Cost to Answer 10,000 Document Questions with LLM APIs
Calculate the cost of using LLM APIs for document question answering. Compare all models for processing 10,000 questions with verified per-million-token pricing.
Prices verified as of Aug 9, 2026 · 178 models tracked
Your workload
Token breakdown — 4K in / 300 out per question
Total tokens: —
What it costs
Cheapest
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Average
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Most expensive
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MODELPRICEWATCH.COM · 2026-08-09
Cost per questions across 178 models
cheapest 15 as bars · full table belowLoading…
Show all 178 models in a table
| Model | Provider | Input $/1M | Output $/1M | Cost for 10K questions |
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How this calculator works
Each document Q&A interaction requires ~4,000 input tokens (retrieved document chunks + question) and ~300 output tokens (the answer). This assumes a RAG pipeline that retrieves relevant context per question. Input tokens dominate because the model needs to read document context before answering.
Formula: cost = (input_tokens × input_price_per_Mtok + output_tokens × output_price_per_Mtok) × quantity / 1,000,000
All prices are per million tokens, sourced directly from official provider pricing pages. No fabricated numbers — every model in the table links to its detail page, which cites the official pricing source.
Frequently asked questions
How much does it cost to answer 10,000 document questions with an LLM?
It depends on the model. RAG Q&A is input-dominated (~4,000 input / ~300 output tokens per question) because each question carries retrieved context. The calculator above computes the exact current cost for every model, cheapest-first, from live per-million-token pricing — see the table above for exact per-model cost at your question volume.
Which LLM API is cheapest for document Q&A?
Models with cheap input pricing are best for RAG, and the calculator sorts them cheapest-first at the top of the table. If your retrieved context repeats across questions, models that support prompt caching can cut cached-input costs substantially.
How are document Q&A token costs calculated?
Each question uses ~4,000 input tokens (retrieved context + question) and ~300 output tokens (answer). Total cost = (input_tokens × input_price + output_tokens × output_price) × number_of_questions. Prices are per million tokens from verified provider pricing.