alt Hacker NewsIts pretty clear that any benchmark that comes out will be outdated and exist within the training data with short measure. There will always be an incentive to optimize specifically for these benchmarks even if just for marketing material. Sure there is a training cutoff, but its usually only 3-6 months off of the public release dates.
The problem with coding benchmarks then becomes creating novel benchmarks that are guaranteed to not already be in the training data, and not borrow anything from previous benchmarks.
In this regard I don't think any benchmark that was created before a given model is released should ever be considered valid or representative of model performance. The potential financial gain for including the data just to be able to market a minor improvement is too swaying. With that in mind they should honestly just stop including benchmarks altogether in marketing material
Let the model speak for itself and let the community decide, but of course that will never slide with corporate types with so much money on the line.
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> let the community decide
Which community are we talking about? The professionals with 10+ years experience using LLMs, the vibe coders that have no experience writing code and everyone in between? If you read some of the online communities the experiences with the models all over the place, some compare GPT 5.5 to the second coming of JC while others think it's stupider than 5.4.
I personally don't have time to build a set of private benchmarks to compare the models that are coming out so I'm mostly relying on private and semi-private benchmarks to get a feel for how models are improving before I subscribe to a service and start using it myself. At least it's something a bit more reliable than the vibes of random people and bots on reddit.
An easy way to make coding benchmarks viable again is to initialize the models with 200k of distracting or unrelated tokens in their context. Or even just run the tests sequentially in the same context and see how far the model gets before it unwinds.
These benchmarks are always greenfield, but people want a model that can deal with a rotted context.
"The community" is astroturfed as hell though. Anthropic pays influencers to promote Claude Code and likely bots a ton as well, so it's hard to come to any kind of consensus online. Even if everyone was acting in good faith, some people will have a much better experience than others because of the domain they're working in (e.g. AI being much better at frontend and commonly used libraries).
The only real way to evaluate a model is to test it yourself but that's exhausting for each new model and not comprehensive anyway.
I think the solution is a bunch of private trusted benchmarks, and averaging their announced results.
I agree with the sentiment but I wonder if a sufficiently large amount of sufficiently sophisticated benchmarks existed then I would be surprised if a model would only memorize those benchmarks while showing terrible real world performance. We are not there yet but maybe one day we will be.
In contrary: In an Interview someone from OpenAI said they are trying to avoid it because it makes it harder for them to determine if a model gets better or not.
They mention this in the article. This is why private (non public) benchmark tasks that have been made from scratch are necessary.
a good benchmark would probably porting a selected repo to another language. then clear context notes, and have it port it back.
as long as theres a test framework, you could gauge success deterministically.
This is why I made Zork bench. Zork, the text adventure game, is in the training data for LLMs. It’s also deterministic. Therefore it should be easy for an LLM to play and complete. Yet they don’t. Understanding why is the goal of Zork bench.
https://github.com/mnky9800n/zork-bench