Snowflake regularly updates its Cortex AI model portfolio to incorporate newer, more capable, and efficient models while deprecating older ones. In April 2026, several models are scheduled for deprecation, affecting users of Cortex AI Functions, Agents API, and Snowflake Intelligence.
This practical, detailed guide provides everything you need to prepare: affected models and timelines, reasons for deprecation, recommended replacements, potential impacts, testing strategies, and best practices to minimize disruption. Whether you’re a data engineer maintaining production agents or an AI leader overseeing enterprise workloads, this resource will help ensure a smooth transition.
Why Models Are Deprecated
Deprecations allow Snowflake to:
- Introduce higher-performing models with better reasoning, efficiency, or cost profiles.
- Standardize on architectures that support advanced agentic features.
- Optimize infrastructure for scalability and sustainability.
Proactive migration ensures continued performance, access to new capabilities, and avoidance of service interruptions.
Affected Models and Deprecation Dates
The following models are scheduled for deprecation in April 2026:
| Model | Deprecation Date | Affected Services |
|---|---|---|
| OpenAI GPT o4-mini | April 16, 2026 | AI_COMPLETE, Agents API, Snowflake Intelligence |
| Claude Sonnet 3.7 | April 28, 2026 | AI_COMPLETE, Agents API, Snowflake Intelligence |
| Snowflake Arctic | April 28, 2026 | AI_COMPLETE, Agents API, Snowflake Intelligence |
After these dates, calls to deprecated models will fail or be redirected (depending on configuration). Plan migrations well in advance.
Recommended Replacements
Snowflake provides clear upgrade paths to maintain or improve performance:
- OpenAI GPT o4-mini → Newer GPT series models (e.g., GPT-5 variants) or equivalent efficient options in Cortex.
- Claude Sonnet 3.7 → Latest Claude models or Anthropic options available in Cortex.
- Snowflake Arctic → Updated Snowflake-hosted models or partner frontier models with superior capabilities.
Test replacements thoroughly, as newer models often deliver better results on complex agentic tasks.
Impact on Existing Agents API and Snowflake Intelligence Workloads
Agents API
- Multi-step reasoning and tool-calling may require prompt or code adjustments for new models.
- Performance characteristics (latency, cost, context window) could change.
- Custom agent logic relying on specific model behaviors needs review.
Snowflake Intelligence
- Personal work agents and Skills features may exhibit different response styles or accuracy.
- Artifacts and workflow automation should be re-validated.
- User experience could improve with more capable models but requires prompt tuning.
Overall impact is typically low for simple queries but higher for sophisticated agentic workflows.
Testing Strategies for Minimal Disruption
Step-by-Step Testing Approach
- Inventory Usage — Query account usage views to identify all references to deprecated models.
- Parallel Testing — Run workloads with both old and new models side-by-side using Cortex features.
- Prompt Engineering — Refine prompts for optimal results with replacement models.
- Performance Benchmarking — Measure latency, accuracy, cost, and output quality.
- Agent-Specific Validation — Test full end-to-end agent behaviors, including tool calls and multi-step reasoning.
- User Acceptance Testing — Involve business users for Intelligence workloads.
Use Snowflake’s development and testing environments to avoid production risk.
Best Practices to Minimize Disruption
- Migrate Early — Start well before deprecation dates to allow thorough testing.
- Use Aliases or Abstractions — Where possible, abstract model selection in code for easier future updates.
- Monitor with Observability — Leverage Snowflake’s built-in monitoring for Cortex usage.
- Document Changes — Maintain records of model versions and prompt adjustments.
- Leverage Snowflake Support — Engage early for guidance on complex migrations.
- Budget for Testing — Allocate credits and time for validation.
Sample Migration SQL Snippet
SQL
-- Example: Switching to a replacement model
SELECT SNOWFLAKE.CORTEX.COMPLETE(
'new-replacement-model',
'Your prompt here...'
);Long-Term Outlook and Strategic Advice
Model deprecations are a normal part of a healthy AI platform lifecycle. They ensure access to cutting-edge capabilities while maintaining governance and performance. Organizations that build flexible, model-agnostic architectures will adapt most easily to future changes.
Treat this April 2026 wave as an opportunity to review and optimize your agentic AI workloads. Strong governance, thorough testing, and proactive migration will position your teams to capitalize on newer models’ advantages.
By staying current with Cortex updates, you ensure your AI initiatives remain powerful, cost-effective, and reliable.