Positive users welcome Kimi-K3 matching top Claude scores at half the cost for its affordability and future potential, while negative users dismiss the benchmark results over efficiency gaps, compute costs, and higher hallucination risks.
Based on 10 visible X reactions from 32 accounts; directional sample.
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@xeophon your hobby is actually just ragebaiting, is it? we want to know what the minimum number of tokens is that is required to get to a given level of performance even if fable models flatline, this doesn't actually matter because we care about the frontier
@scaling01 come on man, you know what you are doing here. you are point at literal noise, deliberately. the CI shows that kimi is within error with the fable levels. that does tell you more about the measurement than the models, but still
@scaling01 Nice was getting pretty frustrated with opus recently was using it how I used to use sonnet subagents
@scaling01 This model, with more training and a focus on efficiency, could become something else entirely.
@scaling01 5.6 sol high looks awesome
@scaling01 good news for non-americans
@xeophon would you please show me the data trend that tells you that higher reasoning performance leads to lower performance? and looking at the conservative estimates of performance (the lower CI bound) all three Fable variants outscore Kimi-K3
@scaling01 Using fable high vs xhigh with those error bars is certainly a choice
Positive users welcome Kimi-K3 matching top Claude scores at half the cost for its affordability and future potential, while negative users dismiss the benchmark results over efficiency gaps, compute costs, and higher hallucination risks.
Based on 10 visible X reactions from 32 accounts; directional sample.
Ask a question below.
Published answers will appear here.
Fable level can easily be reached with 1-2 more iterations of post training
@xeophon *reasoning budget