Paste your prompt or content below, select the token mode, and see estimated token counts and costs across all major AI models. The comparison table ranks models from cheapest to most expensive for your specific text, making it easy to choose the best price-performance option.
Pro tip: Token counts are estimates — exact tokenization varies by model. Input and output tokens are priced differently, with output typically costing 2–5x more than input.
Suggestions to reduce token count without losing meaning.
How AI Tokenization Works
Tokens are the basic units that language models process. A token is roughly 4 characters or 0.75 words in English. The word “hamburger” might be split into “ham,” “bur,” and “ger” — three tokens. Common words like “the” are typically one token, while uncommon or long words may be split into multiple tokens.
Input vs Output Pricing
AI API providers charge separately for input tokens (your prompt) and output tokens (the model’s response). Output tokens are typically 2–5x more expensive because generating text requires more computation than reading it. This is why optimizing your prompts to be concise can significantly reduce costs.
Choosing the Right Model
Smaller models like GPT-4o mini and Claude Haiku can handle many tasks at a fraction of the cost of flagship models. Use the comparison table to identify the cheapest model for your use case. For simple tasks like classification and extraction, smaller models often perform comparably to larger ones at 10–20x lower cost.
Reducing API Costs
Strategies to lower AI API costs include: using concise system prompts, caching repeated context, choosing the smallest model that handles your task, batching requests where possible, and trimming unnecessary examples from few-shot prompts. Even removing redundant whitespace and filler words can reduce token counts by 10–20%.
How Context Windows Affect Token Costs
Most AI APIs charge for the entire context sent on every request, not just the new tokens you add. In a multi-turn conversation, each reply includes the full message history, so token counts (and costs) compound rapidly as the conversation grows. A chat with ten exchanges can easily consume ten times the tokens of a single isolated prompt. To keep costs under control, developers use techniques like message pruning (dropping the oldest turns), summarization (replacing early history with a compressed summary), and sliding window approaches (keeping only the most recent N tokens of context). Understanding your model’s context window limit is equally important — exceeding it causes truncation that silently drops earlier conversation, which can degrade response quality.
Comparing Token Efficiency Across Models
Different AI providers use different tokenization algorithms, so the same text does not always produce the same token count across models. OpenAI uses tiktoken (a byte-pair encoding scheme), Anthropic’s Claude uses a variant of SentencePiece, and Google’s Gemini uses its own tokenizer optimized for multilingual content. In practice, a 1,000-word English document might tokenize to 750 tokens with one model and 820 with another — a meaningful cost difference at scale. Code, technical jargon, and non-English text tend to show the largest variation because these tokenizers were trained on different corpora with different vocabularies. When benchmarking cost for your specific use case, always measure token counts against each model’s actual tokenizer rather than relying on character-count approximations.
If you run scheduled jobs that call AI APIs on a recurring basis, the Cron Expression Builder can help you design precise execution schedules to control when and how often those calls fire. For verifying API keys and signing tokens before sending requests, the Hash Generator provides HMAC and SHA hashing utilities that complement any AI integration workflow.