Prompt tests reveal a punchy split: GPT-5 looks confident, Qwen3-Max underconfident without tools. The findings say GPT-5 is cosmetically tuned for confidence, while Qwen3-Max leans on tooling to avoid overclaiming. This tension is exactly what prompts and data handling decide in real use. [1]
What the tests show • GPT-5 — confident hallucinations; little meta awareness of data quality; risk prompts curb confidence but can trigger underconfidence. That tension hints at cosmetic tuning rather than true reliability, and it shows why OpenAI might fear unchecked confident errors more than occasional underconfidence. [1]
• Qwen3-Max — uncertainty triggers lookups; without lookup, underconfident; needs explicit confidence-boost prompts. Tooling reshapes its self-trust and willingness to assert hard facts. [1]
• OpenAI — "OpenAI is more afraid of the 'unimpressive' underconfidence than of the 'seemingly impressive' confident hallucinations." [1]
Real-world impact and risk • LLMs — are the ultimate demoware, generating dazzling demos that tempt broad ambitions even as real-world reliability varies. The piece warns against equating impressive output with dependable performance. [2]
• Charlie Meyer — frames LLMs as the ultimate demoware, a reminder that hype often outpaces steady, daily usefulness. [2]
• The discussion emphasizes that usefulness exists in certain tasks, but daily deployments require careful risk management and proper tooling, not flashy demos alone. [2]
Closing thought Prompts, tooling, and data trust shape perceived reliability more than raw model size. Calibrated confidence—backed by tools—will be the real test next. [1][2]
References
I spent a few hours prompting LLMs for a pilot study of the "Confidence profile" of GPT-5 vs Qwen3-Max. Findings: GPT-5 is "cosmetically tuned" for confidence. Qwen3, despite meta awareness of its own precision level, defaults towards underconfidence without access to tools.
Compares GPT-5 and Qwen3-Max, notes confidence tuning, hallucinations, prompt effects, and implications for using tools and data trust.
View sourceLLMs are the ultimate demoware
Long thread debating LLMs' value, demoware framing, tutoring usefulness, real-world impact, and open questions about AGI, productivity gains and risk.
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