AI Model Cost Comparison: OpenAI vs Anthropic vs Gemini

Pricing at a glance

Prices are quoted per million tokens (1M = ~750k words). Input tokens are what you send to the model; output tokens are what it generates back. Output is always more expensive.

Pricing changes frequently — these are the publicly listed rates at the time of writing. Always verify on the provider's pricing page before locking in a budget.

The hidden lever: caching

Most production traffic is repetitive — same support questions, same RAG snippets, same system prompt prefixes. All three providers now offer some form of prompt caching:

• OpenAI automatically caches identical prefixes >1024 tokens and discounts cached input tokens up to 50%.
• Anthropic exposes explicit cache breakpoints; cached input reads cost 10% of base, with a 25% write surcharge on first miss.
• Google bills cached Gemini tokens at roughly 25% of the live rate, plus a small per-hour storage fee.

None of these schemes catch semantically identical prompts — "Refund my order #123" and "Please cancel and refund my order 123" hit the model twice at full price. A semantic cache embeds each prompt and serves a stored response when the cosine similarity crosses a threshold (we use 0.92 by default). That's where the cheapest tokens come from: tokens you never send.

Where an AI gateway fits

An AI gateway like ZeroCredit AI gives you one HTTP endpoint that:

1. Checks the semantic cache first — instant, zero-cost reply on a hit.
2. Routes the request to the cheapest model that can plausibly handle the prompt (or to the fastest, balanced, or premium tier you've configured).
3. Logs cost, latency, and a baseline-vs-actual savings number per request.
4. Enforces per-team daily / weekly / monthly budgets before the upstream call happens.

The same OpenAI-compatible JSON body works for every provider, so you don't rewrite application code when prices shift — the routing engine just picks a different upstream.

A simple cost model

Suppose your app sends 1M requests/month averaging 500 input + 300 output tokens. On GPT-4o that's roughly $2,750/month. Route the same traffic through Gemini 1.5 Flash and you're at $128/month — a 95% reduction before caching even kicks in. Add a 40% cache hit rate on top and the bill drops to ~$77/month.

Not every request can run on the cheapest model — that's why ZeroCredit AI ships with four routing modes and a per-request override. The point isn't to use one model for everything; it's to stop paying premium prices for prompts that don't need them.