Large Language Models (LLMs) have achieved human-level performance on a wide array of benchmarks, yet deploying them in critical applications requires that their internal confidence track actual competence. In this study, we evaluate the alignment between LLMs’ self-assessment and their actual competence in solving mathematical problems. We probe this gap on three mathematical datasets: MATH, Math500 and GSM8K, by eliciting LLM’s confidence and comparing it with its solution accuracy. Experiments on 8 open-source models from the Qwen and LLaMA families show three key findings: (1) Most of models display a certain degree of misalignment; (2) Scale and domain-specific fine-tuning matter: 7B-parameter and math-tuned Qwen variants narrow the confidence–performance gap, whereas similarly sized but untuned LLaMA models remain poorly consistent; (3) Misalignment is also sensitive to prompt design.