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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.

Route simple queries to cheap models (GPT-5.4 Mini, Gemini Flash, Haiku) and complex ones to premium models (GPT-5.4, Claude Opus). Using a lightweight classifier or a rule-based router, you can serve 80%+ of queries with cheap models and reserve expensive ones for the hard 20%.

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.

Never rely on a single LLM provider in production. Implement a fallback chain (e.g., Claude -> GPT -> Gemini) so that if one provider has an outage or rate limits you, your application gracefully degrades to the next available model.

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.

The cost of LLM API calls has fallen dramatically since 2023. GPT-3.5-Turbo launched at $2/M input tokens; by 2026, comparable-quality models like Gemini Flash and GPT-4o-mini cost under $0.15/M input tokens — a reduction of over 90% in under three years.

Modern frontier models like GPT-4o, Claude Opus 4, and Gemini 2.5 Pro natively process text, images, audio, and video in a single model. This multimodal capability enables applications like visual question answering, document understanding, and real-time audio conversation that were impossible just two years ago.

Gemini 2.5 Flash offers near-Pro quality at a fraction of the price — ideal for high-volume use cases.

Use Gemini's 1M+ token context window for processing entire codebases or long documents in one shot.

Use model-specific prompting techniques: Claude likes XML, GPT likes markdown, Gemini handles multimodal well.

Most modern LLMs, including GPT and Gemini, are based on the Transformer architecture, introduced in the 2017 paper 'Attention Is All You Need.' Its key innovation is the attention mechanism.

Challenge: Design a prompt that produces consistent, high-quality results across different LLM models (GPT, Claude, Gemini). Test and refine until you achieve similar outputs.