It's the Summer of 2026. A new frontier model is released nearly every month. Each one comes with a new set of benchmark numbers, some hype on X, and the same nagging question for teams running LLMs in production: should we switch?
Most teams never really answer it. Answering it honestly means building a dataset that represents your real traffic, standing up API access to every provider you want to test, writing a rubric, wiring up judging infrastructure, burning the tokens to run it all, and then interpreting the results. That's a week of work, per model, per task, every time something new ships. So the question gets shelved. The model that was chosen months ago, based on a price point and vibes, keeps running because swapping models in production is both
It's the Summer of 2026. A new frontier model is released nearly every month. Each one comes with a new set of benchmark numbers, some hype on X, and the same nagging question for teams running LLMs in production: should we switch?
Most teams never really answer it. Answering it honestly means building a dataset that represents your real traffic, standing up API access to every provider you want to test, writing a rubric, wiring up judging infrastructure, burning the tokens to run it all, and then interpreting the results. That's a week of work, per model, per task, every time something new ships. So the question gets shelved. The model that was chosen months ago, based on a price point and vibes, keeps running because swapping models in production is both
It's the Summer of 2026. A new frontier model is released nearly every month. Each one comes with a new set of benchmark numbers, some hype on X, and the same nagging question for teams running LLMs in production: should we switch?
Most teams never really answer it. Answering it honestly means building a dataset that represents your real traffic, standing up API access to every provider you want to test, writing a rubric, wiring up judging infrastructure, burning the tokens to run it all, and then interpreting the results. That's a week of work, per model, per task, every time something new ships. So the question gets shelved. The model that was chosen months ago, based on a price point and vibes, keeps running because swapping models in production is both
It's the Summer of 2026. A new frontier model is released nearly every month. Each one comes with a new set of benchmark numbers, some hype on X, and the same nagging question for teams running LLMs in production: should we switch?
Most teams never really answer it. Answering it honestly means building a dataset that represents your real traffic, standing up API access to every provider you want to test, writing a rubric, wiring up judging infrastructure, burning the tokens to run it all, and then interpreting the results. That's a week of work, per model, per task, every time something new ships. So the question gets shelved. The model that was chosen months ago, based on a price point and vibes, keeps running because swapping models in production is both
