Kimi K3.1 Pro vs GLM-5.3 753B

Analysis

GLM-5.3 is the open-weight model Kimi K3.1 Pro's predecessor had to beat: on this site's GLM-5.3 vs Kimi K3 page, GLM matches K3 on Terminal-Bench 2.1 at about a third of the memory and wins the security benchmarks. Moonshot included GLM-5.3 in K3.1 Pro's launch table, so the rematch can be read on the same harness. The memory gap is unchanged; most of the benchmark story has flipped.

GLM-5.3 is 753B parameters with 40B active, and its Q4_K_M estimate needs 515.1 GB at an 8k context, against 1,749.1 GB for Kimi K3.1 Pro's native MXFP4 checkpoint, about 3.4 times as much. Long context narrows that, because the two models cache very differently. GLM caches every one of its 78 layers (a 512-wide compressed latent per token, nothing skipped), while Kimi caches only 24 of 93 and keeps a fixed-size state in the rest. At the full 1,048,576-token window GLM's KV cache is 167.5 GB against Kimi's 51.5 GB, so the totals become 701.2 GB against 1,806.4 GB, about 2.6 times. GLM runs on far more hardware: eight 141 GB H200s hold it even at the full window, though this site's estimate falls from 41.9 tokens per second at 8k to 7.3 at 1M as the cache grows. Kimi needs eight 288 GB B300s. Neither fits a single GPU or a 512 GB Mac Studio at Q4_K_M. On Moonshot's table, at max effort for both, Kimi leads on every coding and agent row both report: Terminal-Bench 4.0 65.7 against 41.8, DeepSWE v1.1 75.5 against 66.9, Humanity's Last Exam with tools 70.0 against 62.5, and APEX-Agents 69.4 against 56.6. The security result reverses the K3 comparison: on ExploitBench, where Z.ai's own table had GLM-5.3 at 54.4 against K3's 32.2, Moonshot's table puts K3.1 Pro at 74.7 against the same 54.4, even after Moonshot removed cyber-related training data for the open release. GPQA-Diamond is 94.2 against 91.7, and on Moonshot's Hallu Bench Hard Kimi gets 8.3% of claims wrong against GLM's 22.6%. GLM keeps two advantages. Its license only restricts model-as-a-service businesses above $10 billion in revenue, against Kimi's $20 million threshold. And it is text only where Kimi takes images, which matters only if you need vision. These benchmark figures are Moonshot's; Z.ai has not published a table that includes K3.1 Pro.

Bottom line: If your servers are Hopper-class, GLM-5.3 is still the frontier model you can actually run: eight H200s hold it, and it costs about a third of Kimi's memory at short context. If you already have a B300-class node, Kimi K3.1 Pro is the stronger model on every row of Moonshot's table that includes both, including the security benchmark where GLM-5.3 used to lead, with a much lower hallucination rate on Moonshot's test. For long-context work the gap narrows to about 2.6 times, because GLM's full-layer cache grows to 167.5 GB while Kimi's stays at 51.5 GB.