Avoiding Reasoning Model Failures with Microsoft Foundry

This article details four distinct failure modes when deploying reasoning-tier models (o-series, GPT-5+) via Microsoft Foundry, even with the same endpoint and SDK. It covers API versioning mismatches between legacy and v1 routes, unsupported sampling parameters (temperature, top_p, etc.), reasoning tokens consuming the visible output budget leading to empty responses, and the need for conditional parameter handling. Practical fixes include checking API route compatibility, making sampling parameters conditional, and reserving extra token budget for reasoning. Targeted at developers and IT professionals working with Azure OpenAI.