Pricing in text generation is almost always per-token, split into a cheaper input rate and a more expensive output rate. A million input tokens is roughly 750,000 English words, so even verbose prompts rarely push the per-call price above a few cents. Output is where bills grow: agentic loops that re-prompt themselves, long reports, and uncached few-shot examples add up quickly. Cache the system prompt, trim history aggressively, and prefer JSON-mode for structured tasks to keep token counts down.
The trade-off triangle is quality, latency, and cost. Flagship models (GPT-5, Claude 4.6, Gemini 2.5) cost ten to fifty times more than budget tiers (Haiku, Flash, Mini) and respond two to four times slower, but they reason deeper, follow instructions more reliably, and hallucinate less. For high-volume classification or extraction, a budget model is almost always the right call. For long-form analysis, code review, or anything user-facing, the flagship usually pays for itself.
Watch out for context dilution: when you stuff 200K tokens into the window, the model's attention spreads thin and it starts to ignore the middle of the prompt — even on long-context flagships. Retrieve the relevant 8-16K tokens with embeddings instead of pasting the whole document.