How accurate are these token counts?

They are heuristic estimates, not exact counts. Real models use byte-pair-encoding (BPE) or unigram tokenizers with vocabularies of 100,000+ entries that this tool cannot replicate without bundling each provider’s tokenizer. For ordinary English prose the estimate is usually within ±10–20%; expect more variance for source code, structured data, or non-English text. Use it to budget and compare, and confirm exact counts with the provider’s API or tokenizer when precision matters.

Why do different models show different token counts for the same text?

Each model family tokenizes text differently, so the same string maps to a different number of tokens. This tool reflects that by using a per-family characters-per-token ratio — roughly 3.8 for Claude and about 4.0 for GPT and Gemini — then refining it for whitespace, symbol density, and CJK characters. A model that packs fewer characters per token will report more tokens for identical input.

How is the cost calculated?

Cost is the estimated input tokens times the model’s published input price per million tokens, plus your "expected output tokens" times its output price per million. Output is almost always priced higher than input, which is why the per-model totals can diverge sharply once you raise the expected output. The "number of calls" stepper then multiplies the primary model’s total to project spend at scale.

Does my text get sent anywhere?

No. All counting and pricing happens locally in your browser using a self-contained estimator — there are no API calls and nothing is uploaded or logged. That makes it safe to paste proprietary prompts, internal documents, or sensitive data while you estimate.

What does the context-fit bar mean?

It shows what percentage of a model’s context window your prompt plus expected output would consume. Green means comfortable headroom, amber warns you are above 80% of the window, and red means the combined input and output exceed the window and the request would be rejected or truncated. It is a quick way to spot which models can actually handle a long document.

Are the prices and context windows current?

They reflect public 2026 list pricing and context-window sizes for each model at the time of writing. Providers change pricing, release new model versions, and adjust context limits over time, and negotiated or batch rates may differ from list prices. Treat the numbers as a well-informed baseline and verify against the provider’s current pricing page before committing a budget.

AI Token Counter & Cost Estimator

Estimate LLM token usage and API cost for any prompt across 20+ current models — GPT-5.5, o3/o4-mini, Claude Opus 4.8 / Sonnet / Haiku, Gemini 3, Grok 4, DeepSeek, and Llama — instantly and privately in your browser. Filter by provider, compare per-model token counts, input/output pricing, and context-window fit, then project cost across many calls.

About this ToolHow it works, benefits & use cases

Knowing how many tokens a prompt will cost is the difference between a predictable AI feature and a surprise invoice — and pasting text into a half-dozen provider playgrounds to find out is slow. This tool estimates token usage and dollar cost for any text across 20+ current models in one screen: GPT-5.5, GPT-5.4, o3, o4-mini and the GPT-4.1 family from OpenAI; Claude Opus 4.8, Sonnet 4.6, and Haiku 4.5 from Anthropic; Gemini 3.1 Pro and the Gemini 3 Flash line from Google; Grok 4.3 and 4.1 Fast from xAI; DeepSeek V3 and R1; and Llama 4 — with provider filters and a hide-legacy toggle so you can focus on the models you actually ship. As you type or paste, it shows estimated tokens for your chosen model alongside live character, word, line, and byte counts, then renders a side-by-side comparison table where each row gives that model’s token count, input cost, output cost, total cost, and a green/amber/red bar showing how much of its context window your prompt plus expected output consumes. An adjustable "expected output tokens" stepper drives the output-cost column, and a "number of calls" stepper projects total spend at scale so you can budget a batch job or a per-user feature. Everything runs locally with no network requests, so prompts never leave your browser. Counts are heuristic estimates that account for whitespace, punctuation and symbol density (code packs more tokens), and CJK characters (roughly one token each) — close enough to budget with, but not a substitute for a provider’s exact tokenizer.

How to Use

1Paste or type your prompt, document, or any text into the input box — the estimate updates live as you type.
2Pick a primary model from the dropdown; the large readout shows its estimated token count plus chars, words, lines, and chars-per-token.
3Set "Expected output tokens" to reflect a typical response length — this drives the output-cost column for every model.
4Read the comparison table to see estimated tokens, input cost, output cost, total cost, and context-window fit for all models at once.
5Adjust "Number of calls" to project total spend across a batch, a per-user feature, or a daily volume.
6Copy, download, share, or pipe the Markdown summary table into another tool to capture the full per-model breakdown.

