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.
⚡ Your Workload
📊 Cost Summary
Cost per questions across 153 models
Show all 153 models in a table
| Model | Provider | Input $/M | Output $/M | 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 and verified by our automated scraper pipeline that runs 3× daily. No fabricated numbers — every price links to its source.
Frequently asked questions
How much does it cost to answer 10,000 document questions with an LLM?
Answering 10,000 document questions via RAG costs $5-15 with budget models, $30-60 with mid-tier models, and $150-400+ with frontier models. The dominant cost is input tokens, since each question requires reading document context (~4,000 tokens).
Which LLM API is cheapest for document Q&A?
For RAG-based document Q&A, models with cheap input pricing are best. DeepSeek V3, Gemini Flash, and GPT-4.1 Mini offer the lowest cost per question. If your documents are cached, models with prompt caching (like Claude) can reduce input costs by 50-90%.
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.