@pvncher: While you’re absolutely correct that these routers don’t make sense, I’ve fully soured on running benchmarks that just …
Summary
The author discusses the introduction of a cache-aware model router by OpenRouter, which optimizes model selection for quality, speed, and cost, while criticizing benchmarks that evaluate AI tools in isolation rather than real-world scenarios.
View Cached Full Text
Cached at: 09/28/26, 03:29 AM
@theo While you’re absolutely correct that these routers don’t make sense, I’ve fully soured on running benchmarks that just test a bunch of tiny tasks in isolation to evaluate ideas like this.
In the real world users run prompts that can take an hour+ to run. The model has to
OpenRouter@OpenRouter·Sep 26: Introducing typesafe/jev-router: a cache-aware model router powered by Jev and @typesafeai
The Jev Router picks the best model and reasoning effort for each request, balancing quality, speed, and cost.
Here’s how it works 👇🏻
Similar Articles
@mckaywrigley: bullish model routers. same perf at lower cost is obvious. but there are massive gains to be had by creating "smoother"…
A tweet highlights the promise of model routers and 'model melding', referencing the launch of Not Diamond Code, an intelligent model router for coding agents that cuts costs by 20-65%.
Benchmarks compare open models against closed products, not closed models. We might be missing what were actually paying for
Argues that benchmarks comparing open models against closed API products are misleading because they measure raw inference vs. hidden tooling and preprocessing, suggesting the actual model quality gap may be smaller than reported.
(Rant ;)) Make your benchmarks realistic
A community rant urging realistic AI model benchmarks that account for context size, multimodal features, hardware specifics, and parallel processing, rather than just raw speed.
@mattshumer_: Every time a VC sends me an auto-router company to diligence, I send something like this back. If caching wasn't a thin…
The post critiques auto-routing companies in AI, emphasizing that frequent model switching invalidates prompt caches, resulting in higher token costs and inefficiencies.
Why are MoE models so belittled?
Discusses the common perception that MoE models with low active parameters are inferior to dense models, arguing that router effectiveness and architecture nuances matter.