LLM API Cost Simulator: GPT-4o, Claude 3.5 & Gemini 1.5 Guide
Compare Monthly Token Costs for RAG, Customer Support & Agent Workloads
LLM API costs vary enormously across providers and models. GPT-4o charges $2.50/million input tokens and $10/million output tokens, while Claude 3.5 Sonnet is priced at $3/$15 per million. For a high-volume production deployment processing 50 million tokens monthly, the choice of model can mean a difference of $100,000+ per year in API costs.
Our simulator lets you input monthly input and output token volumes, select any combination of models, and see exact cost projections for 1 month, 6 months, and 1 year. It also calculates the break-even point between using a managed API and self-hosting open-source models like LLaMA 3 on cloud GPU instances.
1 token ≈ 0.75 English words (~4 characters). Output tokens typically cost 3× to 8× more per million than input tokens because autoregressive decoding is memory-bandwidth bound.
2026 Official LLM API Pricing & Prompt Caching Benchmark (Per 1M Tokens)
| Model & Provider | Input ($/1M) | Cached Input ($/1M) | Output ($/1M) | Context Window | Cost at 50M In (50% Cache) + 10M Out |
|---|---|---|---|---|---|
| Anthropic Claude Sonnet 4.6 | $3.00 | $0.30 (90% Off) | $15.00 | 1,000,000 | $232.50 / mo |
| OpenAI GPT-5.4 Flagship | $2.50 | $0.25 (90% Off) | $15.00 | 1,050,000 | $218.75 / mo |
| OpenAI GPT-4.1 | $2.00 | $0.50 (75% Off) | $8.00 | 1,000,000 | $142.50 / mo |
| Google Gemini 2.5 Pro | $1.25 | $0.125 (90% Off) | $10.00 | 1,000,000 | $134.38 / mo |
| Anthropic Claude Haiku 4.5 | $1.00 | $0.10 (90% Off) | $5.00 | 200,000 | $77.50 / mo |
| Google Gemini 2.5 Flash | $0.30 | $0.03 (90% Off) | $2.50 | 1,000,000 | $33.25 / mo |
| DeepSeek V3.2 (Chat/Reasoner) | $0.28 | $0.028 (90% Off) | $0.42 | 128,000 | $11.90 / mo |
| OpenAI GPT-4o-mini | $0.15 | $0.075 (50% Off) | $0.60 | 128,000 | $11.63 / mo |
How Prompt Caching Cuts RAG and Agentic System Spend by Up to 90%
In production AI architectures—such as multi-turn coding agents, legal document analyzers, and RAG customer support pipelines—the same system instructions, tool schemas, and reference documents are sent repeatedly on every API call. Modern providers (Anthropic, OpenAI, Google Gemini, and DeepSeek) support KV-cache reuse ('Prompt Caching'), discounting cached input tokens by 75% to 90%. Placing static system prompts, few-shot examples, and repository context at the very beginning of your prompt prefix maximizes cache hit rates.
Model Routing: Pairing Frontier Reasoning Models with Fast Sub-Agents
Running 100% of production traffic through a frontier model ($2.50–$3.00/1M input and $15.00/1M output) wastes budget on classification, extraction, and summarization tasks. Implementing a two-tier model router—sending 80% of routine requests to high-speed models like Gemini 2.5 Flash, Claude Haiku 4.5, or GPT-4o-mini while reserving Claude Sonnet 4.6 or GPT-5.4 for complex multi-step reasoning—typically reduces monthly API invoices by 65%–80% with negligible quality loss.
When Does Self-Hosting Open-Weight Models (Llama 4 / DeepSeek) Beat Managed APIs?
A dedicated 8×H100 GPU node costs roughly $14,000 to $20,000 per month on cloud GPU providers before DevOps overhead. Consequently, serverless token APIs are almost always more economical until your sustained, uniform throughput exceeds 150M–300M tokens per day or strict air-gapped data residency is required.
Frequently Asked Questions & Statutory Rules
Is it cheaper to use GPT-4o API or self-host an open-source model?
At low to medium volumes, GPT-4o API is cheaper when accounting for GPU rental, engineering, and maintenance. At very high volumes (100M+ tokens/month), self-hosting LLaMA 3 70B becomes cost-competitive.
Why are output tokens 3x to 8x more expensive than input tokens?
Input tokens are processed in parallel during a single 'prefill' pass across GPU tensor cores, whereas output tokens are generated sequentially one token at a time ('autoregressive decoding'), requiring the entire model weights to be read from HBM memory for every single token.
How do 'thinking' or 'reasoning' tokens affect my API bill?
Reasoning models (such as Gemini 2.5 Pro, Claude Sonnet 4.6 extended thinking, OpenAI o-series/GPT-5.4 reasoning, and DeepSeek Reasoner) bill internal chain-of-thought tokens at the model's output token rate even if only the final summary is displayed to the user.
What is the Batch API discount?
OpenAI, Anthropic, and Google offer a 50% discount on both input and output tokens for asynchronous workloads (such as overnight data labeling, evaluations, or bulk embeddings) completed within a 24-hour window.