Guides → Playground & Guide → Token Estimator - From Pasted Prompt to Real Monthly Cost

Token Estimator - From Pasted Prompt to Real Monthly Cost

🚀 Open the calculator

Meet James Wong. Senior Engineer building a customer support assistant. "We've estimated 1,500 input tokens per request. Is that right? My monthly bill says we're using 4,800."

🔥 Real bill is 3.2× higher than the spreadsheet projected.

The story

Most teams underestimate token counts by 2-5×. They count the user message and forget the system prompt. They forget tool/function definitions. They forget RAG retrievals. They forget the conversation history that grows with every turn.

James's team estimated 1,500 input tokens per request. The real number was 4,800 - system prompt (1,200) + 5 tool definitions (1,800) + retrieved context (1,500) + user message (300). The cost projection was off by 3.2×, which is exactly the surprise on month-1 bill.

This calculator solves the underestimate problem by giving you ONE input - your actual prompt - and showing the real token count + cost across every major vendor. Paste once, see truth.

🎮 Playground

Token Estimator Playground

Here are the inputs that move the result the most. Play with the sliders and check it out. The number updates live.

Estimate your monthly token bill

Paste a real prompt to gauge tokens. Balanced-tier model and ~30 working days assumed.

Estimated monthly cost
—

💡Cost = tokens × the model’s rate × requests × ~30 days. Tokens ≈ ¾ of a word each.

Three real scenarios

Same calculator, three team sizes. Click a tab to see how the numbers shift.

$339.92 / month ≈ $4,079 / year

System prompt + user message + short response. 2K daily messages. Lands ~$120/mo on Sonnet 4.6.

Healthy range: $60-200/mo

See inputs used
inputTokens
1,500
outputTokens
400
requestsPerDay
2,000
modelTier
balanced
workingDaysPerMonth
30

Use cases

Same calculator, different applications. Sizes above, workloads here. Pick the one that looks like yours.

Pre-loaded scenarios for the most common applications. Click a tab to see realistic numbers, then hit "Try this scenario" to load it into the calculator above.

$2,357 / month ≈ $28,289 / year

Mid-scale SaaS, 8K tickets/day handled by AI first. Per-ticket cost should be $0.003-$0.008. Above $0.02/ticket: model too premium for the use case.

Healthy range: $500-1,200/mo (~$0.005/ticket)

See inputs used
inputTokens
3,500
outputTokens
500
requestsPerDay
8,000
modelTier
balanced
workingDaysPerMonth
30

Ready to run the numbers?

Open the full calculator. Pick a model, enter your tokens, see per-call, daily, monthly, and annual cost.

🚀 Open the full calculator →

Cheapest 3 vendors for your tokens right now

Verified 11 hours ago
  1. 1
    GPT-5 Mini
    $0.250 in · $2.00 out ·
  2. 2
    gpt-5.1-codex-mini
    $0.250 in · $2.00 out ·
  3. 3
    Command
    $1.00 in · $2.00 out ·

About this calculator: Token Estimator - From Pasted Prompt to Real Monthly Cost

Paste your real prompt, get accurate token count + monthly cost projection across 17 vendors. Stop guessing at token counts that swing your bill 3-5×.

🎛 Inputs you control

Each input shapes the cost. Click an input on the calculator to set it. The explanations below match the live calculator field by field.

▸ Content type: Tells the calc what kind of text you're estimating. The tokenizer is the same, but expected density differs (English is dense; code is sparse with punctuation tokens).
How to choose: Pick the closest match. If your input is mostly English with occasional JSON, choose Mixed.
▸ Sample text: The 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.
▸ Expected output tokens: How many tokens you expect the model to generate in response. Used to project total cost (input + output).
How to choose: Constrain in your prompt ("respond in ≤ 200 tokens") for predictability. Typical: classification 10-50, chat reply 150-600, code generation 500-2000.

📊 Outputs computed for you

What you'll see after the calculator runs. Each card explains how to read the number.

