OpenAI vs Anthropic API pricing (list rates, same table)

Teams narrowing vendors to OpenAI and Anthropic usually compare the same three things: input $/1M, output $/1M, and usable context window. Output is almost always the dominant cost for chat and coding agents, because replies—and tool round-trips—stack generation tokens faster than most users expect.

How the two catalogs typically differ

OpenAI’s public list pairs many GPT-4.x / GPT-4o SKUs with different throughput and context tradeoffs; Anthropic’s public list centers on Claude Opus / Sonnet / Haiku tiers with pronounced gaps between flagship and efficiency models. The right choice is rarely “who is cheaper in the abstract”—it is which SKU matches your latency, accuracy, and median output length after you measure a week of real traces.

Caching and “the first prompt of the day”

Both ecosystems advertise cached or discounted input for repeated system prompts and large stable prefixes. In real IDE and agent setups, cache behavior can swing effective cost more than moving between adjacent list prices—but cache is never a substitute for checking your vendor dashboard, because write/charge semantics differ by model and session.

Practical decision framework

1) Sort by output $/1M for assistant-style workloads. 2) If you run long documents or RAG, weigh input $/1M and context window together—a huge window only saves money if you are not forced into a flagship tier unnecessarily. 3) Pilot with production-like prompts, then translate tokens into dollars with the AI cost calculator. 4) Re-check list prices after major vendor announcements; CloudyBot publishes normalized snapshots on a cadence and validates them before swapping the live table.

Related comparisons

See also our GPT-4o vs Claude Sonnet guide and the cheapest LLM API at list prices explainer. All figures on CloudyBot remain directional list estimates from an aggregated public catalog—confirm on OpenAI and Anthropic billing consoles before you quote a budget.