# Suboptimal Azure OpenAI model type | Cloud Efficiency Hub Canonical: https://www.pointfive.co/efficiency-hub/inefficiencies/suboptimal-azure-openai-model-type-c8913 Azure releases newer OpenAI models that provide better performance and cost characteristics compared to older generations. By: Ariel Fishman-Lichterman Updated: 2026-09-28 [Cloud Efficiency Hub](https://www.pointfive.co/efficiency-hub) # Suboptimal Azure OpenAI Model Type The short version Azure releases newer OpenAI models that provide better performance and cost characteristics compared to older generations. [Ariel Fishman-Lichterman](https://www.linkedin.com/in/ariel-fishman-lichterman-01a096214/) Cloud Researcher @ PointFive Azure service [Azure Cognitive Services](https://www.pointfive.co/efficiency-hub/cloud-services/azure-cognitive-services) Category [AI](https://www.pointfive.co/efficiency-hub/service-category/ai) Reference CER-0254 Type Outdated Version ## Explanation Why the waste happens and who it affects. When workloads remain on outdated model versions, they may consume more tokens to produce equivalent output, run slower, or miss out on quality improvements. Because customers pay per token, using an older model can lead to unnecessary spending and reduced value. Aligning deployments to the most current, efficient model types helps reduce spend and improve application performance. ## Billing model The pricing dimensions that drive this cost. On-demand Azure OpenAI deployments are billed per input and output token. Newer models often offer lower cost per processed token, higher throughput, and reduced latency, but not always a lower bill: reasoning models generate reasoning tokens that are billed as output tokens, so a newer model can cost more per completed task even at a lower per-token price. Compare cost per completed task, along with quality and latency, on your own workload before and after migrating. ## How to detect 4 checks to find it in your estate. - Review Azure OpenAI deployments to identify workloads using older or deprecated model versions - Assess token consumption patterns to determine whether newer models could achieve the same results more efficiently - Evaluate latency or performance issues that may be linked to older model behavior - Check Azure's model lifecycle and release notes to confirm whether a newer recommended model family exists ## How to fix 4 ways to remove the waste. - Migrate workloads to the latest suitable Azure OpenAI model that provides improved efficiency and performance - Establish a periodic review process to ensure deployed models are aligned with current Azure model offerings - Incorporate model lifecycle awareness into architecture standards so workloads are upgraded as new versions become available - Validate compatibility and output quality after migration to ensure a smooth transition to newer models ## Documentation Vendor references for pricing and configuration. - [https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models learn.microsoft.com](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models) - [Foundry Models lifecycle and support policy learn.microsoft.com](https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-retirements) - [Azure OpenAI reasoning models learn.microsoft.com](https://learn.microsoft.com/en-us/azure/foundry/openai/how-to/reasoning) ## Related inefficiencies [Browse the library](https://www.pointfive.co/efficiency-hub) - Azure Cognitive Services CER-0238 ### [Always-On PTUs for Seasonal or Cyclical Azure OpenAI Workloads](https://www.pointfive.co/efficiency-hub/inefficiencies/always-on-ptus-for-seasonal-or-cyclical-azure-openai-workloads-ccb97) Many Azure OpenAI workloads - such as reporting pipelines, marketing workflows, batch inference jobs, or time-bound customer interactions-only run during specific periods. When PTUs remain fully provisioned 24/7, organizations incur... AI - Azure Cognitive Services CER-0248 ### [Non-Production Azure OpenAI Deployments Using PTUs Instead of PAYG](https://www.pointfive.co/efficiency-hub/inefficiencies/non-production-azure-openai-deployments-using-ptus-instead-of-payg-9fe81) Development, testing, QA, and sandbox environments rarely have the steady, predictable traffic patterns needed to justify PTU deployments. These workloads often run intermittently, with lower throughput and shorter usage windows. When PTUs... AI - Azure Cognitive Services CER-0266 ### [Underutilized PTU Quota for Azure OpenAI Deployments](https://www.pointfive.co/efficiency-hub/inefficiencies/underutilized-ptu-quota-for-azure-openai-deployments-da831) When organizations size PTU capacity based on peak expectations or early traffic projections, they often end up with more throughput than regularly required. If real-world usage plateaus below provisioned levels, a portion of the PTU... AI --- Source: the public page above. Product screenshots and illustrative interfaces are examples, not live customer data.