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LLM API Cost Calculator

How much will your LLM API bill be?

Estimate and compare the monthly cost of running GPT, Claude, Gemini, Grok, DeepSeek, and Mistral for your workload — then see how much prompt compression could shave off. Prices are per 1M tokens, as of August 2026.

Start from a preset:
Cheapest option
$84.00/mo
Mistral Mistral Small 4
Most expensive here
$3,000/mo
Anthropic Claude Opus 5$2,916/mo more than the cheapest
Model$/1M in$/1M outCost / requestMonthly
Mistral Small 4Budget
Mistral
$0.15$0.600.168¢$84.00Details
DeepSeek V4 FlashBudget
DeepSeek
$0.22$0.660.229¢$114.40Details
GPT-5.6 LunaBudget
OpenAI
$0.20$1.200.256¢$128.00Details
Mistral Large 3Budget
Mistral
$0.50$1.500.520¢$260.00Details
DeepSeek V4 ProBudget
DeepSeek
$0.66$1.980.686¢$343.20Details
Gemini 3.7 FlashBalanced
Google
$0.75$3.750.900¢$450.00Details
Claude Haiku 4.5Budget
Anthropic
$1.00$5.00$0.01$600.00Details
GPT-5Frontier
OpenAI
$1.25$10.00$0.02$900.00Details
Grok 4.6Balanced
xAI
$2.00$6.00$0.02$1,040Details
Claude Sonnet 5Balanced
Anthropic
$2.00$10.00$0.02$1,200Details
GPT-5.6 TerraBalanced
OpenAI
$2.00$12.00$0.03$1,280Details
Gemini 3.1 ProFrontier
Google
$2.00$12.00$0.03$1,280Details
Claude Opus 5Frontier
Anthropic
$5.00$25.00$0.06$3,000Details

Estimates only. Prices are standard (non-cached, non-batch) per-1M-token rates as of August 2026 and change frequently — verify with each provider. Batch and cached-input discounts can cut these further.

Cut the bill with prompt compression

Most of an LLM bill is input tokens — long chat histories, retrieved docs, and tool traces. Prompt compression removes low-value context before the call. See what a reduction does to your input spend.

Before
$1,200/mo
After
$720.00/mo
You save
$5,760/yr

Picking between these models?

Compare them side by side on features, pricing, and real pros & cons.

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How the LLM cost calculator works

Every API call bills separately for the tokens you send (input) and the tokens the model generates (output), each priced per million tokens. This calculator multiplies your input and output tokens per request by each model's rate, then by your monthly request volume, so you can compare providers on the exact workload you run. A token is roughly 4 characters, or about ¾ of a word.

Why is my output so much more expensive than input?

Output tokens usually cost 4–8× more than input tokens across providers. If your app generates long answers, output dominates the bill; if it sends huge context (RAG, long chats), input dominates — which is where prompt compression helps most.

Are these prices current?

They reflect standard published rates as of August 2026. LLM pricing moves fast — providers cut prices and launch new tiers regularly — so always confirm on the provider's pricing page before budgeting. Batch APIs and cached-input reads can lower costs well below the figures shown here.