Cheapest LLM for Summarization
Compare AI model costs for document summarization. Find the most cost-effective model for processing long documents at scale.
Inputs
Select a use case to pre-fill realistic token and request defaults, or choose Custom.
The model you are using in production today.
Average prompt size per API call — system prompt + context + user message. Typical: simple chat 500–2,000 · RAG chatbot 2,000–8,000 · document analysis 10,000–100,000 tokens.
Average response length. Short answer: 200–500 · paragraph: 500–1,000 · long-form: 1,000–4,000 tokens.
Total API calls per month. 1,000 DAU × 5 calls/day × 30 days = 150,000 requests/month.
Fraction of input tokens currently served from Prompt Cache. 0 = not using cache.
Decision Summary
Saves $2,748.78/yr (98.5%) with high engineering effort. See the ranked table below for all options.
Comparison
| Option | Monthly | Yearly |
|---|---|---|
| Best combo: GPT-5 Nano + cache + batchMaximum possible savings: optimal model, 60% cache, batch API enabled.cheapest | $3.44 | $41.22 |
| Switch to GPT-5 NanoRun a representative quality evaluation against the current model before migrating traffic. | $6.87 | $82.44 |
| Switch to GPT-5 MiniRun a representative quality evaluation against the current model before migrating traffic. | $34.35 | $412.20 |
| Cache (60%) + Batch APIMaximum savings without changing models. | $85.88 | $1,030.50 |
| Switch to Claude Haiku 4.5Run a representative quality evaluation against the current model before migrating traffic. | $101.40 | $1,216.80 |
| Enable Batch API (50% discount)Use async batch endpoint. Suitable for offline / non-realtime workloads. | $116.25 | $1,395.00 |
| Enable Prompt Cache (60% hit)Fix system prompt at context start and reuse across requests. | $171.75 | $2,061.00 |
| Current baselineGPT-5, cache 0%, batch offcurrent | $232.50 | $2,790.00 |
Pricing sources
Last verified 2026-08-03 · developers.openai.com/api/docs/models/compare developers.openai.com/api/docs/models/compare · platform.claude.com/docs/en/about-claude/pricing platform.claude.com/docs/en/about-claude/pricing · ai.google.dev/gemini-api/docs/pricing ai.google.dev/gemini-api/docs/pricing · api-docs.deepseek.com/quick_start/pricing api-docs.deepseek.com/quick_start/pricing
Trends & comparison
Trend
Comparison (monthly vs. yearly)
Summarization cost structure
Summarization is input-dominant: 6,000 input tokens (the document) vs 800 output tokens (the summary). This means input pricing matters most. Models like DeepSeek V4 Flash with $0.14/MTok input dramatically undercut GPT-5's $1.25/MTok. At 15,000 documents/month, the input cost alone ranges from $1.26 (DeepSeek) to $112.50 (GPT-5).
Batch processing for summarization
Most summarization workloads are not real-time — documents are processed in pipelines, overnight jobs, or on-upload queues. This makes them ideal for Batch API (50% discount). Combined with a cheap model, you can summarize 15,000 documents for under $5/month.
Frequently asked questions
Which LLM is cheapest for summarization?▾
GPT-5 Nano at $0.05/$0.40 per million tokens and DeepSeek V4 Flash at $0.14/$0.28 are the cheapest options. Summarization is input-heavy, so models with low input pricing win. Gemini Flash-Lite is also competitive.
How much does AI summarization cost at scale?▾
At 15,000 documents/month with 6,000 input + 800 output tokens each: GPT-5 costs ~$232/month, GPT-5 Mini ~$47, GPT-5 Nano ~$9. Batch API saves another 50% for non-realtime pipelines.
Can cheap models summarize well?▾
Yes. Summarization is one of the most model-tolerant tasks. Even small models produce acceptable summaries for most content. The main risk is missing key details in long documents — validate with a sample before full deployment.
Compare verified first-party API prices on the same standard workload → AI Model Price Leaderboard