Cheapest Option
Gemini 2.5 Flash
$67.50/month
Premium Model
Claude Opus 4.5
$15,750/month
Speed optimized, huge context
OpenAI
Ultra-low cost for simple tasks
DeepSeek
#1 open-source, exceptional coding
Mistral
Great speed/cost balance
OpenAI
Affordable & fast for everyday tasks
DeepSeek
Reasoning model rivaling o1
Anthropic
Fastest Claude for quick responses
Mistral
675B params, top-tier performance
OpenAI
Latest flagship model, 400K context
Best for complex reasoning & code
OpenAI
Previous gen multimodal model
Latest multimodal, 2M context
Anthropic
Best for coding, agents & agentic tasks
Anthropic
Most intelligent Claude, infinite chats
💡 Tips to Reduce Costs
- • Use prompt caching for repeated contexts
- • Choose smaller models for simple tasks
- • Optimize prompts to reduce token count
- • Batch requests when possible
📊 Understanding Tokens
- • ~4 characters = 1 token (English)
- • ~750 words ≈ 1,000 tokens
- • Output tokens cost more than input
- • Context window limits total tokens
⚠️ Disclaimer: Prices are updated periodically and may not reflect the latest rates. Always check the official provider documentation for current pricing.
About LLM Cost Calculator
Compare pricing across OpenAI, Anthropic, Google, and other AI providers. Enter your expected usage and see cost estimates for GPT-4, Claude, Gemini, Deepseek, and more.
AI API costs add up quickly at scale. This calculator helps you compare models side-by-side, estimate monthly costs, and find the most cost-effective option for your use case without surprise bills.
How to use LLM Cost Calculator
Enter your expected input tokens per request.
Enter expected output tokens per request.
Set your estimated requests per day.
View cost comparison across all models.
Adjust parameters to explore different scenarios.
Examples
Chatbot cost estimation
Typical customer service chatbot scenario:
Input: 500 tokens (user message + context) Output: 200 tokens (response) Volume: 1,000 requests/day GPT-4o: $X/month Claude 3.5: $Y/month Gemini Pro: $Z/month Choose based on quality vs. cost tradeoff.
Document processing
Processing long documents for summarization:
Input: 10,000 tokens (full document) Output: 500 tokens (summary) Volume: 100 docs/day Long context = high input costs. Consider: is 128k context worth the premium over chunking with smaller context models?
Batch vs. real-time
Background processing can save 50%:
Real-time API: Full price, instant results Batch API: 50% discount, hours delay For non-urgent tasks (reports, analysis), batch processing cuts costs in half.
Features
When to use this
- •Budgeting for AI-powered product features
- •Comparing models for cost optimization
- •Estimating startup runway with AI costs
- •Choosing between GPT-4 vs Claude vs Gemini
- •Planning batch processing vs real-time API usage
- •Justifying AI investments to stakeholders