Key Benefits

Compare token counts and cost across 20+ models from OpenAI, Anthropic, Google, xAI, DeepSeek, and Meta in one view
Live token, character, word, line, and byte counts update instantly as you edit
Realistic 2026 context windows and published input/output pricing per million tokens
Context-fit bars flag when a prompt plus its expected output overflows a model’s window
Cost-at-scale projection multiplies a single call by the number of calls you expect
Smarter heuristic that adjusts for code, punctuation density, whitespace, and CJK text
Fully private — runs in your browser with zero network calls, so prompts never leave the page

Common Use Cases

Budgeting an LLM feature before launch by comparing per-call cost across candidate models
Deciding whether a long document fits a model’s context window before sending it
Estimating the monthly bill for a batch or per-user workload at a known call volume
Choosing the cheapest model that still fits your prompt for a high-volume task
Sanity-checking a system prompt’s size while iterating, without burning real API credits

Estimated tokens

GPT-5.4

49tokens

194

Characters

Words

Lines

3.96

Chars / token

Primary model

Expected output tokens

Number of calls

GPT-5.4 × 1 call

$0.007623

Estimates only. Counts are computed instantly and privately in your browser with no network calls. Real tokenizers use BPE/unigram vocabularies, so actual usage typically differs by ±10–20% (more for code or non-English text).

Prompt / text

Paste a prompt, document, or any text to estimate tokens and cost.

Model comparison

23 of 23 models · 500 output tokens

Tokens	Input	Output	Total	Context fit
49	$0.000245	$0.015	$0.0152	0.1%1,000k
49	$0.000123	$0.0075	$0.007623	0.1%1,000k
49	$0.000098	$0.004	$0.004098	0.3%200k
49	$0.000027	$0.0011	$0.001127	0.3%200k
49	$0.000098	$0.004	$0.004098	0.1%1,047.576k
49	$0.00002	$0.0008	$0.00082	0.1%1,047.576k
49	$0.000005	$0.0002	$0.000205	0.1%1,047.576k
49	$0.000123	$0.005	$0.005123	0.4%128k
49	$0.000007	$0.0003	$0.000307	0.4%128k
52	$0.00026	$0.0125	$0.0128	0.1%1,000k
52	$0.000156	$0.0075	$0.007656	0.1%1,000k
52	$0.000052	$0.0025	$0.002552	0.3%200k
49	$0.000098	$0.006	$0.006098	0%2,000k
49	$0.000025	$0.0015	$0.001525	0.1%1,048.576k
49	$0.000012	$0.00075	$0.000762	0.1%1,048.576k
49	$0.000061	$0.005	$0.005061	0.1%1,048.576k
49	$0.000015	$0.00125	$0.001265	0.1%1,048.576k
49	$0.000061	$0.00125	$0.001311	0.2%256k
49	$0.00001	$0.00025	$0.00026	0%2,000k
52	$0.000007	$0.00014	$0.000147	0.4%128k
52	$0.000029	$0.001095	$0.001124	0.4%128k
52	$0.00001	$0.000425	$0.000435	0.1%1,000k
52	$0.00001	$0.0002	$0.00021	0.4%128k

49 tokin $0.000245out $0.015
0.1%1,000k
49 tokin $0.000123out $0.0075
0.1%1,000k
49 tokin $0.000098out $0.004
0.3%200k
49 tokin $0.000027out $0.0011
0.3%200k
49 tokin $0.000098out $0.004
0.1%1,047.576k
49 tokin $0.00002out $0.0008
0.1%1,047.576k
49 tokin $0.000005out $0.0002
0.1%1,047.576k
49 tokin $0.000123out $0.005
0.4%128k
49 tokin $0.000007out $0.0003
0.4%128k
52 tokin $0.00026out $0.0125
0.1%1,000k
52 tokin $0.000156out $0.0075
0.1%1,000k
52 tokin $0.000052out $0.0025
0.3%200k
49 tokin $0.000098out $0.006
0%2,000k
49 tokin $0.000025out $0.0015
0.1%1,048.576k
49 tokin $0.000012out $0.00075
0.1%1,048.576k
49 tokin $0.000061out $0.005
0.1%1,048.576k
49 tokin $0.000015out $0.00125
0.1%1,048.576k
49 tokin $0.000061out $0.00125
0.2%256k
49 tokin $0.00001out $0.00025
0%2,000k
52 tokin $0.000007out $0.00014
0.4%128k
52 tokin $0.000029out $0.001095
0.4%128k
52 tokin $0.00001out $0.000425
0.1%1,000k
52 tokin $0.00001out $0.0002
0.4%128k

Comparison summary (Markdown)

Copy, download, share, or pipe the full per-model breakdown.

