OpenAI vs Anthropic cost comparison
Choosing between GPT and Claude often comes down to cost at your volume. This tool puts equivalent models side by side — GPT-4o against Claude Sonnet, GPT-4o mini against Claude Haiku — and prices your exact token profile so you can see which provider is cheaper and by how much.
How it works
You enter the prompt and completion tokens for a typical request and pick
a tier that maps an OpenAI model to its closest Anthropic counterpart. The
calculator prices each side as (prompt ÷ 1M × input price) + (completion ÷ 1M × output price), multiplies by your daily volume, and projects a monthly
figure for both.
The result shows per-request and monthly cost for each provider, the winner, and the percentage difference. Because the same token profile is applied to both, the comparison isolates pricing from usage assumptions.
What the tiers map to
| Tier | OpenAI model | Anthropic model | Notes |
|---|---|---|---|
| Premium | GPT-4o | Claude Sonnet | Flagship mid-tier; strong reasoning |
| Economy | GPT-4o mini | Claude Haiku | Fast, cheap, suited for classification or short replies |
These pairings are rough capability equivalents, not performance guarantees. Each model has its own strengths — GPT-4o tends to be strong on code and structured output while Claude Sonnet often excels on nuanced instruction-following — so cost is only one input to the decision.
Why output tokens dominate cost
Both providers charge several times more per output token than per input token. For example, a model might charge 5× more for output than input, meaning a request with 1,000 input tokens and 500 output tokens would still have its cost dominated by the shorter output side. This makes completion length the most important lever: cutting average output by 30% cuts total cost by more than 30% if the output rate is high. Try a realistic completion length before comparing — the winner can flip depending on whether your workload is read-heavy or write-heavy.
Worked example
Suppose your typical call sends 800 input tokens and receives 400 output tokens,
at 10,000 requests per day. For illustrative purposes only: if Provider A charges
$5/M input and $15/M output, your daily cost is (800/1M × $5 + 400/1M × $15) × 10,000 = $100. Change the output length to 800 tokens and the daily cost jumps to
$160 — showing that doubling output length costs more than doubling input length
because output is the expensive side. Enter your real token counts to see
the actual split for your workload.
Tips and notes
- Equivalence is approximate. Tier pairings reflect rough capability, not identical behaviour — always test quality on your real task before switching.
- Output-heavy workloads shift the winner. A model that is cheaper on input can still cost more if you generate long responses. Use realistic completion lengths.
- Prices are editable estimates. Both providers change rates; confirm current pricing in each dashboard before committing budget.
- Prompt caching changes the math. Both providers offer caching for repeated large prompts, which dramatically reduces effective input cost for system-prompt-heavy architectures. Factor that in if you reuse a large context block across requests.