Unverified50% confidenceFactExact time
Model version iteration and policy drift are inherent features of commercial AI services; providers regularly update safety policies and adjust refusal thresholds, and users typically cannot lock in old versions.
1
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50%
Confidence
Long-term
Relevance
11/3/2026
First Seen
Sources
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Related Claims
UnverifiedA fixed-version deployment is a deployment method in which the user explicitly locks a specific model version number to ensure stable output behavior; after the model is retired it does not receive automatic migration, and call requests will directly return a model-unavailable error.73% similarUnverifiedSome Azure OpenAI deployments are configured with auto-upgrade rules that automatically route traffic to the recommended replacement model after a model is retired; fixed-version deployments are not migrated automatically, and calls will fail directly after retirement.73% similarUnverifiedServer-side changes refer to a model provider updating model weights, inference configurations, safety policies, or system prompts in the background, while the user interface and API version numbers may remain completely unchanged.72% similarUnverifiedRegularly updating the model version in use is itself a necessary maintenance strategy.72% similar
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