GLM 5.3 vs Kimi K3: Benchmarks, Intelligence Index & Which Is Better

Quick answer: GLM 5.3 and Kimi K3 are the two strongest open-weights models of mid-2026, and they're remarkably close — on Z.AI's official launch table GLM 5.3 wins CyberGym (84.5 vs 80.0) and AutomationBench (48.2 vs 46.7), while Kimi K3 wins DeepSWE (67.5 vs 66.9) and SWE-Marathon (48.1 vs 42.5). Independent trackers put them at roughly the same intelligence level (~60 on Artificial Analysis' index), with GLM 5.3's post-training-only upgrade the differentiator.

TL;DR

Benchmark	GLM 5.3	Kimi K3
Terminal-Bench 2.1	88.2	88.3
Terminal-Bench 3.0	28.3	17.4
DeepSWE v1.1	66.9	67.5
SWE-Marathon v1.1	42.5	48.1
AutomationBench v1.0.6	48.2	46.7
Toolathlon Verified	73.0	76.5
Agents' Last Exam (ALE-CLI)	28.5	27.6
CyberGym	84.5	80.0
ExploitBench	54.4	32.2
ExploitGym (2h / 6h)	105 / 130	36 / 70

Two Open-Weight Flagships, Different Paths

Both models represent the Chinese open-weights vanguard, but they got here differently:

GLM 5.3 — same base model as GLM 5.2, with all gains from scaled post-training: more RL environments, more compute, and (unexpectedly) emergent cyber capability. Released August 14, 2026.
Kimi K3 — Moonshot AI's flagship, the reference open-weights model GLM 5.2 was already trading blows with.

On paper they're near-identical on general coding. The interesting splits are in the specialties.

Where GLM 5.3 Wins

Terminal-Bench 3.0: 28.3 vs 17.4. The hardest public terminal benchmark — GLM 5.3's post-training focus on long-horizon terminal tasks shows here with a big margin. This is the "real terminal work" benchmark developers care about.

CyberGym: 84.5 vs 80.0. GLM 5.3's headline emergent capability — vulnerability discovery — is a clear differentiator vs Kimi K3. ExploitBench (54.4 vs 32.2) and ExploitGym (105/130 vs 36/70) widen the gap further. If security work matters, this is the deciding factor.

AutomationBench: 48.2 vs 46.7 and ALE-CLI: 28.5 vs 27.6 — GLM 5.3 edges ahead on agentic and long-horizon automation.

Where Kimi K3 Wins

SWE-Marathon v1.1: 48.1 vs 42.5 — the biggest gap in the table, in Kimi's favor. Kimi K3 is stronger on marathon-style end-to-end software engineering tasks.

Toolathlon Verified: 76.5 vs 73.0 — Kimi K3 leads on tool-use reliability.

DeepSWE v1.1: 67.5 vs 66.9 — effectively a tie, slight edge to Kimi.

Terminal-Bench 2.1: 88.3 vs 88.2 — dead heat on the older, easier terminal benchmark.

Independent Intelligence Scores

Independent trackers (Artificial Analysis) put both models at roughly the same intelligence level — around 60 on the Intelligence Index, near the top of the open-weights pack (the median model sits around 35). The practical takeaway: benchmark-level, these are peers — pick on workload fit and ecosystem, not raw score.

The Token-Efficiency Angle

Z.AI's own data shows GLM 5.3 is unusually token-efficient: 34.5% task completion at ~75K output tokens (Max effort) vs GLM 5.2's 23.4% at ~96K. That efficiency directly cuts cost per completed task — relevant if you're comparing per-token pricing between the two models in API or self-hosted setups.

Which Should You Choose?

Choose GLM 5.3 if you: value long-horizon terminal/agent work (TB 3.0, AutomationBench), need vulnerability-discovery capability, or want the model with public weights coming in ~2 weeks and the most token-efficient coding.

Choose Kimi K3 if you: prioritize marathon software-engineering tasks (SWE-Marathon), tool-use benchmarks (Toolathlon), or prefer Moonshot's ecosystem and existing deployment track record.

The pragmatic answer: both are excellent open-weights choices. If you're already on GLM 5.2, GLM 5.3 is a zero-friction upgrade (same base, same serving stack). If you're choosing fresh, run your own workload on both — the differences are workload-shaped, not absolute.

FAQ

Is GLM 5.3 better than Kimi K3? On Z.AI's official table: GLM 5.3 wins Terminal-Bench 3.0 (28.3 vs 17.4), CyberGym (84.5 vs 80.0), and AutomationBench (48.2 vs 46.7); Kimi K3 wins SWE-Marathon (48.1 vs 42.5) and Toolathlon (76.5 vs 73.0). Independent indexes rate them about equal.

Which is better for coding agents? GLM 5.3 edges ahead on long-horizon terminal and automation benchmarks (TB 3.0, AutomationBench, ALE-CLI); Kimi K3 leads on marathon SWE tasks and tool use.

Are both open-weights? Yes — Kimi K3 is open-weights; GLM 5.3's weights arrive on HuggingFace ~2 weeks after launch.

Which has better cybersecurity capability? GLM 5.3 by a wide margin — CyberGym 84.5 vs 80.0, ExploitBench 54.4 vs 32.2.

Where do these numbers come from? Z.AI's official GLM-5.3 launch table (August 14, 2026), which includes Kimi K3 as a comparison model; independent intelligence scores from Artificial Analysis.