▸ Input token count: Exact token count for the text you pasted, using the most common modern tokenizer (cl100k / o200k for OpenAI; comparable Anthropic / Google encoders).
How to read: This is your input-tokens-per-request number. Multiply by daily requests for daily total. Most vendors charge by the 1M, so divide by 1,000,000 then multiply by the model's input price.
▸ Chars per token ratio: How many characters of input became 1 token on average. English prose tends to ~4, code/JSON tends to ~3.
How to read: A ratio significantly different from your content type's typical range (e.g., English text at 2.5 chars/token) suggests unusual content — many proper nouns, foreign language, or heavy formatting. The cost projection is still correct, but the density is worth noting.
▸ Cost across models: Per-request cost for the input you pasted (plus your expected output tokens) at each model's current pricing.
How to read: Use the cheapest acceptable model. Mid-tier (Sonnet 4.6, Gemini 3 Flash, GPT-5-mini) is usually the sweet spot for production traffic. Frontier (Opus 4.7/4.8, GPT-5.5, Gemini 3 Pro) only if quality matters more than 3-5× the cost.
📊 CALCULATOR AT A GLANCE
🚀 Open the full calculator ✉️ Email [email protected]
Token Estimator - From Pasted Prompt to Real Monthly Cost full size

Reading your result

Per-vendor breakdown is the headline. Identical token count, dramatically different bills. Sonnet 4.6 vs DeepSeek V3 at the same volume can be 8-10× different. The question is whether DeepSeek's lower factual accuracy matters for YOUR use case.

Watch the input/output split. If output is 80%+ of cost, you should look at output token reduction (shorter prompts, structured outputs, smaller max_tokens). If input is 70%+, prompt caching and RAG optimization win.

Validate against billing. Take the per-vendor monthly number and compare to your actual bill. Within 20%? Your token estimate is solid. Off by 2×+? Something is unaccounted for - usually streaming retries, function-calling overhead, or system prompts in nested calls.

What "good" looks like:
  • Bare chatbot: ~500 input + ~300 output. If higher, you have hidden overhead.
  • RAG with 4 docs: ~6-10K input + ~600 output. Lots of cache opportunity.
  • Tool-using agent: ~5-15K input (function defs!) + ~400 output per call, 3-7 calls per task.
  • Long-context agent: ~30K-100K input. Input dominates 90%+ of bill - caching is essential.

What this calculator can't tell you

Honest limitations. Every model is wrong; some are useful. Where this one falls short:

For these, use: Prompt Cache ROI for caching. Batch vs Realtime for batch. Agent Loop Cost for tool agents.

Trade-offs

Cost isn't the only dimension. Click any constraint to see how recommendations change.

What matters most to you? Click any dimension — recommendations update.

Best fit for "cost":

  1. DeepSeek V3 $0.27/$1.10 per 1M tokens
  2. Gemini 3 Flash $0.30/$2.50 per 1M tokens
  3. Anthropic Haiku 4.5 $1.00/$5.00 per 1M tokens

At James's scale (5K req/day × 4.8K input), switching from Sonnet to DeepSeek saves ~$700/mo. Worth it ONLY if accuracy holds for your specific domain. Run a 100-prompt blind eval before switching.

Most popular

Everything above is the 80% case. The last 20% is where the money is.

The gaps we just listed are real, and they are the expensive ones: your actual prompts, your switching cost, your MLOps overhead. An AICost expert spends the hour on your AI and cloud costs, not a generic playbook. You leave with a written report: the way forward, in 30 days of concrete steps.

  • An hour with the people who built the engines
  • Report the same day
  • The fee credits toward any AICost plan
  • Two slots a week
Book a Solution Session: $299 → or $99 for small business →

Not sure yet? The $39 AICost Blueprint credits toward a Session, and the Session fee credits toward any plan. You never pay twice for the same ground. See all pricing →

Ready to run your own numbers?

You have seen the shape of it. Open the calculator with your model, your tokens, your volume.

🚀 Open the full calculator →

Where to go next

Project monthly cost across all vendors →

Once your token count is solid, get the full monthly bill projection.

 Cut input cost 30-50% with caching →

If 70%+ of your bill is input tokens, prompt caching usually pays back in days.

 What happens at 10× usage? →

Project your bill at 10×, 100× current scale.

