AI & Machine Learning Calculators — United States
Model sizing, token economics, memory and throughput. The arithmetic is the same everywhere; you supply the prices, because those change monthly and ours would be wrong.
Cost & token economics
LLM Token Cost Calculator
What a model call costs — input and output tokens priced separately, per request and across a month of traffic.
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Token Count Estimator
How many tokens a piece of text is — from words, characters or pages — and whether it fits a context window.
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Model Training Cost Calculator
What a training run costs — from parameters and tokens to GPU hours, wall-clock time and money.
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Fine-Tuning Cost Calculator
What fine-tuning costs — the run itself plus serving — and the request volume at which it beats putting the same examples in every prompt.
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Prompt Caching Savings Calculator
How much prompt caching saves when many requests share a long prefix — system prompt, documents, few-shot examples — given the discount on cached reads, the premium on cache writes, and how often the cache is reused before it expires.
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Image Token Cost Calculator
How many tokens an image costs a vision-language model under the common tiling rule — scale to fit, cut into tiles, a fixed base plus a fixed charge per tile — and what that means for a monthly volume.
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AI Training Energy Calculator
The electricity a GPU fleet draws over a training run or a month of serving — with the data-center overhead, the power bill and the carbon it implies at your grid's intensity — and the household-years it equals.
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Cost Per Correct Answer Calculator
Compare two models by the cost of a correct result rather than the cost of a call — price per task, accuracy on your evaluation, and the cost of retries or human review for the ones they get wrong.
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AI Automation ROI Calculator
The return on automating a repetitive task with a language model — labour hours saved against API cost, review time and the build — with payback and the volume at which it starts to pay.
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Context Window Calculator
Whether a system prompt, a set of documents, the conversation history and room for the answer fit inside a model’s context window — how many documents fit, and the input cost of each call.
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Inference Energy Calculator
The electricity an AI service uses per query and per million tokens from the accelerator’s power draw and its throughput — with the cost at your tariff and the emissions on your grid.
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Model & memory sizing
GPU Memory Requirement Calculator
How much GPU memory a model needs — weights, KV cache and overhead — and whether it fits the card you have.
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Compute-Optimal Model Size Calculator
For a given training compute budget, the model size and dataset size that make best use of it under the Chinchilla scaling rule — about twenty tokens per parameter — and what a chosen model size would need instead.
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Convolution Output Size Calculator
The output size of a convolutional layer from its kernel, stride, padding and dilation — repeated through a stack — with the receptive field each layer sees and the parameters it holds.
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Quantized Model Size Calculator
How big a model is at each weight precision — 32, 16, 8 and 4 bits — whether it fits the memory of an edge device with room for activations, and the rough inference rate the device's compute allows.
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LLM Parameter Count Calculator
How many parameters a decoder-only transformer has from its depth, hidden size, feed-forward width and vocabulary — and the memory those weights take at 16-, 8- and 4-bit precision.
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Receptive Field Calculator
The receptive field of a convolutional network — how much of the input one output pixel sees — from the kernel sizes and strides of each layer, with the effective stride (jump) and the output size for a given input.
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Serving & throughput
Inference Throughput Calculator
How fast a model serves — tokens per second, time to first token, total latency, and what a served request costs on your own hardware.
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Canary Rollout Duration Calculator
How long a new model version must run on a slice of traffic before you have seen enough requests to judge it — for a target request count, or to detect a given rize in the error rate with confidence.
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LLM Response Time Calculator
How long a language-model response takes from the user's point of view — time to first token plus tokens at the generation rate — and how streaming, a shorter answer or a faster model change what the user waits for.
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KV Cache Memory Calculator
The memory a transformer’s key–value cache takes for a context length and batch — from layers, KV heads, head size and precision — and how many tokens fit in the memory left after the weights.
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Edge Inference Speed Calculator
How fast a model runs on an edge device — inferences per second and milliseconds each — from the model’s operations per inference, the device’s rated throughput and a realistic utilization, with the energy per inference.
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Batching Latency–Throughput Calculator
What happens to throughput and latency when a serving system batches requests — from the single-request time, how much each extra request adds, the arrival rate and the maximum wait to fill a batch.
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Data & storage
Embedding Storage Calculator
How much storage a vector index needs — from document count, chunk size and embedding dimensions — plus the cost of generating the embeddings.
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Confusion Matrix Metrics Calculator
Every standard classification metric from the four cells of a confusion matrix — accuracy, precision, recall, specificity, F1, balanced accuracy and Matthews correlation — with a reading of which one to trust for your class balance.
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Disparate Impact Calculator
Whether a model or process selects one group at a lower rate than another — the disparate impact ratio, the four-fifths rule, the rate difference, and how many more selections would bring the groups into line.
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Population Stability Index Calculator
The population stability index between a reference distribution and a current one — the standard drift score for model inputs and scores — with the per-bin contributions that show where the shift is.
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Preference Win Rate Calculator
From a set of pairwize preference judgements between two models or responses — wins, losses and ties — the win rate, its confidence interval, the Elo-style rating gap it implies, and how many more comparisons you need to tighten it.
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Word Error Rate Calculator
The word error rate of a speech recogniser or transcript from its substitutions, deletions and insertions against the reference — with word accuracy, the error mix, and what the rate means per hour of speech.
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Edit Distance Calculator
The Levenshtein edit distance between two strings — the fewest single-character insertions, deletions and substitutions to turn one into the other — with the normalized similarity, the operation counts and the alignment.
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Expected Calibration Error Calculator
Expected calibration error from binned confidence and accuracy — whether a model that says "80% sure" is right 80% of the time — with the maximum bin gap and which bins are over- or under-confident.
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Equal Opportunity Difference Calculator
Fairness metrics for a classifier across two groups: the difference in true-positive rates (equal opportunity) and in false-positive rates (equalized odds), from each group’s confusion counts.
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KL Divergence Calculator
The KL divergence of one discrete distribution from another — in nats and bits, both directions — and the symmetric Jensen–Shannon divergence, from two lists of counts or probabilities.
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Bradley–Terry Calculator
The Bradley–Terry strength ratio and win probability between two models or responses from how often each was preferred — with an Elo-style rating gap and a confidence interval on the win rate.
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Perplexity Calculator
Convert a language model’s cross-entropy loss to perplexity and back — in nats, bits per token or bits per byte — with what the number means about the model’s predictions.
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Accuracy Confidence Interval Calculator
A confidence interval around a measured accuracy from the number of test items — the Wilson score interval — and the test-set size needed to pin accuracy to a chosen margin.
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About these calculators
These compute from figures you enter. No model names, prices or benchmark numbers are baked into any page — per-token prices and hardware rates move constantly, so they are inputs rather than assumptions, and the formula is always shown so you can check the result against your own provider's billing.