# AI token calculator: GPT-4o, Claude and Gemini cost per 1M tokens in 2026

Canonical URL: https://usepayle.com/blog/ai-token-calculator-guide/
Published: 2026-09-03
Reading time: 9 minutes
Author: Mattia Vizzi
Author profile: https://usepayle.com/authors/mattia-vizzi/
Category: Guides

## Summary

Guide to pricing LLM token usage with the free AI token calculator. Explains what tokens are, why output costs 3-5x input, real 2026 prices per 1M tokens for GPT-4o, GPT-4.1, o3-mini, Claude Opus/Sonnet/Haiku 4, Gemini 2.5 Pro/Flash, DeepSeek V3, Mistral Large and Llama 4 Maverick, how to estimate a real workload from daily token volume, and when tokens belong in a plan budget. Internal links to the prompt cost calculator, pricing calculator, usage simulator and comparison engine.

## Article

The AI token calculator exists because nobody can price tokens by heart. Model pages list $2.50 per million input tokens, but your real question is what a feature costs when 400 users each send a 6,000-token prompt every day. This guide explains tokens, walks through the real per-1M prices of the models people actually use in 2026, and shows how to turn a workload into a dollar number in about a minute.
What is a token, and why do providers bill in tokens?
A token is the smallest unit of text a language model reads or writes: roughly three-quarters of an English word. One hundred thousand tokens is about 75,000 words, the length of a short book. Providers bill separately for the text you send (input tokens) and the text the model generates (output tokens), which is why every pricing table on this site and on vendor pages shows two columns.
Input and output are priced differently for a physical reason: generating each output token requires the model to run a full computation step, while input tokens are mostly processed in parallel. That is why output prices are typically three to five times input prices on the same model, and why cost forecasts that blend them 50/50 understate the bill. The token calculator assumes a realistic 70/30 input/output split for monthly projections, but you can adjust both numbers to your own pattern.
Real prices per 1M tokens in 2026
The calculator runs on a shared catalog of twelve models. These are the indicative list prices at the time of writing, in USD per 1 million tokens (input first, output second):
ModelProvider and roleInput / 1MOutput / 1MGPT-4oOpenAI flagship, strong multimodal and coding$2.50$10GPT-4.1OpenAI long-context production workhorse$2$8GPT-4o miniOpenAI cheap, fast, high-volume$0.40$1.60o3OpenAI reasoning for math and code$1.10$4.40Claude Opus 4Anthropic premium reasoning tier$15$75Claude Sonnet 4Anthropic balance for agents and coding$3$15Claude Haiku 4Anthropic fast everyday tasks$1$5Gemini 2.5 ProGoogle reasoning with huge context$1.25$10Gemini 2.5 FlashGoogle ultra-low-cost throughput$0.30$2.50DeepSeek V3Open-weight aggressive pricing$0.27$1.10Mistral LargeEuropean frontier with EU hosting$2$6Llama 4 MaverickMeta open model, cheapest end$0.20$0.40
GPT-4o (OpenAI): $2.50 / $10. The flagship default; strong multimodal and coding.
GPT-4.1: $2 / $8. The long-context production workhorse from OpenAI.
GPT-4.1 mini: $0.40 / $1.60. Cheap and fast for high-volume extraction and classification.
o3-mini: $1.10 / $4.40. Reasoning-focused model for math and code.
Claude Opus 4 (Anthropic): $15 / $75. The premium reasoning tier; pricey per token, cheapest per hard problem solved.
Claude Sonnet 4: $3 / $15. The balance pick for agents and serious coding.
Claude Haiku 4: $1 / $5. Fast and affordable for everyday assistant tasks.
Gemini 2.5 Pro (Google): $1.25 / $10. Reasoning-heavy with a huge context window.
Gemini 2.5 Flash: $0.30 / $2.50. Ultra-low-cost, high-throughput model.
DeepSeek V3: $0.27 / $1.10. Open-weight challenger with aggressive pricing.
Mistral Large: $2 / $6. European frontier option with EU hosting.
Llama 4 Maverick (Meta): $0.20 / $0.40. Open model at the cheapest end of the table.
Notice the spread: the same 1M tokens can cost $0.20 or $15 depending on the model. Picking a model by benchmark alone ignores a 75x price difference, which is exactly why a model benchmark comparator and a cost view belong in the same decision.
How to use the AI token calculator
Open the AI token cost calculator and enter three things:
Token volume: how many input and output tokens per request (or per day) you expect. Not sure? Paste a sample document into a counter first; most pricing pages underestimate their real prompts by half.
Models to compare: pick the two or three candidates you are actually choosing between, not all twelve.
Scale: the calculator shows per-request and per-100K cost instantly, then extrapolates to a daily and monthly number so the total is a budget line, not trivia.
Everything runs in your browser. Nothing is uploaded, and there is no sign-up: the same privacy promise as the AI pricing calculator.
Reading the comparison table
The result screen shows the same minimal table used across the Payle tools: one row per model, with the provider logo, the blended cost for your mix, and the input/output breakdown. Rows are sorted by total cost for your volume, so the cheapest model for your pattern sits at the top: not the cheapest model per token, which can be a trap.
Two rows can flip order depending on your mix. A model with cheap input but expensive output wins for long prompts with short answers and loses for short prompts with long generations. That is why the calculator asks for both numbers instead of applying a generic rule.
Estimating a real workload, not a fantasy volume
The most common mistake is modeling one request and multiplying. Real workloads look like this:
Support automation: 300 tickets a day, each 2,000 input tokens of conversation history plus a 300-token answer: roughly 690K tokens a day, about $2-4/day on a mid-tier model.
Coding agent: 40 agent turns a day, each 8,000 tokens of context with 1,500 tokens of output: roughly 380K tokens a day, but output-heavy, so the blended rate matters more than the headline.
Batch classification. 1M rows of short text: this is where Gemini 2.5 Flash or Llama class pricing makes the difference between $900 and $30 a month.
If your usage will grow with users, run the number at current scale and at 10x scale. The AI usage simulator automates exactly that kind of scenario planning with the same model catalog.
Tokens versus subscriptions: two different budgets
Plan subscriptions and token usage are different line items, even when the same provider sells both. A ChatGPT Plus or Claude Pro plan buys a capped experience inside the vendor app; hitting the API for your own product or automation is metered separately and usually lands in a different budget owner.
Teams routinely forget the API layer exists until the first surprise invoice. A simple rule: anything a customer or an automated workflow triggers is usage-based, and anything a human opens by hand is subscription-based. Price each layer with the right tool: the AI prompt cost calculator for single-prompt economics, the token calculator for volume, and the pricing calculator for the seat plans around them.
Five mistakes that double token bills
Paying reasoning prices for retrieval work. Use the cheap model for classification and embedding-style tasks, the expensive one only where answers must be deep.
Counting only the prompt. Output tokens cost more per token; long generations are where models like GPT-4o ($10/M output) really charge.
Ignoring context bloat. Every turn re-sends conversation history. A chat that grows to 20,000 tokens of context costs ten times a fresh 2,000-token prompt.
Forgetting caching and batching. Providers discount cached input and batch mode heavily. The listed prices assume uncached; a real pipeline should model the discounted tier.
Blending models by fashion. The newest model is not the cheapest. Re-run your volume through the calculator after each model release: prices move monthly.
What a monthly token budget should look like
Once you have a monthly token figure, treat it like any other software cost. A solo developer building on GPT-4o-class models typically lands between $30 and $150 a month in raw usage; a serious agentic workflow can pass $500. Compare that number against what the workflow saves: for most automation the payback is measured in days, not months.
If cash flow is the blocker rather than the economics, Payle spreads usage financing over three equal monthly installments at 0% APR, so a heavy token month does not become a capital decision. The AI cost tracker keeps the subscription and API budgets in one place so the total never surprises you again.
Start with your real volume
Skip the model-page arithmetic. Open the AI token calculator, type your actual input and output volumes, compare the two or three real candidates, and read the monthly total. The same catalog feeds the AI tool comparison engine and the model benchmark comparator, so whichever tool you start from, the numbers agree.Context length is a cost multiplier
Every model charges for every input token, including the context you are not thinking about. A support conversation that grows to 10,000 tokens of history re-sends all 10,000 tokens on every reply; an agent that re-reads a codebase summary every turn pays for that summary on every turn. Doubling the context of a workflow usually costs far more than doubling the request count, because context is paid repeatedly while the actual question is paid once.
Model choice interacts with this directly. Gemini 2.5 Pro prices a 1M-token window at $1.25 per million input tokens, so long context is genuinely cheap there. Claude Opus 4 at $15 per million makes a 100K-token context a luxury line item. The practical rule: measure the real context length of your workflow before picking the model, and re-measure after the workflow matures, because context bloat is the silent tax that grows with every feature you add.
When cheap models change the product decision
The price spread in the catalog is not trivia: it changes which products are viable. A feature that costs $0.02 per run on GPT-4o-class models can be almost free to serve at volume on Gemini 2.5 Flash or Llama-class pricing, and that difference decides whether a consumer product makes sense at all. Conversely, a $15-per-million model is the right answer when a wrong output costs real money, because rework is priced in outcomes, not tokens.
Run both ends of the decision before building: the quality profile from the model benchmark comparator and the volume cost from this calculator. Products fail when the cheap model is chosen for the wrong workload or the expensive one for the wrong reason; the two tools together make the choice explicit instead of accidental.

