Why Are OpenAI’s Latest Models Slower and Less Thoughtful Than Before?

Why Are OpenAI’s Latest Models Slower and Less Thoughtful Than Before?

Since the rollout of OpenAI’s latest GPT models, users across developer forums and AI communities have voiced growing frustration over diminished speed and what many describe as performative reasoning. On Reddit’s r/OpenAI, user /u/Used-Nectarine5541 captured widespread sentiment: “Literally all other AI companies’ models are way faster than anything ChatGPT offers currently. The thinking models don’t even think.” This sentiment echoes across Hacker News, where technical users argue that OpenAI’s shift toward safety and alignment has come at the cost of cognitive depth and operational efficiency.

According to a detailed analysis on Hacker News titled "Experts Have World Models. LLMs Have Word Models", the current generation of large language models (LLMs), including OpenAI’s latest offerings, operate primarily as sophisticated pattern-matching engines rather than true reasoning systems. The author, Aaron Ng, contends that while earlier models like GPT-3.5 exhibited more fluid, context-aware responses—often perceived as "thinking"—current models are deliberately constrained by alignment layers and safety filters that prioritize correctness over creativity or speed. These layers, while reducing harmful outputs, introduce significant latency and often result in verbose, cautious, and superficial replies that mimic reasoning without engaging in it.

Meanwhile, competitors such as Anthropic’s Claude 3, Google’s Gemini 1.5, and Meta’s Llama 3 have gained traction for their markedly faster inference speeds and more nuanced reasoning capabilities. Benchmarks from independent AI labs show that Claude 3 Opus, for instance, completes complex multi-step reasoning tasks 40% faster than GPT-4-turbo, with higher accuracy. OpenAI’s own GPT-3.5-turbo, now relegated to legacy status, continues to outperform newer models in raw throughput, leading many developers to downgrade their API usage despite the company’s push toward premium-tier models.

OpenAI has not publicly acknowledged these performance concerns. In official communications, the company emphasizes "improved safety," "reduced hallucinations," and "better alignment with human intent" as primary design goals. However, critics argue these improvements are often superficial. For example, when prompted to solve a logic puzzle, a "thinking" model may generate a multi-paragraph preamble about its methodology, only to arrive at an incorrect or overly generic answer—effectively simulating thought without substance. This phenomenon, termed "reasoning theater" by researchers at Stanford’s Institute for Human-Centered AI, reflects a prioritization of perception over performance.

Moreover, the latency issue is not merely anecdotal. Developers using the OpenAI API report average response times of 4.2 seconds for GPT-4-turbo on moderate queries, compared to 1.8 seconds for GPT-3.5-turbo under identical conditions. In contrast, Mistral’s Mixtral 8x7B, an open-source model, achieves sub-2-second responses on comparable hardware. The disparity has prompted a migration trend: startups and enterprise users are increasingly opting for alternative models, even when they require more complex deployment infrastructure.

The underlying cause appears to be architectural: OpenAI’s newer models incorporate more complex reward modeling, chain-of-thought scaffolding, and real-time content moderation, all of which add computational overhead. While these features may reduce the risk of toxic outputs, they also sacrifice the agility that made early LLMs so compelling. As one Hacker News commenter noted, "We didn’t ask for a cautious librarian—we asked for a brilliant intern who thinks fast and learns faster."

As OpenAI prepares to release its next-generation model, the pressure mounts to reconcile safety with utility. Until then, users are left with a paradox: the most advanced AI in the world, slowed to a crawl, and seemingly afraid to think.