{"id":13148,"date":"2025-11-01T14:47:28","date_gmt":"2025-11-01T14:47:28","guid":{"rendered":"https:\/\/www.yiaho.com\/why-is-gpt-5-considered-terrible-the-technical-truth-behind-this-openai-model\/"},"modified":"2025-11-01T14:47:28","modified_gmt":"2025-11-01T14:47:28","slug":"why-is-gpt-5-considered-terrible-the-technical-truth-behind-this-openai-model","status":"publish","type":"post","link":"https:\/\/www.yiaho.com\/en\/why-is-gpt-5-considered-terrible-the-technical-truth-behind-this-openai-model\/","title":{"rendered":"Why is GPT-5 considered terrible? The technical truth behind this OpenAI model"},"content":{"rendered":"

On August 7, 2025, OpenAI unveiled GPT-5, its most advanced language model to date, meant to mark a decisive step toward more unified and powerful artificial intelligence. While waiting for GPT-6<\/a>, the current model was presented as a multimodal system integrating advanced reasoning, image processing, and code generation, promising to surpass its predecessors like GPT-4o<\/a> in fluidity and efficiency. <\/p>\n

Yet barely a week after its launch, forums like Reddit and X were flooded with harsh criticism. Paying users threatened to cancel their subscriptions, calling the model “cold,” “useless,” or even a “regression.” <\/p>\n

But beyond the noise and with necessary hindsight, are there real technical flaws? The Yiaho team explores the underlying reasons, drawing on expert analyses and independent tests, to demystify what makes GPT-5 so disappointing for many, considered by some as simply “terrible.” <\/p>\n

GPT-5 terrible? Excessive expectations and the launch context <\/h2>\n

To understand the disappointment, we need to go back to the anticipation around GPT-5<\/a>.<\/p>\n

For months, Sam Altman, CEO of OpenAI, teased a “revolutionary” model capable of reasoning like a PhD-level expert in any field. The launch, broadcast live, aimed to consolidate OpenAI’s dominance against competitors like Anthropic<\/a> (Claude) or Google (Gemini<\/a>). But the event turned into a fiasco: poorly labeled graphs showed visually misleading bars, contradicting official figures on coding and reasoning performance. <\/p>\n

Worse, the model was imposed as the default in ChatGPT, removing access to previous versions like GPT-4o without prior warning. This triggered a wave of backlash, with thousands of complaints about usage limits (200 messages per week for “reasoning” mode in the free or Plus version) and the absence of rollback options. <\/p>\n

The model’s technical parameters<\/h3>\n

Technically, GPT-5 is not a single monolith, but an intelligent “router” that dynamically selects between several variants: a fast “standard” version for simple tasks, an economical “mini,” and a “Pro” for complex problems involving extended reasoning. This architecture aims to optimize costs and latency, with an extended context of 400,000 tokens in API<\/a>. <\/p>\n

On paper, it’s ingenious. In practice, that’s where the trouble begins. The technical flaws: beyond subjective complaints. The criticisms are not limited to a “lack of personality,” although GPT-5 is perceived as more “sterile” and concise than GPT-4o, with overly brief responses and an absence of humor or empathy. <\/p>\n

Independent tests reveal deeper problems, rooted in the model’s architecture and training.<\/p>\n

Also read on this topic: Failed GPT-5 Launch: Users Prefer GPT-4o<\/a><\/p>\n

1. The faulty router: an invisible bottleneck<\/h3>\n

The heart of GPT-5 is its routing system, which analyzes the query to choose the right sub-model. But this mechanism, supposed to be automatic, proved unstable from launch. Users report that simple tasks are directed to underperforming variants, making the model “dumb” for basic operations like letter counting or elementary logic. <\/p>\n

Sam Altman<\/a> admitted that the router “had bugged” on launch day, forcing a quick update. Result: inconsistent responses, where the model “thinks” unnecessarily long for trivial queries, or jumps directly to a “Pro” variant that overwhelms the user with verbose text without added value. <\/p>\n

Technically, this stems from insufficient router training on real scenarios. Unlike a simple manual switch (as in previous versions), this system relies on probabilistic meta-modeling, sensitive to data biases. Benchmarks like SimpleBench show that GPT-5 only achieves 56.7% success, behind smaller rivals, because routing prioritizes efficiency over accuracy. <\/p>\n

2. Persistent hallucinations and memory weaknesses<\/h3>\n

Despite the promises, GPT-5 has not solved a plague of LLM<\/a>s: hallucinations. Post-launch studies indicate that the factual error rate remains at 40%, similar to GPT-4, particularly in health and sciences. <\/p>\n

