Modelsv1.0.0

Agencies building custom applications can use the orchestration pattern: frontier models for architecture planning, mid-tier models for implementation, and cheap models for parallel subtasks. This reduces costs while maintaining quality, especially for code review and debugging phases.

Firms handling complex data analysis and report generation benefit from using frontier models for extended reasoning on hard math problems and cheaper models for high-volume simple queries. Batch APIs and prompt caching help manage costs for long documents and non-real-time workloads.

Platforms generating articles or marketing materials can use mid-tier models for general assistance and cheap models for bulk content, avoiding overpayment. They should track actual costs per workflow and reassess model choices quarterly to adapt to shifting capabilities.

Organizations processing patient records or research documents can leverage large-context models to handle long documents without chunking, reducing costs. Open-source models ensure data privacy and cost predictability for custom fine-tuning on sensitive datasets.

Offer basic features with cheap models for high-volume simple queries, then charge for premium tiers using mid-tier or frontier models for complex tasks like coding or reasoning. This aligns costs with user needs and encourages upgrades based on task complexity.

Provide services that design AI pipelines using the orchestration pattern, recommending specific models for planning, execution, and review phases. Revenue comes from reduced client costs and improved accuracy, with ongoing reassessment to adapt to model updates.

Build a platform that uses batch/async APIs for non-real-time workloads, passing 50% discounts to users. Focus on industries like finance or media where long documents and high-volume queries benefit from cost-aware model selection and prompt caching.