## Frequently asked questions

- Q: How much does 1 million tokens cost?
  A: One million input tokens costs between about $0.20 (Llama 4 Maverick) and $15 (Claude Opus 4). Output tokens cost more: GPT-4o is $10 per million output tokens, Claude Sonnet 4 is $15, and Gemini 2.5 Flash is $2.50. The token calculator prices your exact input/output mix.
- Q: How does the AI token calculator work?
  A: You enter the input and output token volumes you expect, select the models to compare, and it instantly shows per-request, per-100K and monthly cost in USD for every selected model, sorted by total cost for your mix. It is free, private and needs no sign-up.
- Q: How much does GPT-4o cost per token?
  A: GPT-4o costs $2.50 per 1M input tokens and $10 per 1M output tokens, so 100K input tokens is about $0.25. Pricing via the OpenAI API is usage-based; a subscription like ChatGPT Plus is a separate product.
- Q: Why are output tokens more expensive than input tokens?
  A: Generating each output token requires a full model computation step, while input is processed mostly in parallel. That is why output rates are typically 3-5x input rates on the same model.
- Q: What is a token in AI?
  A: A token is the smallest unit of text a model reads or writes, roughly 0.75 English words. 100K tokens is about 75,000 words. Providers bill both the prompt you send (input) and the text generated (output).
- Q: How do I estimate my monthly token usage?
  A: Estimate requests per day, average input and output tokens per request, then multiply by 30. For agentic or support workloads include conversation history in the input. The token calculator does the multiplication and the monthly projection for you.

## Related links

- https://usepayle.com/blog/
- https://usepayle.com/glossary/
- https://usepayle.com/products/
- https://usepayle.com/pricing/

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