FrugalGPT

FrugalGPT cuts LLM costs via smart routing and fusion while maintaining or improving performance.

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FrugalGPT's Top Features

Cost reduction via strategic model selection and routing

Performance improvement through routing and fusion

Multi-model query routing across providers

Fusion strategies combining multiple model outputs

API compatibility with various LLM endpoints

Evaluation across diverse benchmarks and domains

Thought-level fusion using retrieved abstract templates

Model-level fusion via fine-tuning on top outputs

Supports zero-shot and few-shot prompting

Dynamic coordination to tailor routing and fusion for novel queries

Frequently asked questions about FrugalGPT

FrugalGPT is a research framework that reduces the cost of using large language models while maintaining or improving answer quality through routing and fusion strategies.

It routes each query to the most suitable (often cheaper) model and uses selective fusion of model outputs to balance cost with accuracy.

Yes. By combining routing with thought-level and model-level fusion, it can improve accuracy and robustness compared to single-model baselines.

No. It is an academic research approach described in the paper “FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance.”

The framework is API-agnostic and compatible with various LLM providers, enabling integration across different model endpoints.

It includes thought-level fusion—retrieving abstract templates from high-quality outputs—and model-level fusion—fine-tuning a base model using top-performing outputs.

Queries are routed to one or more models based on expected cost-performance tradeoffs, using similarity signals and learned strategies.

Fine-tuning is optional for model-level fusion; the framework also supports zero-shot and few-shot prompting without extra training.

It is evaluated across diverse domains and benchmarks to demonstrate general applicability and effectiveness.

Implement query routing and selective fusion across available LLM APIs, use few-shot examples when helpful, and evaluate cost-performance on your target tasks.