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Summarization

Compare cost for long inputs and short outputs. This is useful for reports, articles, notes, and other text-heavy work.

What matters most

Summarization is usually input-heavy. Low input price matters a lot, while output price matters less if the summary stays short.

Base example

This page starts with a 10,000-token document, a 500-token summary, and 1,000 documents per month.

Low-cost summarization models in the base example

Model Input Output Monthly cost
Qwen2.5-Coder-3B-Instruct $0.0100 / 1M tokens $0.0300 / 1M tokens $0.12
Qwen2.5-Coder-7B-Instruct $0.0100 / 1M tokens $0.0300 / 1M tokens $0.12
Qwen2.5-Coder-7B $0.0100 / 1M tokens $0.0300 / 1M tokens $0.12
llama3.2-11b-vision-instruct $0.0150 / 1M tokens $0.0250 / 1M tokens $0.16
llama3.2-3b-instruct $0.0150 / 1M tokens $0.0250 / 1M tokens $0.16
gpt-oss-20b $0.0145 / 1M tokens $0.0700 / 1M tokens $0.18
titan-embed-text-v2 $0.0200 / 1M tokens N/A $0.20
Llama-3.2-3B-Instruct $0.0200 / 1M tokens $0.0200 / 1M tokens $0.21

Open calculator to estimate your own workload cost with different token counts and model choices.

Browse providers to compare models from OpenAI, Anthropic, Google, Mistral, and other providers.

Pricing data from catalog last generated Aug 10, 2026. Verify before production decisions. Data sources.