✓ All prices verified

Token Calculator 2026 - Compare 39+ AI Model Prices | Claude Opus 4.6, GPT-5.2, Gemini 3 Pro

The most accurate token calculator for Large Language Models. Compare real-time pricing for 39 AI models from 4 providers including Google (Gemini 3 Pro, Gemini 3 Flash, Gemini 2.5 Pro), Anthropic (Claude Opus 4.6, Claude Opus 4.5, Claude Sonnet 4.5, Claude Haiku 4.5), OpenAI (GPT-5.2, GPT-5.1, GPT-4o), and xAI (Grok 4.1). Get precise token counts and cost estimates with support for prompt caching, batch API pricing, and long context windows. Claude Opus 4.6 pricing is updated from Anthropic's latest official release. Calculate per API call, daily usage, and monthly projections. Updated February 2026.

Supported AI Model Providers

  • Anthropic
  • Google
  • OpenAI
  • xAI

Key Features

  • Real-time token counting with official tokenizers
  • Support for system, user, and assistant messages
  • Cached input pricing calculations
  • Multi-currency support (USD, EUR, GBP, JPY, CNY)
  • JSON import/export for conversation data
  • Model comparison across all providers
  • Daily and monthly cost projections
  • Export cost reports as PNG images

Popular Model Pricing

Average input pricing: $2.65 per million tokens

  • Claude Haiku 3.5: Input $0.8/M, Output $4/M tokens
  • Claude Haiku 4.5: Input $1/M, Output $5/M tokens
  • Claude Opus 4.1 (Legacy): Input $15/M, Output $75/M tokens
  • Claude Opus 4.5 (Legacy): Input $5/M, Output $25/M tokens
  • Claude Opus 4.6: Input $5/M, Output $25/M tokens
  • Claude Sonnet 3.7 (Legacy): Input $3/M, Output $15/M tokens
  • Claude Sonnet 4: Input $3/M, Output $15/M tokens
  • Claude Sonnet 4.5: Input $3/M, Output $15/M tokens
  • Gemini 2.0 Flash: Input $0.1/M, Output $0.4/M tokens
  • Gemini 2.0 Flash-Lite: Input $0.075/M, Output $0.3/M tokens
🔥
NEW: Claude Opus 4.6 Released (Feb 10, 2026)
$5.00/1M input • $25.00/1M output • 90% cache discount • 1M context beta →
🎨
New: AI Image Pricing Calculator
Compare GPT Image 1, Gemini, Stable Diffusion - From $0.003/image →

Token Calculator & API Cost Estimator

Compare real-time pricing for 39 AI models from 4 providers

Quick Price Comparison

ModelProviderInput $/1MOutput $/1MContext
Claude Opus 4.6🔥 NEWAnthropic$5.000$25.000200,000
GPT-5.2🔥 NEWOpenAI$1.750$14.000400,000
GPT-5.1🔥 NEWOpenAI$1.250$10.000200,000
Gemini 3 Pro Preview🔥 NEWGoogle$2.000$12.0002,000,000
Claude Sonnet 4.5🔥 NEWAnthropic$3.000$15.000200,000
Claude Haiku 4.5🔥 NEWAnthropic$1.000$5.000200,000
Claude Opus 4.5 (Legacy)🔥 NEWAnthropic$5.000$25.000200,000
Grok 4.1🔥 NEWxAI$0.200$0.5002,000,000
GPT-5.2 Pro🔥 NEWOpenAI$21.000$168.000400,000
GPT-5.1 miniOpenAI$0.250$2.000200,000
Gemini 2.5 FlashGoogle$0.300$2.5001,000,000
Claude Haiku 3.5Anthropic$0.800$4.000200,000

Showing top 10 models • Download complete table (CSV) above • Interactive calculator loads below

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Use Cases

Whether it's project launch, model selection, or cost optimization, Token Calculator helps you make accurate decisions

Project Cost Estimation

Project Cost Estimation

Estimate AI API costs before project launch to avoid budget overruns. Input expected user volume and conversation frequency for instant daily/monthly cost projections.

Chatbot cost planningAI customer service budgetSmart document processing fees

Model Comparison & Selection

Model Comparison & Selection

Compare pricing and performance across 20+ mainstream models to find the perfect fit for your project. Filter by price, context window, caching support, and more.

GPT-5 vs Claude Opus 4.1Gemini vs Grok cost-effectivenessSmall vs Large model scenarios

Bill Review & Verification

Bill Review & Verification

Verify API billing accuracy after receiving invoices. Our calculator uses official tokenizers to ensure 99.9% accuracy in token counting.

