GLM-5.3 753B vs Kimi K3

Analysis

GLM-5.3 and Kimi K3 are both frontier open-weight releases from rival labs, about three weeks apart (Kimi K3's weights opened July 27, GLM-5.3's August 25), and Kimi K3 is by far the bigger model: 2,800B total parameters against GLM-5.3's 753B, and more than double the active count. On the one benchmark both self-report using the same suite name, Terminal-Bench 2.1, they land within a tenth of a point of each other, and the smaller model's actual hardware bill is a fraction of the larger one's.

GLM-5.3 caches every one of its 78 layers with Multi-head Latent Attention (a 512-dimension compressed KV latent) and applies no further compression on top of that. Kimi K3 takes a sparser route on layers themselves: of its 93 layers, only 24 accumulate a KV cache at all (gated MLA), and the other 69 run Kimi Delta Attention, a linear-attention design whose memory is a fixed-size state that doesn't grow with context. The result is counterintuitive: at the 1,048,576-token context both models reach natively, GLM-5.3's KV cache (167.5 GB, all 78 layers uncompressed) is actually larger than Kimi K3's (51.5 GB, despite Kimi K3 caching barely a quarter of its layers), because Kimi K3's sparser caching pattern outweighs GLM-5.3 simply having fewer layers overall. Weights are where the real gap sits: both ship at their own recommended low-bit format (GLM-5.3's Q2_K estimate, Kimi K3's native MXFP4), and GLM-5.3 needs about 286.9 GB against Kimi K3's roughly 1,400 GB, nearly 5x more. Add each model's KV cache at the shared 1,048,576-token context and the full picture is 508.9 GB for GLM-5.3 against 1,625.7 GB for Kimi K3, a little over 3x. On Terminal-Bench 2.1, the two are essentially tied: 88.2 for GLM-5.3 against 88.3 for Kimi K3, Z.ai's own comparison chart against Moonshot's own KimiCode-harness figure. GPQA Diamond favors Kimi K3: Moonshot reports 93.5 (with Artificial Analysis independently measuring 94), against GLM-5.3's 91.7 (also an Artificial Analysis figure, since Z.ai doesn't publish GPQA). GLM-5.3 pulls ahead clearly on the newer security-adjacent suites Z.ai's own table covers for both models: CyberGym 84.5 against Kimi K3's 80.0, and ExploitBench 54.4 against 32.2, a gap that traces back to the emergent exploitation-chaining ability Z.ai found during GLM-5.3's post-training. Licensing splits differently than the numbers suggest: GLM-5.3's custom license only bites a Model-as-a-Service business over $10 billion in trailing 12-month revenue, while Kimi K3's custom license requires a separate agreement above $20 million and mandatory "Kimi K3" branding above 100 million monthly active users, a lower bar that catches more real businesses. Modality favors Kimi K3 outright: it accepts image and video input through a 401M-parameter MoonViT-V2 encoder, where GLM-5.3 is text-only.

Bottom line: Neither model is realistic to self-host on anything short of a serious multi-GPU server, but GLM-5.3 is the far more attainable of the two: roughly a third of Kimi K3's weight VRAM for essentially the same Terminal-Bench 2.1 score, plus a clear lead on the security-adjacent benchmarks both vendors' own tables report. Kimi K3's real advantages are a meaningfully higher GPQA Diamond score and native image/video input, which matter if your workload is multimodal or leans on broad reasoning over coding and security tasks specifically. For a coding- or agentic-security-focused deployment where hardware budget matters at all, GLM-5.3 is the more practical open-weight pick.