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.

📊 CALCULATOR AT A GLANCE
🚀 Open the full calculator ✉️ Email [email protected]
Token Estimator - From Pasted Prompt to Real Monthly Cost full size

🎛 Inputs you control

Each input shapes the cost. Click an input on the calculator to set it — 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.

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

Input Impact on result Range Typical
Tokens per request (paste your prompt to measure) If you've used the calc form above, this is auto-filled. If estimating: count system prompt + tools + retrieval + user message + history. Most teams: 3-5× their first guess. 100 – 50K 4800
Output tokens per request What the model generates. For chat: 200-800 typical. For structured outputs (JSON): often shorter than expected. For long-form writing: 1500-3000. 50 – 4K 600
Requests per day Active users × requests per user per day. James's team: 2,500 users × 2 messages/day = 5,000. 10 – 100K 5000

Outputs computed for you · model: token

Output How inputs affect it
Monthly cost ($) computed from inputs
Annual cost ($) monthlyUsd × 12

Below: live sliders. Move them to see numbers change in real time. * Output uses the generic compute model — for precise numbers use the full calculator below.

What you're looking at

Each input shapes your cost. Move the slider — see the impact.

4,800

If you've used the calc form above, this is auto-filled. If estimating: count system prompt + tools + retrieval + user message + history. Most teams: 3-5× their first guess.

Estimated: —
600

What the model generates. For chat: 200-800 typical. For structured outputs (JSON): often shorter than expected. For long-form writing: 1500-3000.

Estimated: —
5,000

Active users × requests per user per day. James's team: 2,500 users × 2 messages/day = 5,000.

Estimated: —

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 →

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.

Cheapest 3 vendors for your tokens right now

Verified 20 hours ago
  1. 1
    GPT-5 Mini
    $0.250 in · $2.00 out ·
  2. 2
    Command
    $1.00 in · $2.00 out ·
  3. 3
    devstral-2
    $0.400 in · $2.00 out ·

Three real scenarios

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

$341.23 / month ≈ $4,095 / 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

Trade-offs

Cost isn't the only dimension. Click any constraint — 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.

Use cases

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

$2,379 / month ≈ $28,545 / 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

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.

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
6/4/2026, 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 161 text models · 9 embeddings · 24 vision · 41 audio · 8 vector DBs across 10 vendor pages · last verified 2026-06-05

Methodology

  • All prices are USD per 1 million tokens, current as of 2026-06-05.
  • 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
2026-06-05
https://www.anthropic.com/pricing
Daily snapshot since Sep 2023 · 578 days captured
Anthropic Docs
2026-06-05
https://platform.claude.com/docs/en/about-claude/pricing
Daily snapshot since Sep 2023 · 578 days captured
OpenAI
2026-06-05
https://openai.com/api/pricing/
Daily snapshot since Sep 2023 · 579 days captured
Google AI
2026-06-05
https://ai.google.dev/gemini-api/docs/pricing
Daily snapshot since Dec 2023 · 554 days captured
Google Vertex
2026-06-05
https://cloud.google.com/vertex-ai/generative-ai/pricing
Daily snapshot since Dec 2023 · 554 days captured
DeepSeek
2026-06-05
https://api-docs.deepseek.com/quick_start/pricing
Daily snapshot since May 2024 · 493 days captured
xAI
2026-06-05
https://x.ai/api
Daily snapshot since Nov 2024 · 411 days captured
Mistral
2026-06-05
https://mistral.ai/pricing
Daily snapshot since Dec 2023 · 552 days captured
Cohere
2026-06-05
https://cohere.com/pricing
Daily snapshot since Sep 2023 · 578 days captured
Voyage AI
2026-06-05
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 →