For example, on HealthBench, it scores 46.2%, insufficient for reliable medical advice, where an error can be critical.<\/p>\n

More seriously, long-term memory management is faulty. In extended conversations, the model forgets initial constraints once the context is exceeded (even at 32K tokens in standard mode), leading to repetitions or contradictions. <\/p>\n

An MIT analysis points out that GPT-5 processes all tokens with equal weighting, without abstracting key elements as humans do, causing “context poisoning” where important details drown in noise.<\/p>\n

This makes multi-turn sessions (like code debugging) frustrating: the model “forgets” variables or rules, forcing constant reminders.<\/p>\n

3. Uneven performance in coding and multimodality<\/h3>\n

OpenAI touts GPT-5 as its “best model for coding,” with 74.9% on SWE-bench. Yet developers report regressions: the model generates functional code for isolated tasks, but struggles on complex projects involving state, integration, or multi-file coordination. <\/p>\n

The limited context (32K tokens in practice to avoid costs) prevents an overall view, leading to erroneous imports or dangerous renaming. If you want a good free and unlimited AI for coding<\/a>, there’s one on Yiaho! <\/p>\n

On the multimodal side, the advances are cosmetic. Although capable of processing images and voice, GPT-5 often delegates to legacy subsystems like GPT-4o for visual generation, creating inconsistencies. Tests show it fails 50% of the time on basic visual math, versus 0% for smaller local models like Gemma 2B. <\/p>\n

GPT-5: an ambitious but immature model<\/h2>\n

GPT-5 is not “terrible” in the absolute sense\u2014it excels in math and isolated agentic tasks, helping physicists and biologists accelerate their discoveries.<\/p>\n

But its technical flaws\u2014an unstable router, faulty memory, and hybrid multimodality\u2014make it unpredictable for everyday use. OpenAI seems to have prioritized economic optimization (computing costs) over robustness, making the model “cheap” but frustrating for power users. <\/p>\n

The OpenAI<\/a> response? Quick updates, like the return of GPT-4o for Plus subscribers, and a more detailed prompting guide emphasizing the model’s increased “steerability.” Experts advise treating prompts like code: version them, test them, and force explicit reasoning to work around limitations. <\/p>\n

Yet this reveals a broader problem: LLMs like GPT-5 still require intensive human engineering to shine, far from the promised AGI.<\/p>\n

Toward the future: lessons and perspectives with GPT-6<\/h2>\n

The GPT-5 fiasco is not the end of OpenAI\u2014on the contrary, it underscores the market’s maturity. OpenAI is expected to go public in 2026, and the company is the global AI reference. But with rivals like Claude 3.7<\/a> offering more consistency, and emerging decentralized approaches to address computing limitations, the pressure is high to iterate. <\/p>\n

OpenAI is already teasing GPT-6<\/a> for December 2025, with a truly new architecture and native multimodality. In the meantime, the lesson is clear: AI is not magic. Its technical advances, while real, still run up against fundamental challenges like memory and reliability. For users, the trick is to adjust expectations and prompts<\/a>, rather than hoping for an infallible model, or to use all the different models available on Yiaho. In short, GPT-5 is not terrible\u2014it’s just… humanly imperfect! <\/p>\n","protected":false},"excerpt":{"rendered":"

On August 7, 2025, OpenAI unveiled GPT-5, its most advanced language model to date, meant to mark a decisive step toward more unified and powerful artificial intelligence. While waiting for GPT-6, the current model was presented as a multimodal system integrating advanced reasoning, image processing, and code generation, promising to surpass its predecessors like GPT-4o… Read More »Why is GPT-5 considered terrible? The technical truth behind this OpenAI model<\/span><\/a><\/p>\n","protected":false},"author":4,"featured_media":13149,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"neve_meta_sidebar":"","neve_meta_container":"","neve_meta_enable_content_width":"off","neve_meta_content_width":70,"neve_meta_title_alignment":"","neve_meta_author_avatar":"","neve_post_elements_order":"","neve_meta_disable_header":"","neve_meta_disable_footer":"","neve_meta_disable_title":"","neve_meta_reading_time":"","footnotes":""},"categories":[49],"tags":[],"class_list":["post-13148","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-news"],"yoast_head":"\nWhy is GPT-5 considered terrible? The technical truth behind this OpenAI model<\/title>\n<meta name=\"description\" content=\"GPT-5, released on August 7, 2025, disappoints despite its promises. 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