Bidirectional transformers excel at sentiment analysis, and Large Language Models (LLM) are effective zero-shot learners.Might they perform better as a team?This paper explores collaborative approaches between ELECTRA and GPT-4o for three-way sentiment classification.We fine-tuned (FT) four models (ELECTRA Base/Large, GPT-4o/4o-mini) using a mix of reviews from Stanford Sentiment Treebank (SST) and DynaSent.We provided input from ELEC-TRA to GPT as: predicted label, probabilities, and retrieved examples.Sharing ELECTRA Base FT predictions with GPT-4o-mini significantly improved performance over either model alone (82.50 macro F1 vs. 79.14ELECTRA Base FT, 79.41 GPT-4o-mini) and yielded the lowest cost/performance ratio ($0.12/F1 point).However, when GPT models were fine-tuned, including predictions decreased performance.GPT-4o FT-M was the top performer (86.99), with GPT-4o-mini FT close behind (86.70) at much less cost ($0.38 vs. $1.59/F1 point).Our results show that augmenting prompts with predictions from fine-tuned encoders is an efficient way to boost performance, and a fine-tuned GPT-4o-mini is nearly as good as GPT-4o FT at 76% less cost.Both are affordable options for projects with limited resources.