Are these prices official?

They are editable estimates based on published list prices at the dates shown and are clearly labelled. Providers change pricing frequently, so confirm the current rate in each provider's dashboard before budgeting.

How do you decide which models are equivalent?

We group by capability tier — premium (flagship reasoning), balanced (mid-tier workhorse), and economy (small fast models) — so you compare like for like rather than by marketing name.

Why does the workload ratio change the delta?

Output tokens cost several times more than input tokens, and the two providers price that gap differently. A reasoning-heavy workload with lots of output can flip which provider is cheaper.

Is my data sent anywhere?

No. Everything runs in your browser. Nothing you enter is uploaded, stored, or logged.

Has LLM pricing gone up or down over time?

Broadly down. Intense competition between providers and efficiency gains have driven repeated price cuts at equivalent tiers, even as new flagship models launch at a premium.

What is the Anthropic vs OpenAI Pricing Timeline?

Compare how Anthropic and OpenAI list prices have evolved for equivalent premium, balanced, and economy model tiers — see the per-million-token history and the current cost delta for your own input/output workload. It runs free in your browser on Gera Tools, with nothing uploaded.

Anthropic vs OpenAI Pricing Timeline

Name: Anthropic vs OpenAI Pricing Timeline
Creator: Gera Tools
License: https://creativecommons.org/licenses/by/4.0/

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Anthropic vs OpenAI: how prices have moved

The two leading API providers price by capability tier, and those prices have shifted repeatedly as each releases new models and responds to the other. This tool lines up equivalent tiers — premium, balanced, and economy — and shows both the per-million-token history and the current delta for your workload.

How the comparison works

Each tier maps to comparable models from both providers. Costs are quoted in USD per million tokens, with input and output priced separately. The current delta blends those two rates using your workload ratio:

blended = (input_tokens / total) × input_price
        + (output_tokens / total) × output_price

Because output tokens typically cost several times more than input tokens, your input/output mix can flip which provider is cheaper — so the default 1:1 ratio is only a starting point.

Why the output premium matters so much

Both Anthropic and OpenAI charge more for output tokens than input tokens — often three to five times more at the premium tier. This means the same nominal “price” looks very different depending on your workload:

A retrieval-augmented generation (RAG) pipeline that sends large context windows but generates short answers is input-heavy. The raw input price dominates total cost.
A long-form generation task — drafts, reports, code completions — spends most of its budget on output tokens. Even a modest difference in output price compounds quickly at scale.
A chat interface with roughly equal input and output is somewhere in between; the blended rate is the right figure to compare.

Entering your real ratio shifts the blended calculation and can reveal a different winner than the list-price headlines suggest.

Tips for reading the timeline

Compare tiers, not names. A flagship from one provider should be compared with the other’s flagship, not its mid-tier model. Marketing names change; capability tiers are the stable comparison point.
Mind the trend. Prices at a given tier have generally fallen over time as providers compete and efficiency improves. A model that looked expensive last year may have since been cut or replaced by a cheaper equivalent.
Use your real ratio. Measure actual input and output tokens from your production logs — even a rough split (60:40, 20:80) produces a more useful delta than the default.
Prices are list prices. Volume discounts, committed-use agreements, and cached-input pricing (where available) can change the real cost significantly. Confirm current rates in each provider’s dashboard before budgeting.