Combining both models yields a 16% performance lift.
Many users praise routing with failovers for Kimi K3 Max because it yields performance gains like a 16% lift from cascading beyond mere price parity.
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We analyzed Kimi K3 Max vs. GPT 5.6 Sol Max for software engineering tasks using DeepSWE. Kimi K3 Max matches GPT 5.6 Sol Max at ~55% of the price. Interestingly - used together, the two models deliver a ~16% performance lift. More insights in the thread! 👇
Deepdive: Kimi K3 max vs. GPT 5.6 Sol max on software engineering/DeepSWE tasks. Both of these models provide nice specializations and routing/cascading between them is the right way to go! full deep-dive 👇(1/n)🧵
@togethercompute if folks have any questions, let me know!
@zainhas @togethercompute How does K3 achieve such high performance while only activating 16 of it's 897 MoE? And how does it include both MXFP4 and MXFP8? Wouldn't that introduce noise with irregularities in quantizations? Or is the weights quantize-aware?
@togethercompute It’s fine when your option is API on both. But when you mix in subsidized subscriptions then K3 becomes the expensive option. Figuring out a solution to that is the answer everyone wants. Including me for my product.
@syzodidntmakeit @togethercompute yep the mention that from SFT stage onwards they perform QAT Source: https://www.kimi.com/blog/kimi-k3#:~:text=quantization%2Daware%20training
@togethercompute the 16% lift from cascading is the more interesting number than the price parity. that's the case for keeping a router in the loop instead of picking one model and defending it forever.
@togethercompute Insightful comparison revealing strong value and complementary use
@goodhunt @togethercompute 🚀 routing with failovers is the way of the future!
@zainhas @togethercompute What do you mean by "used together" here? What's the exact approach?
@togethercompute The 16% lift is only actionable if disagreement can be predicted before paying for both. Is that oracle routing/ensemble uplift, or a deployable cascade? In practice, router calibration may be more valuable than either model's marginal benchmark edge.
@togethercompute wow