arXiv (All)
open access
Learning the Cost of Reliable Inference
Benchmarking and routing platforms increasingly act as intermediaries connecting large language model providers with end-users. However, providers on these platforms typically use a fixed price per token, preventing users from achieving the most competitive price for their tasks. % workloads. In thi
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Large Language Models as Statistical Decision-Makers at Inference Time#634803
- Characterization of Request and Token Energy Costs for LLM Inference Workloads on GPU Platforms#784812
- Characterization of Request and Token Energy Costs for LLM Inference Workloads on GPU Platforms#783222
- Post-Reasoning: Improving the Performance of Non-Thinking Models at No Cost#1084202
- How Much Can Language Models Gain from Test-Time Computation?#1156045
Record · ID 1064925
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive