Gemini Ironwood excels in complex reasoning and math tasks, achieving a score of 92 on the AIME math benchmark compared to GPT-4.5's score of 36.73. Ironwood's architecture is designed to support large-scale parallel processing, making it ideal for demanding workloads like LLM inference. While GPT-4.5 focuses less on structured reasoning, it lags behind specialized models with a score of 71.4 compared to Gemini's 75.8 on MMLUPro. Gemini also demonstrates superior performance in long-context tasks, achieving 83.1% accuracy versus GPT-4.5's 48.8%, and in multimodal understanding, with scores of 65.9 for Gemini compared to 74.4 for GPT-4.5. Ironwood's 192 GB of high-bandwidth memory (HBM) per chip enhances its capability for memory-intensive workloads. Although GPT-4.5 performs stronger in basic multimodal tasks with a score of 74.4 on MMMU, it struggles with extended contexts. In terms of factual accuracy, GPT-4.5 leads with a score of 62.5 on SimpleQA, while Gemini follows at 52.9, showing reduced hallucinations at 37.1 compared to older models.