Beyond the price per token: Choosing the right OpenAI model on Amazon Bedrock for your workload
Stop optimizing for token price. Here's how to measure what your AI workload actually costs per correct answer.
Why it matters
Amazon Bedrock published a practical benchmarking framework that shifts the cost-per-token conversation to outcome-based economics — cost per correct answer, agent trajectory cost, and deliverable quality. Practitioners choosing between OpenAI models now have an open-source tool to measure real production ROI, not just API pricing.
The key facts
9 to knowAmazon Bedrock blog post on OpenAI model selection
Open-source benchmarking harness included
Metrics: cost per correct answer, agent trajectory cost, rubric-graded quality
Compares OpenAI models across cost and outcome dimensions
Published Sept 11, 2026
Open-source benchmarking harness for OpenAI models on Amazon Bedrock
Compares multiple OpenAI model tiers
Focus on production workload outcomes, not token pricing
Published by AWS ML blog (vendor content)
Go to the source
AWS Machine Learning Blogaws.amazon.com
Publisher excerpt: Comparing models on dollars per million tokens misses what production workloads actually pay for: outcomes. This post shares an open-source benchmarking harness that measures cost per correct answer, agent trajectory cost, and rubric-graded deliverable quality across OpenAI models on Amazon Bedrock.