anth-cost-tuning

# Anthropic Cost Tuning ## Overview Optimize Claude API spend through model routing, prompt caching, the Message Batches API, and real-time cost tracking. The four biggest levers: model selection (4-19x), prompt caching (10x input), batches (2x), and `max_tokens` discipline. ## Pricing Reference (per million tokens) | Model | Input | Output | Cache Read | Cache Write | |-------|-------|--------|------------|-------------| | Claude Haiku | $0.80 | $4.00 | $0.08 | $1.00 | | Claude Sonnet | $3.00 | $15.00 | $0.30 | $3.75 | | Claude Opus | $15.00 | $75.00 | $1.50 | $18.75 | **Message Batches:** 50% off all model pricing for async processing. ## Cost Calculator ```python def estimate_cost( input_tokens: int, output_tokens: int, model: str = "claude-sonnet-4-20250514", cached_input: int = 0, use_batch: bool = False ) -> float: pricing = { "claude-haiku-4-20250514": {"input": 0.80, "output": 4.00, "cache_read": 0.08}, "claude-sonnet-4-20250514": {"input": 3.00, "output": 15.00, "cache_read": 0.30}, "claude-opus-4-20250514": {"input": 15.00, "output": 75.00, "cache_read": 1.50}, } rates = pricing[model] uncached_input = input_tokens - cached_input cost = ( uncached_input * rates["input"] + cached_input * rates["cache_read"] + output_tokens * rates["output"] ) / 1_000_000 if use_batch: cost *= 0.5 return cost # Example: 10K requests/day, 500 input + 200 output tokens ...