Task-to-Model Optimization for Enterprise LLM Coding Assistants: A Data-Driven Framework for Cost-Optimal Routing
cs.LG
Submitted: 2026-08-09
Updated: 2026-08-31
Comments: 11 pages, 1 figure, 2 tables
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Clio: Privacy-Preserving Insights into Real-World AI Use
- FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance
- RouteLLM: Learning to Route LLMs with Preference Data
- Hybrid LLM: Cost-Efficient and Quality-Aware Query Routing
- AutoMix: Automatically Mixing Language Models
- Large Language Model Routing with Benchmark Datasets
- SWE-bench: Can Language Models Resolve Real-World GitHub Issues?
- Evaluating Large Language Models Trained on Code
- Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena
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