# Beyond GPT-5: Making LLMs Cheaper and Better via Performance-Efficiency Optimized Routing

arXiv: https://arxiv.org/abs/2508.12631 · HTML: https://mlsanity.com/paper/2508.12631/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 36 references that are in the map.
- First posted on arXiv 18 Aug 2025.

## Builds on (most cited first, 26 of 26)

- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
- [GPQA: A Graduate-Level Google-Proof Q&A Benchmark](https://mlsanity.com/paper/2311.12022/) (2023)
- [LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code](https://mlsanity.com/paper/2403.07974/) (2024)
- [Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models](https://mlsanity.com/paper/2506.05176/) (2025)
- [Large Language Models](https://mlsanity.com/paper/2307.05782/) (2023)
- [τ2-Bench: Evaluating Conversational Agents in a Dual-Control Environment](https://mlsanity.com/paper/2506.07982/) (2025)
- [LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion](https://mlsanity.com/paper/2306.02561/) (2023)
- [Humanity's Last Exam](https://mlsanity.com/paper/2501.14249/) (2025)
- [HealthBench: Evaluating Large Language Models Towards Improved Human Health](https://mlsanity.com/paper/2505.08775/) (2025)
- [Measuring short-form factuality in large language models](https://mlsanity.com/paper/2411.04368/) (2024)
- [Token-Budget-Aware LLM Reasoning](https://mlsanity.com/paper/2412.18547/) (2024)
- [RouterBench: A Benchmark for Multi-LLM Routing System](https://mlsanity.com/paper/2403.12031/) (2024)
- [Large Language Model Routing with Benchmark Datasets](https://mlsanity.com/paper/2309.15789/) (2023)
- [GraphRouter: A Graph-based Router for LLM Selections](https://mlsanity.com/paper/2410.03834/) (2024)
- [Routing to the Expert: Efficient Reward-guided Ensemble of Large Language Models](https://mlsanity.com/paper/2311.08692/) (2023)
- [C3oT: Generating Shorter Chain-of-Thought without Compromising Effectiveness](https://mlsanity.com/paper/2412.11664/) (2024)
- [ARC Prize 2024: Technical Report](https://mlsanity.com/paper/2412.04604/) (2024)
- [Multiagent Finetuning: Self Improvement with Diverse Reasoning Chains](https://mlsanity.com/paper/2501.05707/) (2025)
- [RouterDC: Query-Based Router by Dual Contrastive Learning for Assembling Large Language Models](https://mlsanity.com/paper/2409.19886/) (2024)
- [Learning to Decode Collaboratively with Multiple Language Models](https://mlsanity.com/paper/2403.03870/) (2024)
- [RouterEval: A Comprehensive Benchmark for Routing LLMs to Explore Model-level Scaling Up in LLMs](https://mlsanity.com/paper/2503.10657/) (2025)
- [Merge, Ensemble, and Cooperate! A Survey on Collaborative Strategies in the Era of Large Language Models](https://mlsanity.com/paper/2407.06089/) (2024)
- [The Avengers: A Simple Recipe for Uniting Smaller Language Models to Challenge Proprietary Giants](https://mlsanity.com/paper/2505.19797/) (2025)
- [Reasoning on a Budget: A Survey of Adaptive and Controllable Test-Time Compute in LLMs](https://mlsanity.com/paper/2507.02076/) (2025)
- [Decouple and Orthogonalize: A Data-Free Framework for LoRA Merging](https://mlsanity.com/paper/2505.15875/) (2025)

