Token Estimator
Paste text → see what it costs across every model
Quick token count + dollar estimate before you send a prompt. Pick your content type for accuracy.
Pricing verified: 2026-07-28
92 models ranked
Client-side - your text never leaves your browser
What this calculator does
Paste any text and see exactly how many tokens it becomes across major tokenizers (GPT, Claude, Gemini). Cost projection at the current model catalog included.
Why use it
- Words ≠ tokens — same text tokenizes 10-20% differently across vendors, breaking naive cost estimates
- Avoid the classic budget-blowup: thinking 1 word = 1 token, then discovering 1.3× actual
- Pre-flight prompt size before you commit to a model with a tight context window
- Calibrate cost-calculator inputs with real numbers instead of guesses
Who uses this:
Vibe Coder
High
Pre-flight your prompt size before discovering "wait, that hit the context limit"
Small Business
High
Translate "I have these emails / docs / chats" into defensible token counts for the AI budget
Enterprise
High
RFP and procurement need real token shape numbers, not guesses
New to this calculator? Start with the ⚡ Playground — a few sliders, instant ballpark. Then switch to the 🧮 Calculator for your exact number.
Two ways to use this: visualize in the Playground, then get your number in the Calculator.
Estimate your monthly token bill
Paste a real prompt to gauge tokens. Balanced-tier model and ~30 working days assumed.
Estimated monthly cost
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Change the input sliders below to see new estimates.
How we got this estimate
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💡Cost = tokens × the model’s rate × requests × ~30 days. Tokens ≈ ¾ of a word each.
Paste your text below and we'll show the token count and what it costs across every model — using current published pricing.
📊 Not sure of a value? Fields with a ▾ Typical pill offer broad industry ballparks (sourced typical ranges) so you can move forward now — your result gets more accurate as you replace them with your own measured numbers. Values marked * are rough estimates.
📥 Inputs you provide
- Content typeTunes char-per-token expectation
- Sample textThe text to tokenize
- Expected output tokensResponse size estimate
📤 Outputs you get
- Input token countTokens for the pasted text
- Chars per token ratioDensity of your text
- Cost across modelsWhat this text costs per call
🎯 Use your results to
🔢
Convert text → tokens
Stop guessing — paste real samples, get exact counts
📋
Calibrate other calcs
Plug actual numbers into Cost Calculator instead of placeholder guesses
🔍
Catch bloated prompts
See if your prompt is bigger than expected before it hits production
🪜
Pre-flight context fit
Verify your prompt + retrieval + history fits in your chosen model's context window
1
Paste sample text
Drop a representative prompt, document chunk, or code snippet into the textarea. Use real production samples.
2
Pick content type
Toggle English / Code / JSON / Mixed — affects the chars-per-token ratio shown.
3
Read token count
Top number is total input tokens. Cost panel shows what this text costs across major models.
4
Use in other calcs
Plug the number into Cost Calculator as
input tokens. Now your monthly projection isn't a guess.1
Paste real samples
Use actual production text — system prompts, real user queries, real retrieval chunks. Synthetic test text often tokenizes differently because it lacks the irregular patterns (typos, mixed-case, emoji, code fragments) that real input has. If you have a prompt template + variable slots, paste a fully-rendered example, not the template.
2
Pick the right content type
English prose: ~4 chars/token. Code: ~3 chars/token (more punctuation, more whitespace tokens). JSON: ~3 chars/token (curly braces, quotes, colons each consume tokens). Mixed: ~3.5 chars/token. The display ratio updates as you toggle so you understand the cost density. Set this correctly — the tokenizer applies the same algorithm, but the displayed expectations differ.
3
Interpret the count
The big number is total tokens for the input you pasted. Cost projections show this text's cost across ~10 major models at current pricing. If your prompt has variable parts, multiply by your variable's typical size. The output token estimate uses your "expected output tokens" field — set this to a realistic value (typical chat reply: 150-400; code generation: 500-1500).
4
Plug into downstream calcs
Copy the token count into Cost Calculator as input tokens, set output tokens from your "what response am I expecting" estimate, and run the actual monthly cost. Pair with Token Reduction Analyzer if the number is bigger than expected — usually 20-40% of prompt tokens are reducible without quality loss.
👇 Now try the calculator below with your own AI workloads
🎛 CALCULATOR
📝 Paste your textThe actual content you want to count tokens for. Paste real samples — synthetic tests often diverge from production text.How to choose: For prompt templates, paste a fully-rendered example with realistic variable values. For repeating workloads, paste 2-3 different examples and average the counts.Read the full guide →
Hint: Paste a prompt, message, or document to count its length.
Prompt, document chunk, or any content you'd send to an AI API.
Hint: Pick what kind of text it is; code and JSON count differently from prose.
English prose: ~4 chars per token (GPT-5.x tokenizer).
Try:
Characters
0
Words
0
Est. tokens
0
Note: This is an estimate. Each provider uses a slightly different tokenizer.
Hint: Words you expect back. Output usually costs 3-5x more than input, so it matters.
📈 RESULTS
💵 Cost across all models
Enter text to see per-request cost ranked cheapest first.
| Model | Per request | Per 1K requests | Per 1M requests |
|---|---|---|---|
| Paste some text above to compare costs. | |||
📋 What now?
- Right-size your prompts — trim what's wasteful, keep what matters
- Pick the cheapest tokenizer — the per-vendor table is ranked cheapest-first for your exact text
- Project it to scale — per-request × your volume = the real monthly bill
Need help cutting your AI bill?
💼 Talk to a CloudIntelligence advisor →
Now that you have your token count…
What this means + what to do next
💡 What to consider beyond this token count for full TCO
- Per-call retry overhead (typically 3-15% on top of base token cost)
- Conversation history accumulation in multi-turn workloads
- System-prompt overhead repeated on every call (often 200-1000 tokens you're paying for forever)
- Output bloat — verbose models cost more than the input estimate suggests
Rule of thumb: Multiply by 1.15-1.3× to account for retries, system-prompt overhead, and output variance in real production traffic.
Quantify the hidden costs:
- Get the actual $/month at the token counts you measured here Cost Calculator
- For multi-turn workloads, this single-call count vastly underestimates total agent cost Agent Loop Cost
- If your token count is bigger than expected, often 20-40% is reducible without quality loss Token Reduction Analyzer
$ How this fits your overall ROI
This is a measurement tool — ROI conversations happen downstream:
- Is my prompt 30% bigger than it needs to be? (Run reduction analysis.)
- How much of my token budget is overhead vs unique input per call?
- Would a smaller model handle this prompt with acceptable quality?
Bridge to ROI:
- Quantify savings from compression, context-window-pruning, structured output Token Reduction Analyzer
- If your prompt has a stable prefix, caching can cut effective cost 50-80% Prompt Cache Roi
- If some queries are simpler than others, routing cuts cost without quality loss Multi Model Router
Doing something different?
If you don't have sample text yet, these calcs work from estimates:
- You can estimate token shape from memory (typical chat ~1500 in, ~500 out) Cost Calculator
- You're planning multiple apps without specific prompts yet Budget Planner
- You want to see how this cost grows at 10× and 100× volume Scale Projection