Why is LLM API pricing not a simple multiplication?

Because input and output tokens are priced differently, and your workload profile decides the ratio: summarization is ~8:1 input-to-output, code generation is 1:2, chat is 1:0.4. The same 10K daily requests can cost 100× more on one provider than another depending on the profile. This calculator compares 12 models across 6 providers with caching and batch discounts applied.

What is prompt caching and how much does it save?

Prompt caching reuses the token computation for repeated prompt prefixes at a heavily discounted rate — often a fraction of the input price. For workloads with stable system prompts (chat assistants, RAG), caching is the most underutilized discount in the LLM bill. The calculator models cache hit rates so the savings show up in the comparison.

Why do tokenizers change the effective price?

Tokenizers split text differently per model — 1,000 characters is not 1,000 tokens, and the same English text can tokenize to different counts across providers. Combined with input/output ratio differences, the "same" prompt costs different amounts. The calculator accounts for tokenizer variance so your comparison is apples-to-apples.

LLM API Pricing Calculator — Compare OpenAI, Anthropic, Google, DeepSeek Costs

📋 Workload Configuration

Select a workload profile to auto-fill typical token ratios, or configure custom values. Token counts are approximate — different providers use different tokenizers, so the same text produces different token counts. The calculator uses your numbers uniformly; see the methodology note below.

Workload Profile

Daily Requests

Avg Input Tokens / Request

Avg Output Tokens / Request

Prompt Cache Hit Rate: 0%

% of input tokens served from cache. Anthropic gives 90% discount, OpenAI 50%.

Batch Processing: 50% off for async (24h SLA).

Why LLM API Pricing Isn't a Simple Multiplication

The pricing page says $2.50 per million input tokens. Your mental math: "10,000 requests × 2,000 tokens × $2.50/M = $50/day." Then the bill arrives and it's $180/day. Four structural reasons the napkin math fails every time.

1. Input ≠ Output Token Counts

Most pricing pages quote input and output prices separately — but your workload determines the ratio. A chat application with system prompts and conversation history averages roughly 1:0.4 input-to-output tokens (2.5× more input than output). A summarization pipeline averages 8:1 (long document in, short summary out). A code generation agent averages 1:2 (short prompt, longer code block out). Since output tokens cost 3–4× more than input tokens at most providers, the workload ratio is a cost multiplier. A summarization workload at 8:1 pays mostly input prices; a code generation workload at 1:2 pays mostly output prices. Same daily request count, same provider — 3× cost difference between profiles.

2. Prompt Caching: The Most Underutilized Discount

Anthropic gives a 90% discount on cached input tokens. OpenAI gives 50% off. Google's context caching is free for prompts under 128K tokens with a small storage fee. If your application sends the same system prompt with every request — and most do — that's 500–2,000 tokens per request that qualify for caching. At a 90% discount on Anthropic, a 2,000-token system prompt at 1M requests/month saves $27,000/month on Claude Opus 4 alone. The discount exists. Most teams never configure it.

3. Batch Processing: Half Price for Patience

Both OpenAI and Anthropic offer 50% off for batch (asynchronous) processing with a 24-hour completion SLA. For offline workloads — evaluation runs, dataset labeling, embedding generation, nightly summarization jobs — batch pricing cuts the bill in half with no downsides. Combined with prompt caching on Anthropic, the effective cost for batched, cached inference can be 5–10% of the sticker price.

4. Tokenizer Variance: 1,000 Tokens ≠ 1,000 Tokens

OpenAI, Anthropic, Google, and DeepSeek each use different tokenizers. The same English paragraph might be 800 tokens on OpenAI's cl100k_base tokenizer, 750 on Anthropic's tokenizer, and 880 on Google's SentencePiece tokenizer. Across 1 million requests per day, a 10% tokenizer efficiency difference translates to thousands of dollars per month in cost variance — before you even compare the per-token prices. The calculator uses uniform token counts for comparability; in practice, benchmark your actual text across providers before committing.

How to Use This Calculator

Pick your workload profile — this sets realistic input/output token ratios.

Set your daily request volume and adjust token counts if needed.

Slide the prompt cache hit rate based on what percentage of your requests share a common prefix (system prompts, few-shot examples, RAG context).

Toggle batch processing for offline/non-realtime workloads.

Compare the "Annual Cost" column (no optimizations) with "Optimized Annual" (cache + batch) — the gap is savings you're leaving on the table.