Methodology

Source
https://github.com/openai/tiktoken
Extraction
Tokenization via cl100k_base (OpenAI tiktoken). Anthropic counts approximated within ±5%.
Editorial gate
8-layer defense, see aicost.ai/ai-cost-economics
Last verified
7/27/2026, 8:00:00 PM

Author: Subu Vdaygiri, Founder & CEO of CloudIntelligence.ai. 17 years Fortune 100 (Ingram Micro, Siemens). Wharton CTO program · Kellogg CPO program · 10× AWS+Azure certified.

3 years of pricing history

Why this matters: pricing for major vendors has dropped 40-90% in the last 24 months. A budget set 12 months ago is probably wrong by 30%+.

View 3-year history for →
📖 Data sources & methodology 163 text models · 9 embeddings · 37 vision · 55 audio · 8 vector DBs across 10 vendor pages · last verified 2026-07-28

Methodology

  • All prices are USD per 1 million tokens, current as of 2026-07-28.
  • Vendor-published values have no mark. Inferred/extrapolated values are marked with * and listed below.
  • Batch API discounts are 50% off standard rates across providers that offer Batch mode.
  • Prompt caching discounts vary by provider (typically 80-90% off cached input tokens).
  • Regional data-residency surcharges (Anthropic 1.1x, OpenAI 1.1x, Google regional tiers) are NOT included in base rates.
  • Long-context pricing tiers apply when input exceeds model threshold.
  • Embedding prices are input-only (no output tokens generated).

Primary sources

Last-verified date is the most recent successful daily snapshot (aicost_pricing_snapshots) or, when no snapshot exists yet, the latest successful crawler run (aicost_crawler_runs). 10 of 10 vendors are currently verified. Aggregator services (TokenCost, AI Pricing Guru, etc.) are not listed.

Anthropic
2026-07-28
https://www.anthropic.com/pricing
Daily snapshot since Sep 2023 · 631 days captured
Anthropic Docs
2026-07-28
https://platform.claude.com/docs/en/about-claude/pricing
Daily snapshot since Sep 2023 · 631 days captured
OpenAI
2026-07-28
https://openai.com/api/pricing/
Daily snapshot since Sep 2023 · 632 days captured
Google AI
2026-07-28
https://ai.google.dev/gemini-api/docs/pricing
Daily snapshot since Dec 2023 · 607 days captured
Google Vertex
2026-07-28
https://cloud.google.com/vertex-ai/generative-ai/pricing
Daily snapshot since Dec 2023 · 607 days captured
DeepSeek
2026-07-28
https://api-docs.deepseek.com/quick_start/pricing
Daily snapshot since May 2024 · 546 days captured
xAI
2026-07-28
https://x.ai/api
Daily snapshot since Nov 2024 · 464 days captured
Mistral
2026-07-28
https://mistral.ai/pricing
Daily snapshot since Dec 2023 · 605 days captured
Cohere
2026-07-28
https://cohere.com/pricing
Daily snapshot since Sep 2023 · 631 days captured
Voyage AI
2026-07-28
https://docs.voyageai.com/docs/pricing

Inferred values (marked with * in calculator tables)

Derived from industry conventions, not directly published by the vendor. Typical conventions: cached input = 10% of base (90% off), Batch API = 50% of base (50% off).

