LLM Token & API Cost Calculator
BPE & Prompt CachingEstimate prompt token density and compare production API inference costs across OpenAI, Anthropic, and Google Gemini.
1. Live Prompt Token Estimator (BPE Heuristic)
2. API Workload Volume & Prompt Caching
3. Provider Pricing Matrix (150,000 reqs/mo)
| Model | Input / 1M | Output / 1M | Daily | Monthly |
|---|---|---|---|---|
GPT-4o OpenAI (128K) | $2.50 | $10.00 | $50.00 | $1,500.00 |
GPT-4o mini OpenAI (128K) | $0.15 | $0.60 | $3.00 | $90.00 |
o3-mini (Reasoning) OpenAI (200K) | $1.10 | $4.40 | $22.00 | $660.00 |
Claude 3.7 Sonnet Anthropic (200K) | $3.00 | $15.00 | $64.20 | $1,926.00 |
Claude 3.5 Haiku Anthropic (200K) | $0.80 | $4.00 | $17.12 | $513.60 |
Gemini 2.5 Pro Google (2M) | $1.25 | $10.00 | $38.74 | $1,162.20 |
Gemini 2.0 Flash Google (1M) | $0.10 | $0.40 | $1.90 | $57.00 |
{
"workloadParameters": {
"inputTokensPerRequest": 2000,
"outputTokensPerRequest": 600,
"requestsPerDay": 5000,
"monthlyRequests": 150000,
"promptCacheHitRate": "40%"
},
"monthlyCostComparisonUSD": {
"OpenAI - GPT-4o": "$1500.00/mo ($50.00/day)",
"OpenAI - GPT-4o mini": "$90.00/mo ($3.00/day)",
"OpenAI - o3-mini (Reasoning)": "$660.00/mo ($22.00/day)",
"Anthropic - Claude 3.7 Sonnet": "$1926.00/mo ($64.20/day)",
"Anthropic - Claude 3.5 Haiku": "$513.60/mo ($17.12/day)",
"Google - Gemini 2.5 Pro": "$1162.20/mo ($38.74/day)",
"Google - Gemini 2.0 Flash": "$57.00/mo ($1.90/day)"
}
}Place static instructions and JSON schemas at the very top of your prompt prefix to maximize automatic KV cache hits across requests.
Byte-Pair Encoding (BPE) Tokenization & Inference Cost Economics
Modern Large Language Models do not process raw UTF-8 strings character by character. Instead, text is segmented into numerical subword units using Byte-Pair Encoding (BPE) vocabularies such as o200k_base (200,000 tokens in GPT-4o) or SentencePiece. In standard English prose, 1 token averages approximately 4 characters or 0.75 words, whereas dense JSON keys, source code indentation, and multi-byte non-Latin scripts consume significantly more tokens per kilobyte.
Why Output Tokens Cost 3x to 5x More Than Input
Transformer inference operates in two distinct hardware phases: the Prefill phase processes all input prompt tokens in parallel via compute-bound matrix-matrix multiplications, whereas the Decode phase generates output tokens autoregressively one token at a time, bottlenecked by GPU HBM memory bandwidth loading the Key-Value (KV) cache on every step.
KV Prefix Caching & Batch API Discounts
When your system prompt, RAG context, or few-shot examples remain static at the beginning of a prompt (typically above 1,024 tokens), prefix caching reuses precomputed KV tensors across requests—reducing input token charges by 50% to 90%. Asynchronous 24-hour Batch APIs provide an additional flat 50% discount for non-interactive workloads.