{"version":1,"type":"story","url":"https://digestai.news/story/comparing-openai-models-on-aws-for-workload-efficiency","json":"https://digestai.news/story/comparing-openai-models-on-aws-for-workload-efficiency.json","markdown":"https://digestai.news/story/comparing-openai-models-on-aws-for-workload-efficiency.md","slug":"comparing-openai-models-on-aws-for-workload-efficiency","headline":"Comparing OpenAI Models on AWS for Workload Efficiency","summary":"Organizations often compare models based solely on the price per million tokens, but this overlooks critical factors like how often a model is correct and the number of tokens needed. This post uses an open-source benchmark to measure these multipliers across five models: gpt-5.6-luna, gpt-5.6-terra, gpt-5.6-sol from Amazon Bedrock, and two OpenAI API baselines (gpt-5.4-mini and gpt-5.4-nano). The study focuses on three key questions: the cost of a correct answer, the cost per agent trajectory, and whether models can produce acceptable work. Results show that while luna is cheaper per token, it often outperforms mini in terms of accuracy and cost per outcome. The most efficient model for professional deliverables was gpt-5.6-luna, achieving higher rubric scores and lower costs compared to the other models.","keyPoints":["luna from Amazon Bedrock is cheaper per token than OpenAI API baselines","gpt-5.6-luna outperforms other models in terms of accuracy and cost per outcome","gpt-5.6-luna achieves higher rubric scores for professional deliverables"],"whyItMatters":"Understanding the true costs and efficiencies of different AI models is crucial for organizations deploying generative AI applications, as it can significantly impact their operational costs and outcomes.","category":{"slug":"models","name":"Generative AI & Models","url":"https://digestai.news/category/models"},"entities":{"companies":["AWS","OpenAI"],"models":["gpt-5.6-luna","gpt-5.6-terra","gpt-5.4-mini","gpt-5.4-nano"],"people":[]},"firstPublishedAt":"2026-09-11T18:24:38Z","updatedAt":"2026-09-11T18:24:38Z","sourceCount":1,"hasPrimarySource":true,"sources":[{"outlet":"AWS Machine Learning Blog","title":"Beyond the price per token: Choosing the right OpenAI model on Amazon Bedrock for your workload","url":"https://aws.amazon.com/blogs/machine-learning/beyond-the-price-per-token-choosing-the-right-openai-model-on-amazon-bedrock-for-your-workload","publishedAt":"2026-09-11T18:24:38Z","type":"primary","primary":true,"lead":true}],"sourceNotes":null,"discussions":[],"thread":{"title":"OpenAI's GPT-6 Expansion and Pricing Overhaul","url":"https://digestai.news/thread/comparing-openai-models-on-aws-for-workload-efficiency","storyCount":10},"cite":{"text":"Digest AI, \"Comparing OpenAI Models on AWS for Workload Efficiency\", 11 September 2026, https://digestai.news/story/comparing-openai-models-on-aws-for-workload-efficiency","publisher":"Digest AI","title":"Comparing OpenAI Models on AWS for Workload Efficiency","datePublished":"2026-09-11T18:24:38Z","url":"https://digestai.news/story/comparing-openai-models-on-aws-for-workload-efficiency"},"generatedBy":"Written by Digest AI's editorial model from the linked sources; the sources are the record.","license":"Headlines, digests and key points are written by Digest AI and may be quoted with a link to the story page. Linked articles belong to their publishers. Terms: https://digestai.news/terms#reuse"}