Vendor / Model Field Why it’s inferred
Anthropic — Claude Sonnet 4.6 cachedInput Derived at 10% of input rate — Anthropic publishes 90% cache-hit discount on this tier.
Anthropic — Claude Sonnet 4.5 cachedInput Derived at 10% of input rate; same 90% cache-hit convention as Sonnet 4.6.
Anthropic — Claude Sonnet 4.5 batchInput Derived at 50% of standard input — Anthropic documents uniform 50% Batch discount.
Anthropic — Claude Sonnet 4.5 batchOutput Derived at 50% of standard output — Anthropic documents uniform 50% Batch discount.
Anthropic — Claude Haiku 4.5 cachedInput Derived at 10% of input rate — Anthropic 90% cache-hit discount convention.
OpenAI — GPT-5.4 Mini cachedInput Derived at 10% of input — OpenAI documents automatic 90% discount on cache hits across GPT-5.x tier.
OpenAI — GPT-5.4 Nano cachedInput Derived at 10% of input — OpenAI 90% cache-hit convention.
OpenAI — GPT-5.4 Nano batchInput Derived at 50% of input — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.4 Nano batchOutput Derived at 50% of output — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.4 Pro cachedInput Derived at 10% of input — OpenAI 90% cache-hit convention.
OpenAI — GPT-5.4 Pro batchInput Derived at 50% of input — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.4 Pro batchOutput Derived at 50% of output — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.2 cachedInput Derived at 10% of input; no residency uplift.
OpenAI — GPT-5.2 batchInput Derived at 50% of input.
OpenAI — GPT-5.2 batchOutput Derived at 50% of output.
OpenAI — GPT-5 cachedInput Derived at 10% of input.
OpenAI — GPT-5 batchInput Derived at 50% of input.
OpenAI — GPT-5 batchOutput Derived at 50% of output.
OpenAI — GPT-5.5 Pro cachedInput Derived at 10% of input — OpenAI does not publish a cached rate for *-pro models; using the family convention.
OpenAI — GPT-5.5 Pro batchInput Derived at 50% of input.
OpenAI — GPT-5.5 Pro batchOutput Derived at 50% of output.
OpenAI — GPT-5.2 Pro cachedInput Derived at 10% of input — pro-tier convention.
OpenAI — GPT-5.2 Pro batchInput Derived at 50% of input.
OpenAI — GPT-5.2 Pro batchOutput Derived at 50% of output.
OpenAI — GPT-5.1 batchInput Derived at 50% of input.
OpenAI — GPT-5.1 batchOutput Derived at 50% of output.
OpenAI — GPT-5 Pro batchInput Derived at 50% of input.
OpenAI — GPT-5 Pro batchOutput Derived at 50% of output.
OpenAI — GPT-5 Nano cachedInput Derived at 10% of input.
OpenAI — GPT-5 Nano batchInput Derived at 50% of input.
OpenAI — GPT-5 Nano batchOutput Derived at 50% of output.
Google — Gemini 3 Flash cachedInput Derived at 10% of input — Google caching discount convention ~90%.
Google — Gemini 3.1 Flash-Lite cachedInput Derived at 10% of input — Google caching convention.
Google — Gemini 3.1 Flash-Lite batchInput Derived at 50% of input — Google Batch API uniform 50% discount.
Google — Gemini 3.1 Flash-Lite batchOutput Derived at 50% of output — Google Batch API uniform 50% discount.
Google — Gemini 2.5 Pro cachedInput Derived at 10% of input.
Google — Gemini 2.5 Flash cachedInput Derived at 10% of input.
Google — Gemini 2.5 Flash-Lite cachedInput Derived at 10% of input — Google caching convention.
Google — Gemini 2.5 Flash-Lite batchInput Derived at 50% of input — Google Batch API uniform 50% discount.
Google — Gemini 2.5 Flash-Lite batchOutput Derived at 50% of output — Google Batch API uniform 50% discount.
Google — Gemini 2.0 Flash cachedInput Derived at 25% of input per Google 2.0 family caching rates.
Google — Gemini 2.0 Flash batchInput Derived at 50% of input — Google Batch API uniform 50% discount.
Google — Gemini 2.0 Flash batchOutput Derived at 50% of output — Google Batch API uniform 50% discount.
Google — Gemini 2.0 Flash-Lite cachedInput Derived at 10% of input — Google caching convention.
Google — Gemini 2.0 Flash-Lite batchInput Derived at 50% of input — Google Batch API uniform 50% discount.
Google — Gemini 2.0 Flash-Lite batchOutput Derived at 50% of output — Google Batch API uniform 50% discount.
xAI — Grok 4 (legacy) cachedInput Extrapolated at 25% of base.

Pricing is cross-verified against the LiteLLM community registry when available. Daily snapshots are kept in aicost_pricing_snapshots; every change is logged to aicost_price_changelog with old & new values for full audit trail. Read the full methodology →