# Token & Cost Estimate

Input: **194 chars**, 31 words, 1 lines, 194 bytes. Assumed output: **500 tokens**.

| Model | Provider | Est. input tokens | Input $ | Output $ | Total $ | Context fit |
| --- | --- | ---: | ---: | ---: | ---: | ---: |
| GPT-5.5 | OpenAI | 49 | $0.000245 | $0.015 | $0.0152 | 0% (fits) |
| ★ GPT-5.4 | OpenAI | 49 | $0.000123 | $0.0075 | $0.007623 | 0% (fits) |
| o3 | OpenAI | 49 | $0.000098 | $0.004 | $0.004098 | 0% (fits) |
| o4-mini | OpenAI | 49 | $0.000027 | $0.0011 | $0.001127 | 0% (fits) |
| GPT-4.1 | OpenAI | 49 | $0.000098 | $0.004 | $0.004098 | 0% (fits) |
| GPT-4.1 mini | OpenAI | 49 | $0.00002 | $0.0008 | $0.00082 | 0% (fits) |
| GPT-4.1 nano | OpenAI | 49 | $0.000005 | $0.0002 | $0.000205 | 0% (fits) |
| GPT-4o | OpenAI | 49 | $0.000123 | $0.005 | $0.005123 | 0% (fits) |
| GPT-4o mini | OpenAI | 49 | $0.000007 | $0.0003 | $0.000307 | 0% (fits) |
| Claude Opus 4.8 | Anthropic | 52 | $0.00026 | $0.0125 | $0.0128 | 0% (fits) |
| Claude Sonnet 4.6 | Anthropic | 52 | $0.000156 | $0.0075 | $0.007656 | 0% (fits) |
| Claude Haiku 4.5 | Anthropic | 52 | $0.000052 | $0.0025 | $0.002552 | 0% (fits) |
| Gemini 3.1 Pro | Google | 49 | $0.000098 | $0.006 | $0.006098 | 0% (fits) |
| Gemini 3 Flash | Google | 49 | $0.000025 | $0.0015 | $0.001525 | 0% (fits) |
| Gemini 3.1 Flash-Lite | Google | 49 | $0.000012 | $0.00075 | $0.000762 | 0% (fits) |
| Gemini 2.5 Pro | Google | 49 | $0.000061 | $0.005 | $0.005061 | 0% (fits) |
| Gemini 2.5 Flash | Google | 49 | $0.000015 | $0.00125 | $0.001265 | 0% (fits) |
| Grok 4.3 | xAI | 49 | $0.000061 | $0.00125 | $0.001311 | 0% (fits) |
| Grok 4.1 Fast | xAI | 49 | $0.00001 | $0.00025 | $0.00026 | 0% (fits) |
| DeepSeek V3 | DeepSeek | 52 | $0.000007 | $0.00014 | $0.000147 | 0% (fits) |
| DeepSeek R1 | DeepSeek | 52 | $0.000029 | $0.001095 | $0.001124 | 0% (fits) |
| Llama 4 Maverick | Meta | 52 | $0.00001 | $0.000425 | $0.000435 | 0% (fits) |
| Llama 3.3 70B | Meta | 52 | $0.00001 | $0.0002 | $0.00021 | 0% (fits) |

> Estimates only — generated locally with no network calls. Real tokenizers use BPE/unigram vocabularies, so actual counts typically differ by ±10–20% (more for code or non-English text).

How the estimate works

Each model family has a typical characters-per-token ratio (≈3.8 for Claude, ≈4.0 for GPT and Gemini). This tool starts from that ratio and refines it for whitespace runs, punctuation/symbol density (code packs more tokens), and CJK characters (counted ≈1 token each). Cost is then derived from each model’s published input/output price per million tokens, and the context-fit bar compares your prompt plus expected output against the model’s window. Nothing leaves your browser.

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AI Token Counter & Cost Estimator

How to Use

Key Benefits

Common Use Cases

Estimated tokens

Model comparison

How the estimate works

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