Beyond the backlash: Why experts see promise in GPT-5’s model router

OpenAI, the ChatGPT maker, unveiled GPT-5, the newest model under its generative pre-trained transformer (GPT) model, last week. The model launch came over six months after the Sam Altman-led company introduced the preceding GPT-4.5 model. 

Of the several new features introduced with GPT-5, one of the most intriguing is the model router. It is a system that selects between the different variants of the model, such as the fast performer, deeper thinking version, or a fallback mini model, based on the nature of the query. 

OpenAI described it as “a real‑time router that quickly decides which to use based on conversation type, complexity, tool needs, and your explicit intent”. 

Despite the promise of improved optimisation and resource allocation, this system left a lot of users and developers bitter. Users felt a loss of control and transparency when the familiar option to choose specific models like GPT-4o was removed and replaced by an opaque router system, leaving them blindsided.

Many also perceived performance issues with GPT-5, describing it as slower, less creative, or more error-prone than previous models and the auto-switching made it difficult to pinpoint the exact cause, as they could not tell which model was actually responding.

Taking the criticism into account, OpenAI’s Altman took to the social networking website X, rolling out the Auto, Fast, and Thinking, which will allow users to pick their model of choice. Only the Auto model, when enabled, is expected to function as the router system as introduced initially. 

Industry leaders’ optimism about model router

But the experts believe that the model router, despite its shortcomings, should not be entirely written off. A model router tilts the experience towards ‘abstraction and automation’, they say. 

According to Jacob Joseph, VP – Data Science, CleverTap, users no longer have to understand model names or performance trade-offs; the system just routes your query to the right engine, which is an efficiency gain. For providers like OpenAI, it allows compute usage optimisation behind the scenes.

“But the real opportunity lies in what this signals: a shift toward model-agnostic interfaces. This is likely the future, where users don’t talk to a model; they talk to a capability, and the system handles orchestration invisibly. From an AI infrastructure standpoint, it’s elegant. And with the increasing cost and diminishing returns of monolithic models, it’s arguably the only scalable path forward,” added Joseph. 

Calling it an extension of the agentic paradigm, which is already prevalent, Samiksha Mishra, Director AI at R Systems, said that the model router is not novel and just introduces a smaller model/system in between to route the queries in an appropriate format. This modular approach enables independent upgrades to routing logic, reasoning models, and lightweight models without retraining the entire system, while also overcoming GPU memory limits and single-model performance plateaus.

“It is just a value add to existing LLMs from OpenAI. Fewer hallucinations have been claimed, but a thorough evaluation is still pending in the research community. This isn’t the future; it’s the present catching up to economics. Within 18–24 months, routing could be standard AI infrastructure,” she added. 

Echoing similar sentiments, Satyajith M, CTO at Hexaware Technologies, said that the router-based architecture is very likely to become the dominant paradigm for future language models, with compelling efficiency gains. The current trajectory of AI development strongly suggests moving away from monolithic 'do-everything' models toward more sophisticated, specialized systems.

“Perhaps most importantly, the rise of autonomous agents expected by 2025 will fundamentally change how we interact with AI systems. These agents need to seamlessly switch between different types of reasoning—from data analysis to creative problem-solving to code generation—often within a single conversation. A router architecture naturally supports this multi-modal intelligence better than any single model could,” he said. 

Path to AGI

Several experts see the router-based design as more than just an efficiency upgrade—it could also foreshadow how advanced AI systems, and possibly AGI, might be structured. AGI, or artificial general intelligence, is a futuristic concept where machines have human-like capabilities.

Instead of a single, all-knowing brain, such systems may function as a coordinated network of specialized models, with a central layer deciding which to deploy for a given task. This modular, task-adaptive approach is well-suited to supporting autonomous agents and is also expected to address today’s scalability challenges by optimising compute use, reducing costs, and improving performance. 

GPT-5 is still far from human-like intelligence, but its ability to orchestrate multiple expert models and shift reasoning modes suggests a path toward more versatile AI.