AI Token Cost Calculator
Estimate your AI API costs before you spend. Compare GPT, Claude, and Gemini pricing in seconds.
$2.5/1M input tokens · $10/1M output tokens · Context: 128,000 tokens
Used to project your monthly cost (assumes 30 days).
Input Cost
$0.0025
Output Cost
$0.0050
Total Cost / Request
$0.0075
Projected Monthly Cost
Based on your requests/day input
$22.50
Compare All Models
Cost for the same 1,000 input / 500 output tokens, cheapest first.
| Model | Provider | Input Cost | Output Cost | Total Cost | Context Window |
|---|---|---|---|---|---|
| Gemini 1.5 FlashCheapest | $0.0001 | $0.0002 | $0.0002 | 1,000,000 | |
| GPT-4o mini | OpenAI | $0.0002 | $0.0003 | $0.0005 | 128,000 |
| Claude Haiku | Anthropic | $0.0008 | $0.0020 | $0.0028 | 200,000 |
| Gemini 1.5 Pro | $0.0013 | $0.0025 | $0.0038 | 1,000,000 | |
| GPT-4o | OpenAI | $0.0025 | $0.0050 | $0.0075 | 128,000 |
| Claude Sonnet | Anthropic | $0.0030 | $0.0075 | $0.0105 | 200,000 |
| Claude Opus | Anthropic | $0.0150 | $0.0375 | $0.0525 | 200,000 |
GPT vs Claude vs Gemini Cost Calculator
Choosing between OpenAI's GPT, Anthropic's Claude, and Google's Gemini usually comes down to two questions: which one handles your task well, and which one costs less for your volume of requests. Growfiy's calculator lets you enter the same prompt once and see the estimated cost across all three, side by side, so the decision is based on real numbers instead of general reputation.
Built for developers picking a default model for a new project, founders comparing vendor costs before committing to one API, and teams deciding whether to route different tasks to different providers.
How to Decide Between Them
Sticker price alone rarely tells the full story. A fair comparison weighs cost against what each model actually costs you to accomplish a task — including how verbose its typical responses are, since output tokens are usually priced higher than input tokens.
- For high-volume, simple tasks (classification, short summarization, basic Q&A) — the cheapest tier from any provider is usually sufficient; compare their lightweight models against each other rather than their flagships.
- For complex reasoning or long-context tasks — flagship models justify their premium more often here; compare cost per successful outcome, not just cost per token.
- For latency-sensitive applications — smaller, cheaper models typically respond faster, which can matter as much as raw cost for real-time features.
- For mixed workloads — routing tasks to different models by complexity often beats picking one provider for everything.
What Changes If You Switch Providers Later
Moving between GPT, Claude, and Gemini at the API level is generally straightforward, since all three follow a similar request/response pattern. What usually needs rework is prompt tuning — instructions that produce reliable output on one model may need adjustment on another — along with any provider-specific features like function/tool-calling schemas or structured output formats. Planning for this upfront makes it easier to switch later if pricing or performance shifts.
Frequently Asked Questions
Which is cheaper — GPT, Claude, or Gemini?
It depends on the specific model tier within each family rather than the provider as a whole. Each provider offers a range from a low-cost, lightweight model to a premium flagship model, so the fair comparison is tier-to-tier — a budget Gemini model against a budget GPT model, for example — not flagship-to-flagship pricing alone.
Does a higher price always mean better output quality?
Not necessarily for every task. Flagship models tend to handle complex reasoning, long context, and nuanced instructions better, but for straightforward tasks like classification, extraction, or short-form summarization, a cheaper mid-tier model from any of the three providers often performs close enough to justify the lower cost.
How do I compare costs fairly across GPT, Claude, and Gemini?
Compare using the same prompt and expected output length across each model's calculator estimate, since input and output tokens are priced separately and at different rates on every platform. A model that looks cheaper per token can end up costing more if it tends to generate longer responses.
Is it hard to switch between GPT, Claude, and Gemini later?
Switching providers is usually manageable at the API level since most use a similar request/response structure, but prompts tuned for one model's behavior may need re-testing on another, and any provider-specific features (like function calling formats) will need adjustment during migration.
Should I use one model for everything or mix providers?
Many teams route by task — a cheaper model for high-volume, low-stakes requests (like tagging or summarizing) and a premium model for tasks that need stronger reasoning or accuracy — which often lowers total cost without sacrificing quality where it matters most.
Do GPT, Claude, and Gemini offer free usage tiers?
Each provider has offered free or trial credits for new API accounts at various points, but the amount and terms change frequently, so it's worth checking each provider's current pricing page directly rather than relying on older information.