside broader multimodal ambitions
Google announced Gemini 1.0 in December 2023 as a multimodal model family and described it as trained on Google TPU v4 and v5e infrastructure. Anthropic’s March 2024 Claude 3 launch introduced three tiers: Haiku, Sonnet, and Opus. Anthropic stated an initial 200K-token context window for Claude 3. The packaging signaled a more explicit choice among speed, capability, and workload fit, rather than treating one flagship model as the only option.
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Anthropic also said Claude 3 Opus outperformed peers on many common evaluations and reported a twofold accuracy improvement over Claude 2.1 on challenging open-ended questions. Those are Anthropic’s claims about its evaluations; they do not establish a neutral ranking across companies.
“Multimodal” does not mean every model handles every medium
Support varies by model and by direction: a model may accept an image, audio, or video input without generating that same medium as output. GPT-4’s documented image-and-text input and text output illustrate why it is more useful to identify the exact model’s supported inputs and outputs than to label a whole generation of models as capable of handling everything. Check the model’s own documentation for the specific interface or API you plan to use.
Reasoning became a visible product choice
By 2025, vendors were describing deeper reasoning as an explicit capability rather than leaving it implicit in a single model label. Anthropic documents “extended thinking” for complex tasks, and its platform release notes describe Claude Opus 4 and Sonnet 4 as supporting extended-thinking capabilities. This is the vendor’s description of how those models can be used, not a promise that they will reason correctly on every difficult task.
OpenAI’s August 7, 2025 GPT-5 announcement described a unified system with a fast model, a deeper-reasoning model, and a real-time router that selects between them. This makes routing and reasoning mode part of the product design: the user may not always be choosing a fixed model in the same way as before. The practical trade-off is that deeper reasoning may be useful for harder tasks, while a faster mode can suit simpler requests; the available evidence here does not quantify a universal latency or cost trade-off.
What vendor evaluations say—and what they do not
OpenAI reported that GPT-5 with thinking performed better than o3 on selected capabilities while using 50–80% fewer output tokens. With web search enabled, it reported GPT-5 responses were about 45% less likely to contain a factual error than GPT-4o responses; GPT-5 with thinking was about 80% less likely to contain a factual error than o3. OpenAI also reported that GPT-5 reasoning responses reduced deception rates from 4.8% for o3 to 2.1% on a large set of production-representative conversations.
Each figure is an OpenAI-reported result tied to a particular comparison and evaluation setup. It should not be read as a universal improvement across prompts, tasks, products, or providers. The evidence does not provide a standardized cross-vendor test under identical conditions, so it cannot support a definitive ranking of the best current LLM.
For a meaningful comparison, hold the task and date constant, then check the model version, evaluation dataset, baseline, and who ran the test. Also compare what the model accepts and produces, whether it has a deeper-reasoning mode, its documented context capacity, and the specific tier and access surface being tested. A longer context window by itself does not show that a model performs better.
Availability now depends on product surface and date
Model access is not a single yes-or-no fact. A model can be available in an API but not in a particular app, or be retired from one surface while remaining unaffected elsewhere. OpenAI’s ChatGPT release notes said GPT-4o, GPT-4.1, GPT-4.1 mini, and o4-mini were retired from ChatGPT on February 13, 2026, while stating there were no API changes at that time. That notice describes ChatGPT availability on that date; it should not be generalized into a claim about API status, regional access, or availability after that date.
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When checking a model for current use, verify the named version, whether you mean the consumer app or API, regional availability, and any dated retirement or migration notice. These details can change independently of a model family’s general capability claims.
How to interpret the evolution
The most defensible account is a shift from a small number of flagship-model choices toward broader families with different tiers, modalities, reasoning modes, and lifecycle rules. Published evaluations offer clues about performance on the tasks each vendor tested, but without a common independent evaluation they do not prove universal superiority. For a real decision, compare the exact models on your own representative tasks and confirm their current access and supported inputs and outputs.
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