Teams googling AI API pricing usually land on three vendor pages and a spreadsheet. This guide compares how OpenAI, Anthropic, and Google charge for model APIs against a flat-rate AI API (Daymora at $50/month), using production economics rather than homepage list rates.
Provider token prices change often. Treat the ranges below as order-of-magnitude 2026 list prices and confirm on each vendor's pricing page before you sign. The structure of the bill — tokens vs subscription — does not change as fast as the numbers.
What "AI API pricing" actually means
There are two dominant models:
1. Pay-per-token — you pay for input tokens and output tokens, sometimes with separate rates for cached input, batch, or tools.
2. Flat-rate — you pay a published monthly or annual fee. Usage is governed by fair use, not a per-token meter.
What is a flat-rate AI API? covers the definition. The rest of this article is the shopping comparison.
Headline list prices (indicative)
Premium chat models in 2026 typically sit in a wide band: on the order of $0.50–$30 per million tokens depending on model class and whether you are buying input or output. "Cheap" small models sit at the low end; frontier models sit at the high end, especially on output.
That range is why a demo that costs cents can become a four-figure invoice after you add:
- a 200–800 token system prompt on every call
- 4–20 turns of history
- retrieved chunks from your docs
- a 10–30% retry tax
The True Cost of AI APIs walks through the multipliers.
OpenAI API pricing (token)
OpenAI bills per model, per million input and output tokens. Newer flags (GPT-4o-class and GPT-5-class) are priced as premium. You also pay for:
- stored or cached input (if you use those products)
- tools / assistants usage where applicable
- extra keys and orgs if you split environments poorly
When it wins: maximum model choice, huge ecosystem, pass-through billing to your users.
When it hurts: any product where context windows should be large and traffic is correlated with your own success.
Anthropic API pricing (token)
Anthropic (Claude) also bills tokens, with separate input/output rates and optional prompt caching. Caching can cut repeated system-prompt cost; it does not remove the output-token line or the fact that long conversations still grow.
When it wins: teams standardized on Claude's behavior and willing to operate a second vendor account.
When it hurts: the same production overhead as any token API — history, retries, and spikes.
Google Gemini API pricing (token)
Google's Gemini API is typically cheaper per token at a given capability tier, with its own context-window and caching story. "Cheaper tokens" still scale with success. A 1M-token context window is a feature and a cost amplifier if you fill it.
When it wins: Google Cloud shops, long-context experiments, lower list rates on mid-tier models.
When it hurts: you still cannot quote a single monthly number without a usage forecast.
Flat-rate: Daymora
Daymora charges $50/month or $500/year. You get one API key and call POST https://daymora.com/api/v1/chat for GPT-5, Claude Sonnet 4, and Gemini 2.5 Pro. No per-token invoice. Commercial use included. Fair usage applies.
When it wins: production apps, internal tools, startups that need a line item for the board.
When it hurts: near-zero hobby usage, or a hard requirement for a model Daymora does not expose.
Full numbers: Daymora AI API pricing.
Comparison table
| Feature | OpenAI | Anthropic | Google Gemini | Daymora |
|---|---|---|---|---|
| Billing | Per token | Per token | Per token | Flat $50/mo |
| Invoice as traffic 10× | ~10× | ~10× | ~10× | Unchanged |
| Accounts to manage | OpenAI org | Anthropic org | Google Cloud | One Daymora key |
| Models | Broad catalog | Claude family | Gemini family | GPT-5, Claude Sonnet 4, and Gemini 2.5 Pro |
| Surprise usage bills | Yes | Yes | Yes | No |
| Best first question | "What's our token forecast?" | Same | Same | "Is this a real product feature?" |
A worked example (order of magnitude)
Assume a SaaS assistant: 1,000 weekly active users, ~8 turns, ~800 tokens of prompt overhead per turn, plus output.
Naive "user typed 50 tokens" math might predict tens of dollars. Fully loaded math (history + system + retries) often lands in the low hundreds per month on premium models — and keeps climbing if you do not starve the product of context.
At that point $50/month is not a premium; it is a hedge. Below ~$20 of true fully loaded token spend, tokens can still be cheaper. Measure in production, not in the playground.
How to choose in 15 minutes
1. Log fully loaded tokens per request (not just the user message) for a week.
2. Multiply by expected monthly requests. Add ~25% for retries.
3. Compare that number to $50.
4. If you are already over, or will be at next quarter's user count, prefer flat-rate.
5. If you must stay on a specific vendor SKU, stay on tokens and invest in caching.
Related guides
AI API pricing is not a single cell on a vendor page. It is a billing shape. Pick the shape that matches whether you are experimenting or shipping.