A user describes a workflow optimization strategy for using large language models, particularly Anthropic's Claude. Initially, they relied heavily on Claude Opus for research, planning, and coding, but found the costs prohibitive. They then experimented with using Claude Sonnet for execution and Opus for review, which was more cost-effective. The current setup involves using Opus for high-level planning and review, while offloading coding tasks to cheaper models like DeepSeek, GLM, and Kimi, significantly reducing token expenditure. AI
IMPACT Users are developing cost-saving strategies by segmenting tasks between premium and budget LLMs, indicating a growing focus on operational efficiency.
RANK_REASON User describes a personal workflow and cost optimization strategy for using LLMs, not a new product release or industry-wide event.
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