Claude Cost Calculator

Anthropic charges for Claude API usage based on the number of **input tokens** (the prompt sent to the model) and **output tokens** (the completion response generated by the model). Pricing is calculated per million (1M) tokens. Output tokens are typically **5x more expensive** than input tokens because generating text requires more computation than reading it. To convert text to tokens, a good rule of thumb is: **1 token ≈ 4 characters** or **0.75 words**. For example, a 750-word document is roughly 1,000 tokens.

**Prompt Caching** is a powerful feature that allows you to cache frequently used context, like large system prompts, source documents, or conversation history. When you send a prompt with caching enabled, Anthropic stores it. Subsequent requests that match the cached prefix bypass reading fees and are billed at a **cached read rate** (which is up to **90% cheaper** than standard input rates). For example, on Claude 3.5 Sonnet, uncached inputs cost $3.00/M tokens, while cached reads cost only $0.30/M tokens. To use prompt caching:

• Minimum Context Size: The prompt must be at least **10,000 tokens** for Claude 3.5 Sonnet and Claude 3 Opus, or **20,000 tokens** for Claude 3.5 Haiku.
• Write Cost: The first request that writes to the cache is billed at standard input rates plus a slight write overhead (typically 1.25x).
• Read Cost: Subsequent hits are billed at the highly discounted cached rate (10% of base input rate).

For workloads that do not require real-time responses (such as content translation, bulk summaries, or off-line data cleaning), Anthropic offers the **Message Batch API**. By submitting requests in batch files, you allow Anthropic up to 24 hours to process them in the background. In exchange, Anthropic provides a **flat 50% discount** on both input and output token rates across all models. Our Claude Cost Calculator allows you to check the Batch API box to instantly apply this 50% discount to all model calculations.

• 1. Implement Prompt Caching: Always cache stable resources like system instructions, RAG reference documents, or tools/schemas. Even a 70% cache hit rate can cut your input bills in half.
• 2. Select the Right Model Tier: Use a multi-model architecture. Route simple classification, routing, and text-trimming tasks to the fast, low-cost Haiku model. Reserve flagship Sonnet or Fable models only for high-complexity analysis.
• 3. Enforce Output Token Limits: Since output tokens are 5x more expensive than inputs, pass a strict `max_tokens` value and instruct the model to be concise to prevent runaway generation.
• 4. Use the Batch API for Bulk Tasks: If your application can tolerate a 24-hour SLA, queue jobs into batches to secure a permanent 50% cost reduction on all requests.