Like many of you building with LLMs, I often found myself jumping between multiple documentation pages just to figure out how much a certain query would cost across different models.

And let’s be honest — no one has time to memorize OpenAI’s per-million token costs, compare them with Anthropic, DeepSeek, or Gemini, and then mentally compute costs based on input/output tokens and query types.

So… I built myself a simple cost calculator that does exactly what I need:
📍 Give me an approximate cost of running a specific type of query on a selected model.

💡 Why I Built It

I was repeatedly:

Searching for the latest OpenAI pricing
Comparing it with Claude or Gemini
Trying to remember if 75 words = 100 tokens or the other way around
Doing math in my head or a notepad every time I needed to estimate costs

It got old, fast.

So I made a small spreadsheet-based calculator that lets me:

✅ Pick a query type (normal, research, function calling, or custom)
✅ Choose a model from OpenAI, Anthropic, Google, or DeepSeek
✅ Instantly get a final estimated cost for a fixed number of queries (default: 10)

🧮 What It Does

Behind the scenes, the calculator:

Uses a standard 75 words = 100 tokens conversion
Maps query types to average input/output token counts
Pulls in model-specific cost per million tokens
Computes the total cost for the number of queries selected

And that’s it. Simple, fast, and surprisingly handy.

🔧 What's Inside?

Here’s a peek at what powers the tool:

Component	Purpose
Query Type	Sets average input/output tokens
Model Selection	Pulls cost per million tokens
Token Math	Computes cost per query type/model
Final Cost	Combines everything × number of queries

📌 Why Approximate?

This tool isn’t meant to be 100% accurate to the last decimal — it’s designed for:

Quick ballparks during architecture discussions
Budget estimates before production usage
Cost comparisons across providers/models

In real-world use, actual token counts vary due to system messages, model verbosity, and temperature settings. But this gives you a solid directional cost estimate.

🛠️ What's Next?

I'm thinking of extending it with:

Support for embedding models
Somehow integrate caching values
Batch API estimations
Adjustable verbosity (to estimate more or fewer output tokens)
Overhead token buffers for function calling

Let me know if that would be useful — or if you want a copy to use or contribute to!

🔗 Want to Try It?

https://docs.google.com/spreadsheets/d/18-TPKGPVEiYMH5I0dt9YMRQuL_--mqE-HbdxtW4vkzc/edit?gid=0#gid=0

Any feedback? Just drop a comment or DM me on Twitter/X.

💬 Ever built your own utility out of frustration? Share your tool or workflow below!

Quick AI Model Cost Estimator

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