Looks like it doesn't get close to GPT-5, Claude 4, or GLM-4.5, but still does reasonably well compared to other open weight models. Benchmarks are rarely the full story though, so time will tell how good it is in practice.
garbage benchmark, inconsistent mix of "agent tools" and models. if you wanted to present a meaningful benchmark, the agent tools will stay the same and then we can really compare the models.
there are plenty of other benchmarks that disagree with these, with that said. from my experience most of these benchmarks are trash. use the model yourself, apply your own set of problems and see how well it fairs.
I also publish my own evals on new models (using coding tasks that I curated myself, without tools, rated by human with rubrics). Would love you to check out and give your thoughts:
How can a benchmark be secret if you post it to an API to test a model on it?
"We totally promise that when we run your benchmark against our API we won't take the data from it and use to be better at your benchmark next time"
:P
If you want to do it properly you have to avoid any 3rd party hosted model when you test your benchmark, which means you can't have GPT5, claude, etc. on it; and none of the benchmarks want to be 'that guy' who doesn't have all the best models on it.
How do you propose that would work? A pipeline that goes through query-response pairs to deduce response quality and then uses the low-quality responses for further training? Wouldn't you need a model that's already smart enough to tell that previous model's responses weren't smart enough? Sounds like a chicken and egg problem.
It just means that once you send your test questions to a model API, that company now has your test. So 'private' benchmarks take it on faith that the companies won't look at those requests and tune their models or prompts to beat them.
They have quite large amounts of money. I don't think they need to be very cost-efficient. And they also have very smart people, so likely they can figure out a somewhat cost-efficient way. The stakes are high, for them.
Depends. Something like arc-agi might be easy as it follows a defined format. I would also guess that the usage pattern for someone running a benchmark will be quite distinct from that of a normal user, unless they take specific measures to try to blend in.
I remember asking for quotes about the Spanish conquest of South America because I couldn't remember who said a specific thing. The GPT model started hallucinating quotes on the topic, while DeepSeek responded with, "I don't know a quote about that specific topic, but you might mean this other thing." or something like that then cited a real quote in the same topic, after acknowledging that it wasn't able to find the one I had read in an old book.
i don't use it for coding, but for things that are more unique i feel is more precise.
I wonder if Conway's law is at all responsible for that, in the similarity it is based on; regional trained data which has concept biases which it sends back in response.
I'm doing coreference resolution and this model (w/o thinking) performs at the Gemini 2.5-Pro level (w/ thinking_budget set to -1) at a fraction of the cost.
https://www.tbench.ai/leaderboard
Looks like it doesn't get close to GPT-5, Claude 4, or GLM-4.5, but still does reasonably well compared to other open weight models. Benchmarks are rarely the full story though, so time will tell how good it is in practice.