Token Reduction Analyzer

Paste your prompt. See what's wasteful.

Automated analysis of redundancy, verbosity, and low-value tokens. Typical prompts have 30-50% reducible tokens.

Client-side analysis · nothing leaves your browser Pricing verified: 2026-06-13
📖 What this is / how to use
What this calculator does

Paste a prompt and see exactly which tokens are wasteful — politeness padding, redundant instructions, over-stuffed few-shot examples — and what trimming them saves at your volume.

Why use it
  • Most prompts carry 20-50% redundant tokens that add cost without improving output
  • Token reduction stacks multiplicatively with routing and caching
  • Analysis runs locally in your browser — your prompt is never sent anywhere
  • See the dollar impact at your request volume across every model
📖 Read the full guide →

These are the inputs, outputs, and how you can use this calculator for your AI workloads.

📥 Inputs you provide
  • Your promptThe text to analyze
  • ModelModel used to price savings
  • Requests per dayDaily call volume for this prompt
📤 Outputs you get
  • After optimizationToken count once cuts are applied
  • Potential monthly savingsDollars saved per month
  • Token reductionShare of input you can cut
  • FindingsRanked waste patterns
🎯 Use your results to
✂️
Trim the waste

Cut padding, redundancy, and over-stuffed examples; 30-50% reduction is common

📐
Restructure for clarity

Consolidate instructions and prune few-shot down to what actually helps

💰
See the dollar impact

Token cuts become real monthly and annual dollars at your volume

🔌
Build it into your pipeline

Run reductions as a step in your prompt build; MCP available for agents

👇 Now try the calculator below with your own AI workloads

📊 Calculator at a glance
📊 How it works (diagram)
📅 Schedule a meeting via AvatarVA ✉️ Email [email protected]
Token Reduction Analyzer full size
🎛 CALCULATOR
📝 Paste your promptThe prompt to analyze — system prompt, user template, or any repeated AI input. Detection runs entirely in your browser; nothing is uploaded.How to choose: Paste the actual text you send in production, including system instructions, tool definitions, and few-shot examples. The more representative the sample, the more accurate the findings. Use a Load-sample button if you just want to see how it works.Read the full guide →

System prompt, user template, or any repeated AI input. Analysis runs locally.

Load sample
📈 RESULTS
Potential monthly savings
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Current tokens
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After optimization
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Current monthly cost
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Optimized monthly
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🔍 Findings
📊 Cost across models - before & after optimization

What you save on each model if you apply all suggestions.

Model Current / day Optimized / day Monthly savings Annual savings
Token estimator → Cache what's left → Prompt caching guide →
📋 What now?
📅 Book a prompt-optimization session to apply this to your workload →

Go deeper

Our playbooks on cutting this number.

💾
Prompt Cache ROI
Cache the slimmed prompt
📝
Token Estimator
Compare across all models
📖
Caching Playbook
Implementation details
🧮
Cost Calculator
Full workload cost

Need help using this calculator for your workloads?

AICost.ai has 50+ calculators and playbooks. Schedule an AvatarVA meeting and we'll work through your real cost scenarios across AI & Cloud: visibility, cost reduction, optimization, forecasting and capacity planning, without sacrificing accuracy or performance.

📅 Schedule an AvatarVA meeting →
📖 Data sources & methodology 171 text models · 9 embeddings · 30 vision · 46 audio · 8 vector DBs across 10 vendor pages · last verified 2026-06-13

Methodology

  • All prices are USD per 1 million tokens, current as of 2026-06-13.
  • 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
2026-06-13
https://www.anthropic.com/pricing
Daily snapshot since Sep 2023 · 586 days captured
Anthropic Docs
2026-06-13
https://platform.claude.com/docs/en/about-claude/pricing
Daily snapshot since Sep 2023 · 586 days captured
OpenAI
2026-06-13
https://openai.com/api/pricing/
Daily snapshot since Sep 2023 · 587 days captured
Google AI
2026-06-13
https://ai.google.dev/gemini-api/docs/pricing
Daily snapshot since Dec 2023 · 562 days captured
Google Vertex
2026-06-13
https://cloud.google.com/vertex-ai/generative-ai/pricing
Daily snapshot since Dec 2023 · 562 days captured
DeepSeek
2026-06-13
https://api-docs.deepseek.com/quick_start/pricing
Daily snapshot since May 2024 · 501 days captured
xAI
2026-06-13
https://x.ai/api
Daily snapshot since Nov 2024 · 419 days captured
Mistral
2026-06-13
https://mistral.ai/pricing
Daily snapshot since Dec 2023 · 560 days captured
Cohere
2026-06-13
https://cohere.com/pricing
Daily snapshot since Sep 2023 · 586 days captured
Voyage AI
2026-06-13
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 →