For what it's worth, I work at OpenAI and I can guarantee you that we don't switch to heavily quantized models or otherwise nerf them when we're under high load. It's true that the product experience can change over time - we're frequently tweaking ChatGPT & Codex with the intention of making them better - but we don't pull any nefarious time-of-day shenanigans or similar. You should get what you pay for.
There's almost 0% chance that OpenAI doesn't quantize the model right off the bat.
I am willing to bet large amounts of money that OpenAI would never release a model served as fully BF16 in the year of our lord 2026. That would be insane operationally. They're almost certainly doing QAT to FP4 for FFN, and a similar or slightly larger quant for attention tensors.
Thank you for your answer. I have a similar question as OP, but in regards of the GPT models in MS copilot. My experience is that the response quality is much better when calling the API directly or through the webUI.
I know this might be a question that's impossible for you to answer, but if you can shed any light to this matter, I'd be grateful as I am doing an analysis over what AI solutions that can be suitable for my organisation.
Although Arena is adversarial and resistant to goodharting, it's not immune. Models that train on Arena converge on helpfulness, not necessarily truthiness
This is great, but personally, I really wish we had an Elo leaderboard specifically for the quality of coding agents.
Honestly, in my opinion, GPT-5.5 Codex doesn't just crush Claude Code 4.7 opus —it's writing code at a level so advanced that I sometimes struggle to even fully comprehend it. Even when navigating fairly massive codebases spanning four different languages and regions (US, China, Korea, and Japan), Codex's performance is simply overwhelming.
How would we even go about properly measuring and benchmarking the Elo for autonomous agents like this?
It was often much faster, and when I revisited the code later, there were cases where I realized it had moved the implementation toward a better abstraction.
I should also add that I am not claiming to be a particularly great programmer. I have never worked at FAANG, and I haven't had much exposure to the kind of massive codebases those engineers deal with every day.
Most of the code I've worked with comes from Korean and Chinese startups, industrial contractors, or older corporate research-lab environments. So I know my frame of reference is limited.
When I write code, I usually rely on fairly conservative patterns: Result-style error handling instead of throwing exceptions through business logic, aggressive use of guard clauses, small policy/strategy objects, and adapters at I/O boundaries. I also prefer placing a normalization layer before analysis and building pure transformation pipelines wherever possible.
So when Codex produced a design that decoupled the messy input adapter from the stable normalized data, and then separated that from the analyzer, it wasn't just 'fancier code.' It aligned perfectly with the architectural direction I already value, but it pushed the boundaries of that design further than I would have initially done myself.
This is exactly why I hesitate to dismiss code as 'bad' just because I don't immediately understand it. Sometimes, it really is just bad code. But sometimes, the abstraction is simply a bit ahead of my current local mental model, and I only grasp its true value after a second or third requirement is introduced.
To be completely honest, using AI has caused a significant drop in my programming confidence. Since AI is ultimately trained on codebases written by top-tier programmers, its output essentially represents the average of those top developers—or perhaps slightly below their absolute peak.
I often find myself realizing that the code I write by hand simply cannot beat it
The interesting thing I find is how Anthropic has been more consistently improving over time in the last few years, that allows it to catchup and surpass OpenAI and Google. The latter two have pretty much plateau over the last year or so. GPT 5.5 is somehow not moving the needle at all.
I hope to see the other labs can bring back competition soon!
The Elo rating system measures relative performance to the other models. As the other models improve or rather newer better models enter the list, the Elo score of a given existing model will tend to decrease even though there might be no changes whatsoever to the model or its system prompt.
You can't use Elo scores to measure decay of a models performance in absolute terms. For that you need a fixed harness running over a fixed set of tests.
Is this slop? It has wildly aggressive language that agrees with a subset of pop sentiment, re: models being “nerfed”. It promises to reveal this nerfing. Then, it goes on to…provide an innocuous mapping of LM Arena scores that always go up?
the decays are just more capable other models entering the population, making all prior models lose more frequently
That sounded like a press bulletin, so just to let you clarify yourself: Does that mean you may switch to lightly quantized models?
I am willing to bet large amounts of money that OpenAI would never release a model served as fully BF16 in the year of our lord 2026. That would be insane operationally. They're almost certainly doing QAT to FP4 for FFN, and a similar or slightly larger quant for attention tensors.
I know this might be a question that's impossible for you to answer, but if you can shed any light to this matter, I'd be grateful as I am doing an analysis over what AI solutions that can be suitable for my organisation.
Honestly, in my opinion, GPT-5.5 Codex doesn't just crush Claude Code 4.7 opus —it's writing code at a level so advanced that I sometimes struggle to even fully comprehend it. Even when navigating fairly massive codebases spanning four different languages and regions (US, China, Korea, and Japan), Codex's performance is simply overwhelming.
How would we even go about properly measuring and benchmarking the Elo for autonomous agents like this?
Most of the code I've worked with comes from Korean and Chinese startups, industrial contractors, or older corporate research-lab environments. So I know my frame of reference is limited.
When I write code, I usually rely on fairly conservative patterns: Result-style error handling instead of throwing exceptions through business logic, aggressive use of guard clauses, small policy/strategy objects, and adapters at I/O boundaries. I also prefer placing a normalization layer before analysis and building pure transformation pipelines wherever possible.
So when Codex produced a design that decoupled the messy input adapter from the stable normalized data, and then separated that from the analyzer, it wasn't just 'fancier code.' It aligned perfectly with the architectural direction I already value, but it pushed the boundaries of that design further than I would have initially done myself.
This is exactly why I hesitate to dismiss code as 'bad' just because I don't immediately understand it. Sometimes, it really is just bad code. But sometimes, the abstraction is simply a bit ahead of my current local mental model, and I only grasp its true value after a second or third requirement is introduced.
To be completely honest, using AI has caused a significant drop in my programming confidence. Since AI is ultimately trained on codebases written by top-tier programmers, its output essentially represents the average of those top developers—or perhaps slightly below their absolute peak.
I often find myself realizing that the code I write by hand simply cannot beat it
I hope to see the other labs can bring back competition soon!
You can't use Elo scores to measure decay of a models performance in absolute terms. For that you need a fixed harness running over a fixed set of tests.
Thank you, I just looked at the chart and said to myself: ELO? YOLO!
That Elo ranking is also called chess ranking