✓ All prices verified
Token Calculator 2025 - Compare 24+ AI Model Prices
The most accurate token calculator for Large Language Models. Compare real-time pricing for 24 AI models from 4 providers including OpenAI (GPT-4o, GPT-4-turbo), Anthropic (Claude 3.5 Sonnet, Claude 3.5 Haiku), Google (Gemini Pro, Gemini Flash), and xAI (Grok). Get precise token counts and cost estimates per API call, daily usage, and monthly projections.
Supported AI Model Providers
- Anthropic
- 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: $2.52 per million tokens
- Claude Haiku 3.5: Input $0.8/M, Output $4/M tokens
- Claude Opus 4.1: Input $15/M, Output $75/M tokens
- Claude Sonnet 3.7 (Legacy): Input $3/M, Output $15/M tokens
- Claude Sonnet 4: 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
- Gemini 2.5 Flash: Input $0.3/M, Output $2.5/M tokens
- Gemini 2.5 Flash-Lite: Input $0.1/M, Output $0.4/M tokens
- Gemini 2.5 Pro: Input $1.25/M, Output $10/M tokens
- GPT-4.1: Input $2/M, Output $8/M tokens
Loading calculator...
🆕 Featured Model Calculators
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 is a feature offered by providers like Anthropic and Google where you can reuse the same context (system prompt, examples, documents) across multiple API calls at a 50-90% discount. For example, Claude 3.5 Sonnet's regular input is $3/1M tokens, but cached input is only $0.30/1M tokens - a 90% savings. This is ideal for chatbots, RAG systems, or any application with repeated context.
Which AI model offers the best price-to-performance ratio in 2025?
As of September 2025, Claude 3.5 Haiku offers exceptional value at $0.25/1M input tokens with performance rivaling GPT-4o-mini. For high-volume applications, Gemini 1.5 Flash provides competitive pricing with a massive 1M token context window. GPT-4o-mini remains popular for its balance of cost ($0.15/1M input) and OpenAI ecosystem integration. The 'best' choice depends on your specific needs: latency requirements, context length, and feature support.
How do I calculate costs for a production chatbot serving 10,000 users?
For production scaling: 1) Estimate average conversation length (typically 5-10 exchanges). 2) Calculate tokens per conversation (usually 500-2000 tokens total). 3) Multiply by daily active users and conversation frequency. Example: 10,000 users × 2 conversations/day × 1,000 tokens = 20M tokens/day. With GPT-4o-mini, that's about $3-12/day depending on input/output ratio. Our calculator's 'requests per day' feature helps you model these scenarios precisely.
Can I use this calculator for fine-tuned or custom models?
Yes, our calculator supports fine-tuned model pricing. OpenAI's fine-tuned models typically cost 2x the base model rate. For GPT-4o fine-tuned, that's $5/1M input tokens instead of $2.50. We also support custom enterprise models if you know the pricing. The tokenization remains the same, so counts are still accurate. For completely custom models, you can use our base tokenizers as approximations.
How often are the model prices updated and verified?
We verify all prices daily through automated checks against provider APIs and documentation. When providers announce price changes, we typically update within 2-4 hours. Each model shows a 'last verified' timestamp. We also track historical pricing trends, which is valuable for budgeting and forecasting. Major price drops in 2024-2025 have made LLMs 70% cheaper on average.
What's the difference between streaming and batch API pricing?
Most providers charge the same for streaming and non-streaming requests - you pay for total tokens regardless of delivery method. However, OpenAI offers Batch API with 50% discount for non-urgent requests (24-hour turnaround). Some providers like Anthropic offer priority tiers with different pricing. Our calculator shows standard synchronous pricing by default, but you can mentally apply batch discounts where applicable.
How do I optimize token usage to reduce API costs?
Key strategies: 1) Use system message caching for repeated contexts (90% savings). 2) Implement prompt compression techniques - remove unnecessary words while maintaining clarity. 3) Use smaller models where possible - GPT-4o-mini often suffices instead of GPT-4o. 4) Batch similar requests together. 5) Set appropriate max_tokens limits. 6) For RAG systems, optimize chunk sizes (we have a RAG Chunk Optimizer tool). These techniques can reduce costs by 50-70% without sacrificing quality.
Related Resources for AI Developers
Build LLM Agents: Visual Guide to AI Development
Learn how to build autonomous AI agents
Top 10 RAG Frameworks 2024: Complete Guide
Choose the best RAG framework for your project
AI Programming Assistant: Future of Coding
How AI is transforming software development
What is Agentic RAG? Complete Implementation Guide
Advanced RAG patterns for production systems
Supervised Fine-Tuning: A Practical Guide
Optimize LLMs for your specific use case
Ollama Guide: Run LLMs Locally
Deploy models on your own hardware