Update (29 July 2026): We have run Kimi K3 through the same setup, served with SGLang. At 1.4TB of weights, K3 does not fit within the memory budget of the 8×B200 node used for GLM-5.2 (1.5TB of total HBM leaves no headroom for KV cache). This run therefore used an 8×B300 node, which brings 288GB of HBM per GPU instead of 192GB, or 2.3TB per node. That averages out to around 20% higher hardware cost than the 8×B200 setup, depending on your rental provider. In our runs, K3 served 16 concurrent sessions (GLM-5.2 managed 24). Aggregate token throughput is about 30% lower (122 vs 170 tok/s at 16 users), and median task time is about 50% longer (38 vs 26 minutes). That makes K3 roughly 8 times slower than our Claude Code baseline. However, K3 makes up for it in quality, resolving 86.4% of tasks , 24 percentage points above both GLM-5.2 and Opus 4.8 (62.5% for both). One caveat is that our benchmark tasks from SWEBench Pro may have been in K3's training data, so treat that resolution rate with a grain of salt.…