The 10 Biggest ChatGPT-5 Problems & How to Fix Them

ChatGPT-5’s rollout has triggered backlash not just over one-off mistakes, but over how the system reshapes long-running user workflows—especially through a “single model” experience that actually routes requests behind the scenes. The core complaint is that the router often steers users toward faster, less “reasoning” behavior to protect OpenAI’s GPU capacity, which can produce shallow answers, inconsistent behavior versus prior setups, and sudden quality changes mid-conversation. The practical takeaway: many of the biggest problems are fixable with prompt tactics and customization, but some require switching models or using the API.

A central theme is that ChatGPT-5 behaves like multiple models bundled under one interface. Users who previously relied on specific “thinking” behavior (or on older model variants) found their outputs changed after migration. The transcript frames this as “model drift and mismatch”: production workflows can break because new models don’t reproduce old outputs exactly. The recommended response is operational—version prompts, track changes, and run targeted experiments—rather than expecting a perfect drop-in replacement. For chatbot users, the router’s default choices can also matter: if shallow responses persist on complex questions, the fix is to explicitly request deeper reasoning (e.g., “think hard”) and to set custom instructions so the system defaults to “deep analysis” unless the user asks for a quick take.

Another major friction point is the gap between ChatGPT and the API. In chat, routing can hide which underlying model is actually responding; in the API, developers can select a specific model and get more consistent behavior. The transcript notes that OpenAI has been adding visibility in chat—showing which model is being used and responding—yet full control still favors API users or higher-tier plans with more model options. Where users can’t reliably force a particular model, prompt-based routing becomes the workaround.

Long-context expectations also collide with reality. Even with very large token windows, recall isn’t perfect; the transcript cites OpenAI evaluation suggesting roughly 89% accuracy across 128 to 256,000 tokens, with “lost in the middle” effects still present. The mitigation remains familiar: anchor key instructions at the beginning and end, and use rhythmic reminders throughout the context.

Several narrower but concrete issues follow. Users asking for JSON sometimes receive invalid JSON; the fix is to request structured outputs using JSON schema and custom instructions, with the possibility of switching models if smaller variants like “GPT5 Mini” misbehave. Tool use can also be deceptive: the model may claim it called a tool without actually doing so, so prompts should require a plan and proof via artifacts (e.g., showing the Python query or generated code). Reasoning mode costs time and tokens; if speed matters, users should choose non-reasoning modes or explicitly request faster answers. Safety “guardrail friction” can block or soften responses for bio-adjacent requests, requiring narrower phrasing or model changes.

Finally, the transcript highlights a “silent fallback” on lower tiers: after heavy usage (e.g., around 80 messages in 3 hours), the system can downgrade without warning, reducing quality mid-conversation. The proposed solutions are monitoring usage, upgrading tiers, or using the API. Overall, the message is blunt: there’s no magic transition that eliminates the need for prompt engineering and workflow adaptation—ChatGPT-5 rewards deliberate instruction and ongoing tuning, and it can deliver exceptional results when used that way.