ToolsSeptember 11, 2026via AWS Machine Learning Blog
Beyond the price per token: Choosing the right OpenAI model on Amazon Bedrock for your workload
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.
Key signals
- Amazon 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)
The hook
Stop optimizing for token price. Here's how to measure what your AI workload actually costs per correct answer.
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.