Is anyone actually solving per-prompt model routing well yet, or are we all just eyeballing it?
Summary
The article explores the challenge of per-prompt model routing in AI agents, questioning whether anyone has effectively solved it. It points out that current practices rely on gut feeling, flat-rate plans reduce pressure to optimize, and a triage layer may introduce its own costs.
Similar Articles
@tomas_hk: yes it is have written our learnings here:
A comprehensive guide explaining model routing as a technique to intelligently select the best AI model per request to optimize cost, quality, and latency, contrasting it with AI gateways and emphasizing its importance for agentic AI workloads.
Would you rather tune one model’s reasoning depth or route across two models?
A reflection on the trade-offs between using a single trillion-parameter reasoning model with adjustable depth (like Ring-2.6-1T) versus routing between separate specialized models, exploring which approach is cleaner or more cost-effective for agent workflows.
No One Fits All: From Fixed Prompting to Learned Routing in Multilingual LLMs
Researchers from National Taiwan University propose replacing fixed translation-based prompting strategies in multilingual LLMs with lightweight learned classifiers that route each instance to either native or translation-based prompting. Their analysis across 10 languages and 4 benchmarks shows no single strategy is universally optimal, with translation benefiting low-resource languages most, and the learned routing achieving statistically significant improvements over fixed strategies.
Running a 24/7 AI agent dev team: I route each role to a different LLM (Claude/Kimi/MiniMax/GPT) to dodge a ~$2k/mo API bill. Setup + what actually breaks.
The author describes a setup where different AI models are assigned to specific roles (planning, coding, review) to reduce API costs for a 24/7 autonomous engineering team, and shares common failure points like model wandering and hallucinated ownership.
Can prompting reduce AI sycophancy or is it mostly model behavior?
A user explores whether prompt engineering can reduce AI sycophancy in models like Gemini, ChatGPT, and Claude, or whether it's fundamentally a model alignment issue. The discussion touches on differences between models in handling disagreement and objective criticism.