KERNOLIX
2026 BENCHMARK & PRICING MATRIX

AI Token Cost & LLM Calculator

Simulate exact monthly API expenses across state-of-the-art foundation models with input/output token split and prompt caching discounts.

0 M
Total Monthly Tokens
0 M
Input Tokens (Prompt + Context)
0 M
Output Tokens (Completion)
$0
Est. Prompt Cache Savings
Workflow Parameters
50,000
Number of API calls or agent loop turns per month
1,500
System prompt + retrieved documents + chat history
500
Generated response, structured JSON, or tool call plan
30%
Portion of input context eligible for prompt caching
Live Model Pricing Comparison

Eliminate Runaway LLM Costs with Kernolix

Kernolix Operator orchestrates local tool execution, permission gates, and token budgets so your production agents never overrun expected operational expenses.

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Frequently Asked Questions

How are LLM tokens calculated in 2026?

1 token roughly equals 0.75 English words (or ~4 characters). A typical 1,000-word prompt translates into approximately 1,333 tokens. Input tokens and output tokens are priced separately by providers because generation requires autoregressive sampling which consumes considerably more GPU compute.

What is prompt caching and how does it save money?

Providers like Anthropic, OpenAI, and Google support caching static parts of your prompt (e.g., long system instructions, documentation, tool definitions). When cached, input tokens are discounted by 50% to 90%, massively reducing costs for repetitive agent loops.

Which model offers the best balance for enterprise automation?

For complex business reasoning and multi-step tool calls, Claude 3.5 Sonnet and GPT-4o deliver state-of-the-art accuracy. For high-volume classification, extraction, and summary tasks, GPT-4o-mini and Gemini 1.5 Flash offer unmatched cost efficiency.