## Built on by (most cited first, 30 of 34)

- [LLMRouterBench: A Massive Benchmark and Unified Framework for LLM Routing](https://mlsanity.com/paper/2601.07206/) (2026)
- [Dynamic Model Routing and Cascading for Efficient LLM Inference: A Survey](https://mlsanity.com/paper/2603.04445/) (2026)
- [Intelligence per Watt: Measuring Intelligence Efficiency of Local AI](https://mlsanity.com/paper/2511.07885/) (2025)
- [Learning Compact Representations of LLM Abilities via Item Response Theory](https://mlsanity.com/paper/2510.00844/) (2025)
- [When Routing Collapses: On the Degenerate Convergence of LLM Routers](https://mlsanity.com/paper/2602.03478/) (2026)
- [VL-RouterBench: A Benchmark for Vision-Language Model Routing](https://mlsanity.com/paper/2512.23562/) (2025)
- [LLM Routing with Dueling Feedback](https://mlsanity.com/paper/2510.00841/) (2025)
- [V0: A Generalist Value Model for Any Policy at State Zero](https://mlsanity.com/paper/2602.03584/) (2026)
- [RADAR: Reasoning-Ability and Difficulty-Aware Routing for Reasoning LLMs](https://mlsanity.com/paper/2509.25426/) (2025)
- [On the Provable Performance Guarantee of Efficient Reasoning Models](https://mlsanity.com/paper/2510.09133/) (2025)
- [ProxRouter: Proximity-Weighted LLM Query Routing for Improved Robustness to Outliers](https://mlsanity.com/paper/2510.09852/) (2025)
- [Mid-Think: Training-Free Intermediate-Budget Reasoning via Token-Level Triggers](https://mlsanity.com/paper/2601.07036/) (2026)
- [Select-then-Solve: Paradigm Routing as Inference-Time Optimization for LLM Agents](https://mlsanity.com/paper/2604.06753/) (2026)
- [Route to Rome Attack: Directing LLM Routers to Expensive Models via Adversarial Suffix Optimization](https://mlsanity.com/paper/2604.15022/) (2026)
- [Language Models and Logic Programs for Trustworthy Tax Reasoning](https://mlsanity.com/paper/2508.21051/) (2025)
- [Opportunity Is Not Realizability: Selection-Valid Diagnostics for Multi-LLM Routing](https://mlsanity.com/paper/2608.08265/) (2026)
- [Towards Generalized Routing: Model and Agent Orchestration for Adaptive and Efficient Inference](https://mlsanity.com/paper/2509.07571/) (2025)
- [Federate the Router: Learning Language Model Routers with Sparse and Decentralized Evaluations](https://mlsanity.com/paper/2601.22318/) (2026)
- [From GPT-3 to GPT-5: Mapping their capabilities, scope, limitations, and consequences](https://mlsanity.com/paper/2604.10332/) (2026)
- [From Sampled Outcomes to Capability Distributions: Rethinking Supervision for LLM Routing](https://mlsanity.com/paper/2606.06924/) (2026)
- [HyDRA: Hybrid Dynamic Routing Architecture for Heterogeneous LLM Pools](https://mlsanity.com/paper/2605.17106/) (2026)
- [The Routing Plateau: Understanding the Accuracy Limits of LLM Routers](https://mlsanity.com/paper/2606.07587/) (2026)
- [RouteJudge: An Open Platform for Reproducible and Preference-Aware LLM Routing](https://mlsanity.com/paper/2606.18774/) (2026)
- [RSI-Router: Evolving Subtask-Level LLM Routing and Skills for Cost-Efficient Agents](https://mlsanity.com/paper/2609.34712/) (2026)
- [Dynamic LLM Routers are Often Misguided](https://mlsanity.com/paper/2610.02762/) (2026)
- [The ORCA Benchmark: Evaluating Real-World Calculation Accuracy in Large Language Models](https://mlsanity.com/paper/2511.02589/) (2025)
- [Learning to Route LLMs from Implicit Cost-Performance Preferences via Meta-Learning](https://mlsanity.com/paper/2606.06178/) (2026)
- [RLCascadeRouter: Quality-Estimator-Free Cascade Routing via Reinforcement Learning](https://mlsanity.com/paper/2608.15817/) (2026)
- [AIBuildAI-2.5: Efficient Autonomous AI Model Development Through LLM-Guided Tree Search](https://mlsanity.com/paper/2609.25047/) (2026)
- [HM-ROUTER: Joint Model and Harness Routing for Agentic Systems](https://mlsanity.com/paper/2609.32213/) (2026)

## Abstract (arXiv)

Balancing performance and efficiency is a central challenge in large language model (LLM) advancement. GPT-5 addresses this with test-time routing, dynamically assigning queries to either an efficient or a high-capacity model during inference. In this work, we present Avengers-Pro, a test-time routing framework that ensembles LLMs of varying capacities and efficiencies, providing a unified solution for all performance-efficiency tradeoffs. The Avengers-Pro embeds and clusters incoming queries, then routes each to the most suitable model based on a performance-efficiency score. Across 6 challenging benchmarks and 8 leading models -- including GPT-5-medium, Gemini-2.5-pro, and Claude-opus-4.1 -- Avengers-Pro achieves state-of-the-art results: by varying a performance-efficiency trade-off parameter, it can surpass the strongest single model (GPT-5-medium) by +7% in average accuracy. Moreover, it can match the average accuracy of the strongest single model at 27% lower cost, and reach ~90% of that performance at 63% lower cost. Last but not least, it achieves a Pareto frontier, consistently yielding the highest accuracy for any given cost, and the lowest cost for any given accuracy, among all single models. Code is available at https://github.com/ZhangYiqun018/AvengersPro.