Anchoring bias causes large language models (LLMs) to shift quantitative judgments in response to irrelevant numerical primes. We analyze this bias as a function of model confidence and accuracy in base, instruction-tuned, and distilled variants of Llama and Qwen models. We find that anchoring susceptibility is negatively correlated with model confidence without regard to accuracy: confidently incorrect models resist anchoring as effectively as accurate ones, provided their internal priors are sufficiently strong. We further show that post-training impacts the strength of this relationship, and that models are more susceptible to high anchors than to low anchors. Our findings suggest anchoring resistance is a structural property of distributional concentration (certainty) rather than knowledge correctness (factual accuracy), with implications for deploying LLMs in numerical reasoning tasks.