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Google's Gemini 3.1 Pro pushes context limits further with a 2 million token window - roughly 1.5 million words. This allows analysis of entire software repositories, legal document sets, or multi-book corpora in one call.

Gemini 3.1 Flash costs as little as $0.35/M input tokens - one of the lowest prices among frontier models. For high-volume pipelines processing millions of tokens daily, choosing Flash over Pro can reduce costs by 10-20x.

Anthropic and Google now support prompt caching, where large repeated system prompts or document contexts are cached server-side. Subsequent calls with the same cached prefix cost as little as 10% of normal input price. For RAG pipelines reusing the same document chunks, this is transformative.

Gemini 2.5 Flash achieves sub-second time-to-first-token for short prompts, making it excellent for interactive applications where responsiveness matters. Pair it with streaming responses for the best perceived latency in chat UIs.

The Transformer architecture, introduced in the 2017 paper 'Attention Is All You Need' by Vaswani et al. at Google, replaced recurrent neural networks and became the foundation for virtually every modern large language model including GPT, Claude, Gemini, and Llama.

GPUs (Graphics Processing Units) are excellent for parallel processing and are widely used for training AI models. TPUs (Tensor Processing Units), developed by Google, are custom-built accelerators specifically designed for neural network workloads, often providing better performance and efficiency for specific AI tasks.