Unify – Dynamic LLM Benchmarks and SSO for Multi-Vendor Deployment
Hey HN! I’m the founder of Unify, and we’ve just released our Model Hub, which provides a collection of LLM endpoints with live runtime benchmarks all plotted across time: https://unify.ai/hub A key finding is that static tabular runtime benchmarks for LLMs simply do not work. It’s necessary to take a time-series perspective, and plot the variations through time. We currently have 21 models provided by: Anyscale, Perplexity AI, Replicate, Together AI, OctoAI, Mistral AI and OpenAI, with more on the roadmap. We test across different regions (Asia, US, Europe), with varied…
Unify – Dynamic LLM Benchmarks and SSO for Multi-Vendor Deployment launched on February 6, 2024 with 91 votes, #680 of 3,962 launches that month and more than 90% of that year's launches.
In plain words
Unify provides a Model Hub that tracks live performance metrics for 21 large language models from providers including OpenAI, Mistral AI, and Together AI. Rather than static benchmarks, it displays runtime performance as time-series data across different regions and concurrency levels, revealing how model stability and speed vary over time. The platform helps developers understand real-world performance variations caused by API updates and system changes, with benchmarking code available open source.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hey HN! I’m the founder of Unify, and we’ve just released our Model Hub, which provides a collection of LLM endpoints with live runtime benchmarks all plotted across time: https://unify.ai/hub A key finding is that static tabular runtime benchmarks for LLMs simply do not work. It’s necessary to take a…
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