Gemini

• 1M-2M token context handles whole books, videos, and codebases at once; native multimodal (MMMU 82%) processes MP4, audio, charts, and UI screenshots directly. • Strong algorithmic and abstract reasoning; clean code, but weaker at multi-step debugging. • Deep Google integration (Gmail, Docs, Sheets, Android) and Google search; API up to 4x cheaper; free tier ~500 msgs/mo. • Weaker creative writing; limited plugin ecosystem; stricter content moderation. • 1M-2M token context handles whole books, videos, and codebases; native multimodal processes MP4, audio, and diagrams directly. • Strong algorithmic reasoning and clean code; good for codebase analysis. • Deep Google Workspace (Gmail, Docs, Sheets, Android) and Google search; cheaper API pricing. • Weaker creative writing; stricter content moderation; fewer third-party coding plugins. • MMLU 90%, but higher hallucination (some Gemini 3 Pro tests up to 88%); indirect, hard-to-verify citations. • 1M+ (up to 10M) token context; native video/audio/image; strong structured reasoning and code. • Deep Google Workspace integration (Gmail, Docs, Sheets); strong for daily productivity and automation. • Higher hallucination; citations hard to verify; real-time info may lag. • 2M-token context (≈3,000 pages) handles whole codebases at once; GPQA 91.9% vs Copilot's 88.1%. • Deep Google Workspace (Gmail, Docs, Sheets), NotebookLM, Google Search. • Lower enterprise total cost ($48-60/user/mo) since Workspace base is cheaper. • Weaker Microsoft integration; compliance less mature; no Purview/FedRAMP.