What is an AI Token?
A token is the basic unit of text that AI language models process. For English text, 1 token is approximately 4 characters or 0.75 words. So a typical sentence of 20 words equals about 27 tokens. Both your input (prompts) and output (responses) are measured in tokens and charged accordingly.
Understanding token costs is essential for anyone building AI-powered products, automating workflows, or managing AI infrastructure costs. Token pricing varies dramatically between providers and model tiers.
LLM Token Pricing Comparison 2026
Here's how the major providers stack up on cost per 1 million tokens:
| Model | Provider | Input / 1M Tokens | Output / 1M Tokens | Best For |
|---|---|---|---|---|
| Gemini 3 Flash-Lite | $0.05 | $0.20 | Highest volume, cheapest option | |
| Gemini 3 Flash | $0.075 | $0.30 | Balanced cost for real-time apps | |
| GPT-4o mini | OpenAI | $0.15 | $0.60 | High volume, cost-sensitive apps |
| DeepSeek V3 | DeepSeek | $0.27 | $1.10 | Open-weight, strong performance |
| Claude 3.5 Haiku | Anthropic | $0.80 | $4.00 | Fast, affordable casual tasks |
| Gemini 3 Pro | $0.35 | $1.05 | Mid-range capability, large context | |
| Claude 4 Sonnet | Anthropic | $3.00 | $15.00 | Long docs, analysis, coding, writing |
| GPT-4o | OpenAI | $2.50 | $10.00 | General purpose, code, reasoning |
| GPT-5 | OpenAI | $10.00 | $40.00 | Maximum capability |
How to Use This Calculator
- Select a Model: Choose from the provider dropdown — pricing auto-fills
- Enter Input Tokens: The token count of your prompt, context, or system instructions
- Enter Output Tokens: Expected or actual response token count
- View Results: See the total cost plus per-1K breakdowns instantly
Token Cost Formula
Total Cost = (Input Tokens × Input Price) + (Output Tokens × Output Price)
Where both prices are per 1,000,000 tokens (divide by 1,000,000 for the calculation).
Example: 1,000 input tokens + 500 output tokens on GPT-4o:
Input: (1,000 / 1,000,000) × $2.50 = $0.00250
Output: (500 / 1,000,000) × $10.00 = $0.00500
Total: $0.00750
Real-World Examples
Example 1: Chatbot Conversation
Model: GPT-4o mini (most cost-effective for chat)
Input: 200 tokens (short user message)
Output: 150 tokens (short response)
Cost: (200/1M × $0.15) + (150/1M × $0.60) = $0.00012 per message
1,000 conversations → $0.12 | 100,000 conversations → $12.00
Example 2: Document Analysis
Model: Claude 3.5 Sonnet (great for long documents)
Input: 8,000 tokens (10-page document)
Output: 600 tokens (detailed summary)
Cost: (8,000/1M × $3.00) + (600/1M × $15.00) = $0.03300 per document
100 documents → $3.30 | 1,000 documents → $33.00
Example 3: RAG Pipeline (10,000 queries/month)
Model: Gemini 3 Flash-Lite (cheapest for high-volume RAG)
Input: 500 tokens (retrieved context + query)
Output: 200 tokens (answer)
Cost per query: (500/1M × $0.05) + (200/1M × $0.20) = $0.000065
10,000 queries/month → $0.65/month | 1M queries/month → $65/month
How to Reduce AI Token Costs
- Use smaller models for simple tasks: GPT-4o mini or Gemini Flash-Lite cost 10–50x less than GPT-5 or Claude Opus for straightforward queries
- Optimize prompts: Remove redundant instructions and context. Every token you save is money saved
- Implement smart routing: Route simple queries to cheap models, complex ones to capable models
- Cache responses: For repeated queries, cache results and avoid re-computation
- Use completion hints: Many APIs support max_tokens limits to cap output costs
- Batch API calls: Some providers offer batch pricing at 50% discount for async processing
Frequently Asked Questions
How many tokens is my text?
As a rough estimate: 1 token ≈ 4 characters or 0.75 words in English. For precise counting, use OpenAI's tokenizer tool or Anthropic's token counter. For typical English prose: 1,000 words ≈ 1,333 tokens. Code typically uses more tokens per word than prose.
Why are output tokens more expensive than input tokens?
Output (completion) tokens require more compute because the model generates them token-by-token using probabilistic sampling, while input tokens are processed in a single forward pass. Most providers charge 2–5x more for outputs to reflect this difference.
Which model gives the best value for money?
For cost-effectiveness: Gemini 3 Flash-Lite ($0.05/$0.20 per 1M) is the cheapest for most use cases. For capability per dollar, Claude 4 Sonnet often outperforms its price tier. For maximum quality regardless of cost, GPT-5 or o3 are the top performers.
Is self-hosting cheaper than using paid APIs?
At high volume (>10M tokens/month), self-hosting DeepSeek V3 can be cheaper. However, you pay for GPU infrastructure (~$0.50–2.00/hr per A100), maintenance, and ops overhead. For most teams under 1B tokens/month, paid APIs offer better value.