No, some benchmarks are definitely objective, but most can be easily gamed. For example, most of the benchmarks on the model cards: they have measurable answers that don't rely on a human judge (a human made the question, but the answers are measuring some uncontroversial knowledge or capability). But because there is a single, correct answer, and those answer leak (or are randomly discovered and optimized for in training), they lose value over time, and regardless, they have a ceiling on the intelligence they can measure.

Others are purely subjective, like LMArena, which really only measures the personality and style preferences of the masses at this point, because frontier LLM technical answers are too hard for the average person to judge.

Then there are some interesting one-off benchmarks, but they lack enough rigor, breadth, and samples to draw larger conclusions from.

So we designed our benchmark with 3 goals: objective measurements (individual submissions not dependent on a human or LLM judge), no known correct answer (so simulations can scale to much higher levels of intelligence), and enough variety over important aspects of intelligence. We do this by running multiple models in cooperative/competitive environments with very complex action spaces and objective scoring, where model performance is relative and affected by the actions of other participants.

And yeah, there are some interesting results when you have a more objective benchmark. It should raise eyebrows when every single sub-release of every company's model is better across the board than its predecessor -- that isn't reality.

I agree that benchmarks are inherently subjective.

but the fact that you cite your brief as your main argument is funny - you don't even have any inherently subjective numbers to justify what you believe, you only have "I don't believe".

you are arguing with your belief instead of an objective truth. benchmark is more objective, if you don't agree with it, come up with a better one. but what you believe doesn't matter.

I think most hardcore people I know are still sticking with 4.5 for coding workflows

Take a look at the Time column in https://gertlabs.com/?mode=oneshot_coding -- this is the total time to complete a solution for a reasonably complex problem end-to-end (you would have to divide by avg submission size to estimate tok/s). It's fast in the sense that most of the smart, recent Chinese releases are quite slow, especially the DeepSeek Pro variant. Opus 4.7 is also quite fast.

If pure speed is most important for your use case, GPT-5.3 Chat is the fastest model we've tested and it's still reasonably smart. Not meant for agentic tool usage / long context, though.

So it might be more useful for business applications or non-engineering usage where you don't need exceptional intelligence, but it's useful to get fast, cheap responses.

Waiting on public API release. Once it drops, results will be up within 24 hours.