A user documented their experience exhausting DeepSeek's 5 million free API tokens within 14 days, averaging about 357,000 tokens daily. They identified three key mistakes that led to this rapid consumption: using the more expensive 'deepseek-reasoner' model for non-reasoning tasks, failing to set a `max_tokens` limit on chat completions, and resending full document context in every Retrieval-Augmented Generation (RAG) call. By implementing habits like defaulting to the 'deepseek-chat' model for general tasks, capping response lengths, and optimizing RAG context, the user estimates the same token grant could have lasted a full month. AI
IMPACT Provides practical insights for developers on optimizing LLM API usage and managing token costs.
RANK_REASON User-generated content detailing API token usage and cost-saving strategies for a specific LLM.
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