LLMs
Token Estimator
Estimate token count from text length with an explicit heuristic and understand why tokenizers differ.
AI + ML developer mathCalculate first.
Calculate first.
Understand why.
Fast, browser-based calculators for the technical decisions that show up in AI development: token budgets, LLM costs, model memory, and classification metrics.
Built for learning
Useful output, visible assumptions.
Every result shows the formula, what is being estimated, and where the model breaks down.
- Transparent formulas you can check
- Practical examples for real decisions
- Guides that explain the reasoning
What this application is
A small toolkit for practical AI and ML decisions.
ModelMetric Lab is a browser-based collection of transparent calculators and learning guides. It helps developers estimate token usage, plan LLM API costs, evaluate classification models, and size GPU memory. The tools run without an account and show the assumptions behind every estimate, so you can use the result as a starting point rather than treating it as an unexplained answer.
Tools
Start with the calculations developers repeat
Do the math in your browser, then read the explanation below it. The goal is not just a number; it is a reusable mental model.
API economics
LLM Cost Calculator
Turn input/output token rates and request volume into per-request and monthly spend.
Evaluation
ML Metrics Calculator
Convert a confusion matrix into precision, recall, F1, and accuracy.
Hardware
GPU VRAM Estimator
Estimate weights memory from parameter count, precision, and an overhead factor.
Guides
Learn the idea behind the calculator
These pages are intentionally longer than the tool UI: they target informational queries and teach the reasoning that makes the tool useful.
LLM basics
How many tokens are in 1,000 words?
Why the answer is an estimate, how character count changes it, and how to budget prompts safely.
Cost planning
How to calculate LLM API cost
A transparent formula for input tokens, output tokens, request volume, and monthly spend.
Machine learning
Precision vs recall vs F1
A confusion-matrix-first explanation with examples and the trade-offs behind each metric.
AI hardware
How to estimate GPU VRAM
Understand weight memory, precision, overhead, and why training needs more than raw model weights.
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View all guidesEditorial principle
No black-box “AI facts” pages.
The site is designed around calculations that can be shown, checked, and explained. For changing facts such as provider pricing, publish dated source notes and update the page instead of pretending a static number is forever correct.