LLM API Token & Cost Calculator
Estimate single request costs, monthly API burn rates, and compare savings across Claude 3.7, GPT-4o, Gemini 2.5, and DeepSeek.
⚙️ Configuration Parameters
Optimization Discounts
Simulates 50%-75% discount on cached input tokens (Anthropic / OpenAI / DeepSeek).
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Full Market Cost Comparison
Same request parameters (1.5K in / 500 out @ 5K/day) across all frontier models.
| Model Provider | Input / 1M | Output / 1M | Cost / Req | Monthly Spend | Cost Delta |
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Understanding LLM API Pricing Dynamics
Essential rules for budgeting, architecting, and optimizing AI applications.
Why are output tokens 3x to 5x more expensive than input tokens?
Input tokens can be processed in parallel by GPU tensor cores using matrix multiplication during the prefill phase. In contrast, output tokens must be generated autoregressively — one token at a time — requiring memory bandwidth passes for every single generated word. This makes generation significantly more compute-heavy and memory-bound for data centers.
How does prompt caching fundamentally cut token expenses?
When sending large system prompts, retrieval augmented generation (RAG) context chunks, or few-shot examples, providers like Anthropic and OpenAI save the KV cache state in VRAM. If your next request reuses the same prefix, the model skips re-encoding that block, slashing input costs by up to 90% on Anthropic and up to 75% on DeepSeek.
When should you switch from a hosted API to self-hosted vLLM?
If your monthly API expenditure on models like Llama 3.3 70B or DeepSeek exceeds $1,500/month, renting an 8x H100 or A100 GPU cluster through RunPod or Lambda Labs and running vLLM often cuts token costs by 40% to 65%, while eliminating external rate limits.