OpenAI bill verificationAnthropic fee confirmationAbnormal charge investigation

Cost Optimization Strategy

Cost Optimization Strategy

Test different optimization strategies: prompt compression, caching utilization, smaller model alternatives. See cost reduction effects in real-time for data-driven optimization decisions.

Cached Input saves 90%Batch API discount calculationPrompt engineering cost reduction

Start calculating now, optimize your AI project costs

100% free to use, no registration required, all calculations are done locally, data never uploaded

210+ AI models supported
99.9% accuracy
Real-time pricing updates

🆕 Featured Model Calculators

🔥 NEW

Claude Opus 4.6

200K context • $5.00/1M input • Latest Anthropic flagship

Anthropic Flagship
🔥 NEW

GPT-5.2

400K context • $1.75/1M input • Latest OpenAI flagship

OpenAI Flagship
HOT

Claude Sonnet 4.5

200K context • $3.00/1M input • Strong coding performance

Anthropic Balanced
🔥 NEW

Claude Haiku 4.5

200K context • $1.00/1M input • Latest low-latency Claude

Anthropic Fast Tier
⚡

Gemini 2.5 Pro

200K context • $1.25/1M input • Strong value in Google stack

Google Pro
🔥 Opus 4.6 vs GPT-5.2🔥 Opus 4.6 vs Gemini 3 Pro🔥 Opus 4.6 vs Grok 4.1🔥 Opus 4.6 vs Sonnet 4.5
🔥 GPT-5.2 vs Sonnet 4.5🔥 GPT-5.2 vs Gemini 3 Pro🔥 GPT-5.2 vs GPT-5.1🔥 Grok 4.1 vs GPT-5.1
💰 Grok 4.1 vs Gemini 3 Pro💰 100K Opus 4.6 tokens cost💰 100K Haiku 4.5 tokens cost🆚 View all model comparisons
⭐ Best AI Models 2025 Guide💰 Grok 4.1 vs Gemini 3 Pro📚 Browse all calculators🧮 Open interactive calculator
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Frequently Asked Questions

How accurate is the token count compared to actual API billing?
Our calculator achieves 99.9% accuracy by using the exact same tokenizers as the API providers. For OpenAI models, we use the official tiktoken library. For Anthropic's Claude models, we implement their tokenization algorithm. This means our counts match exactly what you'll be billed for, unlike estimators that use simple character division.
What is cached input pricing and how much can it save?
Cached input pricing lets you reuse repeated context (system prompts, instructions, long docs) at a lower rate. Anthropic, OpenAI, Google, and xAI all support caching on key models. Example: Claude Opus 4.6 input is $5/1M tokens, while cached reads are $0.50/1M tokens, a 90% reduction on cached input.
Which AI model offers the best price-to-performance ratio in 2026?
There is no single winner across every workload. In early 2026, Claude Haiku 4.5, GPT-5 mini, and Gemini Flash-class models are common value choices for high-volume apps, while Claude Opus 4.6 and GPT-5.2 are premium options for harder reasoning tasks. The best choice depends on latency targets, context length, and output quality requirements.
How do I calculate costs for a production chatbot serving 10,000 users?
Estimate average tokens per conversation first, then multiply by user and session volume. Example: 10,000 users × 2 conversations/day × 1,000 tokens = 20M tokens/day. Convert that into input/output splits (for example 70/30) and apply your model pricing. Use this calculator's requests/day and cached-input toggle to project daily and monthly spend before deployment.
Can I use this calculator for fine-tuned or custom models?
Yes, our calculator supports fine-tuned model pricing. OpenAI's fine-tuned models may differ from base rates. For GPT-4o fine-tuned, we use $3.75/1M input, $15/1M output, and $1.875/1M cached input as defaults. You can also set custom enterprise prices if needed. Tokenization is unchanged, so counts remain accurate.
How often are the model prices updated and verified?
Prices are updated directly from official provider documentation, and each model includes a last-verified date. For major releases or pricing changes, updates are typically shipped the same day. Always double-check enterprise or region-specific pricing in your provider account because contracted rates can differ from public tables.
What's the difference between streaming and batch API pricing?
Streaming and non-streaming usually have the same token pricing. Batch APIs can be cheaper when you don't need immediate responses. For example, OpenAI and Anthropic publish batch discounts on supported models. This calculator shows standard synchronous rates; apply provider-specific batch multipliers when modeling delayed workloads.
How do I optimize token usage to reduce API costs?
Key levers: 1) Cache repeated context blocks. 2) Trim prompts and keep instructions concise. 3) Route easy tasks to cheaper models and reserve premium models for hard cases. 4) Set strict max output limits. 5) Use batch mode for non-real-time jobs. 6) Tune RAG chunking so you send only relevant context. These controls usually cut spend significantly without harming quality.

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