How to use Earnings-Call Summarization Cost Calculator
Per-stock per-quarter LLM cost to summarize earnings transcripts across Sonnet, Opus, GPT-4o, and Gemini 2.5 Pro/Flash, with cache-hit-rate awareness and snapshot pricing so you can plan a coverage universe budget.
What It Does
Use the calculator with intent
Per-stock per-quarter LLM cost to summarize earnings transcripts across Sonnet, Opus, GPT-4o, and Gemini 2.5 Pro/Flash, with cache-hit-rate awareness and snapshot pricing so you can plan a coverage universe budget.
Engineers and PMs scoping an earnings-coverage product who need a realistic per-name budget across the model landscape before committing.
Interpreting Results
Per-stock per-quarter cost x universe size = the workload. Cache-hit rate is the key dial: 70%+ cache hit (boilerplate sections repeat) cuts cost ~3x.
Input Steps
Field by field
- 1
Enter inputs
Enter average call duration in minutes (typical: 60), or upload sample transcripts to measure actual token counts.
- 2
Pick option
Pick model: Sonnet for budget efficiency, Opus for highest extraction accuracy, GPT-4 for tool-use chains.
- 3
Set parameters
Set summary output length (typical: 500-1500 tokens for a structured summary).
- 4
Read outputs
Read per-call cost and per-quarter cost (multiplied by your call volume). Compare across models.
- 5
Toggle setting
Toggle prompt caching if you have a stable extraction schema. 90% discount on cached tokens often shifts the cost ranking between models.
Common Scenarios
Use realistic starting points
Small universe, high-quality summary
Universe
S&P 100
Model
Opus
Per-quarter cost manageable for 100 names; quality suits an in-depth product. Watch for response-token inflation.
Large universe, light summary
Universe
Russell 3000
Model
Sonnet
Per-quarter cost steep without batch + caching; toggling both brings it into a defensible range.
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FAQ
Questions people ask next
The short answers readers usually want after the first pass.
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