LLM API pricing, in one place

Researching this page meant running the obvious searches — "Claude API pricing", "OpenAI API pricing per million tokens" — and the results were a mess: aggregator sites citing model names that don't exist, prices attributed to the wrong tier, and numbers contradicting each other across the first page of results. That's not a knock on any one site; it's what happens when pricing changes every few months and nobody re-verifies the scraped table.

Every number on this page was pulled directly from each provider's own current pricing documentation, not a third-party summary. Where a provider's docs give conflicting numbers for the same model (introductory vs. standard pricing, context-length tiers), both are shown with the condition that applies.

Cache read pricing is 10% of the base input rate — a cache hit costs a tenth of processing that same content fresh. Cache writes cost more than base input (1.25x for a 5-minute cache, 2x for a 1-hour cache), so caching only pays off once content is actually reused across calls, not on a single-shot prompt. Source: Anthropic's official pricing docs.

The Batch API cuts every model's pricing above by 50%, for requests that can tolerate asynchronous processing instead of a live response. The -pro tier (gpt-5.5-pro, and gpt-5.4-pro at the same rate) is a distinct, much more expensive reasoning tier — 6x the input cost and 6x the output cost of the standard flagship. Source: OpenAI's official pricing docs.

Gemini's Pro-tier models step to a higher rate once a request exceeds 200K input tokens — a context-length premium neither Anthropic nor OpenAI applies the same way on their current flagships. Batch processing is 50% off across all models. Gemini 2.5 Flash-Lite (the cheapest row above) is scheduled for retirement on October 16, 2026, after which Gemini 3.1 Flash-Lite becomes the budget floor at $0.25/$1.50.

Normalizing three different naming schemes into one table by capability tier:

Take a concrete workload: a RAG chatbot serving 10,000 queries/day, averaging 2,000 input tokens (retrieved context + question) and 400 output tokens per query. That's 20M input tokens and 4M output tokens per day. Using Anthropic's published rates (the same math applies to any provider's published rates):

It's tempting to say "token prices always fall" — the data doesn't cleanly support that as a straight line. In July 2024, OpenAI's GPT-4o mini launched at $0.15/$0.60 per million tokens, about 60% cheaper than GPT-3.5 Turbo's blended rate a year earlier (~$2/MTok blended, per contemporaneous reporting on the launch). Today's cheapest OpenAI tier, gpt-5.4-nano, is priced at $0.20/$1.25 — higher on input than 2024's GPT-4o mini was.

The more accurate claim is narrower and more useful: capability per dollar has risen sharply, even where the literal cheapest-tier list price hasn't kept falling in a straight line. gpt-5.4-nano in 2026 is a meaningfully more capable model than GPT-4o mini was in 2024, at a price in the same order of magnitude — the honest framing is "similar spend buys much more," not "spend keeps shrinking."