How Gemini was trained differently from GPT — a technical breakdown

Read through the latest technical report and the key architectural differences are fascinating. Gemini was trained natively multimodal from the start — not separate vision/language models stitched together. This explains why it handles interleaved image-text reasoning so naturally. The mixture-of-experts approach also means the 2.5 Pro model activates only ~30% of parameters per inference, which is how they keep costs low despite the massive total parameter count.