LLM API Pricing &
Cost Calculator
Compare API pricing and calculate real-world monthly costs for top AI models (GPT-6 Astra, Claude Opus 5.5, Gemini 3.8 Flash, DeepSeek-V4.1-Flash). Unlike basic calculators, our engine accurately simulates provider-specific Prompt Caching (Write surcharges, Read discounts, hourly storage fees) and long context tiers.
Understanding LLM API Pricing & Cost Optimization
Essential concepts and practical techniques to minimize your AI infrastructure spending
Input vs. Output Pricing Ratio (3-5x)
API pricing is billed per 1 million (1M) tokens. Output tokens (text generated by AI) are typically 3x to 5x more expensive than input tokens. Choosing a model with affordable output rates is critical for long-form generation tasks.
Prompt Caching: Up to 90% Savings
Frontier models from OpenAI, Anthropic, Google, and DeepSeek support prompt caching. By keeping reusable instructions and context in memory, you can slash input costs by 50% to 90% on repeated calls.
Intelligent Multi-LLM Routing
Instead of sending every query to the most expensive frontier model, route lightweight classification and extraction to Flash/Mini models, and reserve flagship models for complex reasoning and coding.
Frequently Asked Questions (FAQ)
In-depth answers to LLM API formulas, caching architectures, long context tiers, and cost reduction
Q. How does this simulator calculate monthly LLM API expenses?＋
The core equation is "(Requests × Input Tokens ÷ 1,000,000 × Input Rate) + (Requests × Output Tokens ÷ 1,000,000 × Output Rate)". Our simulator further isolates input into "Shared Context" (system prompt/documents) and "Unique Tokens", modeling provider-specific Prompt Caching rules, Long Context tiered thresholds, and hourly storage charges. Click "View Formula" on any model card to inspect the exact mathematical breakdown (Write, Read, Storage).
Q. How do Prompt Caching billing models differ across Anthropic, Google, OpenAI, and DeepSeek?＋
Each provider employs unique caching mechanics modeled by our engine: • Anthropic (Claude): Charges a cache write creation surcharge (1.25x for 5-min TTL, 2.0x for 1-hour TTL) with subsequent reads (N-1) discounted by 90%. • Google (Gemini): Charges a 1x standard write fee + discounted read rate, plus an hourly storage fee per 1M tokens. • OpenAI & DeepSeek: DeepSeek automatically caches prompts above 64 tokens with no write fee; OpenAI 5.6 / 6 models (such as GPT-6 Astra) feature a 30-min TTL with a 1.25x write surcharge and deeply discounted reads.
Q. When does Prompt Caching become more expensive than standard non-cached calls?＋
When request volume is very low (1-2 calls) or when Gemini context is stored across long idle periods, the sum of write surcharges and storage fees can exceed standard input costs. Our simulator includes an automated "Cost Guard" that disables caching and falls back to standard rates whenever caching would result in higher total expenses.
Q. How do Long Context Tiered Pricing Thresholds work?＋
Certain models raise input, output, and cache rates by 1.5x to 2x once prompt volume exceeds defined thresholds. Our simulator detects when your input exceeds these limits and automatically applies longContextPricing rates.
Q. How are Benchmark Scores (0–100) calculated and normalized?＋
Benchmark scores (0–100) are normalized metrics synthesized from authoritative public evaluations including MMLU-Pro, SWE-bench, HumanEval, MATH, and GPQA. Sorting by "By Benchmark" allows you to evaluate price-to-performance tradeoffs across all frontier models.
Q. What are the top practical strategies to minimize production AI costs?＋
1. Multi-LLM Tiering: Route routine classification and extraction to ultra-low-cost models (Gemini Flash, GPT Mini, DeepSeek at $0.1–$0.5/1M), reserving flagship models solely for complex reasoning. 2. Prompt Prefix Architecture: Place static instructions and background context at the very top of prompts, exceeding provider minimums (e.g. 1,024 tokens) to achieve 80–90% caching discounts. 3. Output Token Constraints: Because output tokens cost 3–5x more than input, enforce strict max_tokens and concise structured schemas.
Note: Performance scores are reference values (0-100) compiled based on various benchmarks.