Press release
Omni Calculator Reveals Why AI Struggles With Precision and Trust in Calculations
New research from Omni Calculator explains why AI chatbots struggle with precise calculations, citing issues with numerical precision and user distrust. In response, the company will launch the "ORCA Benchmark" in November 2025 to measure the accuracy of top AI models on 500 real-world problems and highlight how structured tools can improve accuracy.AI chatbots can write essays, explain physics, and even simulate expert reasoning, but when it comes to precise, multi-step calculations, confidence does not always equal correctness.
Omni Calculator, creators of over 3,500 specialized calculators used by millions worldwide, has released two expert-informed studies examining why AI models often miscalculate and how user trust can be enhanced.
These studies set the stage for the ORCA Benchmark, which will launch in November 2025. This benchmark will measure how accurately AI models, such as ChatGPT 5, Gemini 2.5 Flash, Claude Sonnette 4.5, and DeepSeek V3.2, solve 500 real-world, everyday calculation prompts-the same verified problems Omni Calculator handles daily.
When AI Sounds Like an Expert, How to Make It Act Like One Too
https://www.omnicalculator.com/reports/why-ai-sounds-like-an-expert
Large language models (LLMs) are designed to predict text patterns, not to compute verified answers. As a result, they often answer with certainty, even when no reliable data exists.
It's important to note that chatbots are interfaces for LLMs, not the models themselves. Experts emphasize that combining LLMs with verified calculation tools or plugins can enhance AI's reliability, enabling chatbots to provide accurate, reproducible results.
Multi-step problems are particularly challenging. Mathematician Anna Szczepanek, PhD, explains that step-by-step calculations can overwhelm LLMs, leading to rounding errors or mistakes that compound across steps. Additionally, LLMs may include unnecessary or distracting information, further increasing the risk of incorrect outcomes.
"AI chatbots can talk math, they're great at explaining concepts, but they struggle when precision is needed, especially with very large or very small numbers. The root issue is how computers represent numbers: floating-point arithmetic is inherently approximate, and round-off errors propagate. Even well-engineered algorithms in numerical analysis must guard against instability and loss of significance. LLMs struggle with that a lot."
Only 59.2% of Users Trust AI with Calculations
https://www.omnicalculator.com/reports/ai-chatbot-interface
Omni Calculator's UX research and global surveys reveal that users judge reliability not by algorithms but by interface cues. Structure, feedback, and visible logic help users trust results. Even when AI is technically correct, chatbots' text-only interfaces can make answers feel unreliable.
The study also shows that the next UX frontier lies in adaptive transparency - showing just enough of the reasoning behind an answer to reinforce user confidence without overwhelming them.
The study also shows that the next UX frontier lies in adaptive transparency - showing just enough of the reasoning behind an answer to reinforce user confidence without overwhelming them.
Toward a Benchmark for AI Precision
The upcoming Omni Calculator benchmark will test top AI models, including ChatGPT-5, Gemini 2.5 Flash, Claude 4.5 Sonnet, Grok 4, and DeepSeek V3.2, against verified real-world problems. By quantifying the gap between AI confidence and actual accuracy, Omni Calculator aims to provide developers with a roadmap to more trustworthy and dependable AI, highlighting both the potential and the current limitations of today's LLMs.
Omni Calculator
Mikołajska 13/42, 31-027 Kraków, Poland
Samantha Balboa
marketing@omnicalculator.com
Omni Calculator transforms complex formulas into clear answers through 3,500+ online calculators covering science, finance, health, and everyday life. Its mission is to make knowledge accessible through user-friendly, math-powered tools.
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