Kimi K2.7 CodevsGLM-5.3
Kimi K2.7 Code | GLM-5.3 | |
|---|---|---|
| Specifications | ||
Parameters | 1T | 743B |
Context window | 256k | — |
| Benchmarks | ||
Agentic coding CursorBench v3.2Cursor's own test of harder, real-world coding tasks inside a code editor, on the refreshed v3.2 task set. Scores aren't comparable with v3.1. Higher is better. | 49.7% | — |
Agentic coding DeepSWE 1.1Artificial Analysis' independent test of deep, agentic software-engineering work — the AI has to plan and carry out substantial coding tasks end to end. (Version 1.1 of the test.) Higher is better. | 31% | 66.9%Best |
Next.js coding Next.js EvalsVercel's open eval of how well AI coding agents build and migrate real Next.js apps — measured as the share of tasks the agent completes successfully. Higher is better. | 75% | — |
Agentic terminal coding Terminal-Bench 3.0command-line task completion (v3.0, much harder task set) | — | 28.3% |
Cybersecurity CyberGymTests the AI on cybersecurity challenges — finding and exploiting software weaknesses inside a safe sandbox. Higher is better. | — | 84.5% |
Cybersecurity ExploitBenchA 'capability ladder' for security research, built by CMU researchers: the AI is given known bugs in Chrome's V8 engine and scored on how far it gets toward a working exploit inside a research sandbox — from understanding the patch to triggering a crash. Higher is better. | — | 54.4% |
Cybersecurity ExploitGym · 6-hour budgetCan an AI agent turn a known software vulnerability into a working attack in a controlled lab? Built by MPI-SP researchers, the score is how many of 898 real cases (userspace programs, the V8 engine, the Linux kernel) it cracks — here with a 6-hour compute budget per case. Higher is better. | — | 130 |
Cybersecurity ExploitGym · 2-hour budgetCan an AI agent turn a known software vulnerability into a working attack in a controlled lab? Built by MPI-SP researchers, the score is how many of 898 real cases (userspace programs, the V8 engine, the Linux kernel) it cracks — here with a 2-hour compute budget per case. Higher is better. | — | 105 |
Multidisciplinary reasoning Humanity's Last Exam · with toolsHumanity's Last Exam — extremely hard expert questions across many subjects. “With tools” means the AI is allowed to search the web or run code while answering. Higher is better. | — | 62.5% |
Agentic computer use Agent's Last ExamA hard set of desktop and operating-system tasks an AI agent has to finish by looking at the screen and working the machine itself. The score is the share it passes outright — partial credit does not count. Higher is better. | — | 28.5% |
Business workflows AutomationBenchTests whether the AI can run real multi-step business workflows — the kind of end-to-end office processes companies want to automate — from start to finish. Higher is better. | — | 48.2% |
Knowledge work GDPval-AA v2economically valuable knowledge work (v2, re-based Elo) | — | 1769 |
Community preference (code) Arena Elo (Code)Like the text arena, but people vote on which AI writes better code. The votes become a chess-style Elo rating on arena.ai. Higher is better. | 1469 | — |
| Overview | ||
| Company | Moonshot AI | Z.ai |
| Release date | Jun 12 2026 | Aug 14 2026 |
| Access | Open Weight | Proprietary |
Which is better: Kimi K2.7 Code or GLM-5.3?
GLM-5.3 leads Kimi K2.7 Code on 1 of the 1 benchmark they both report (DeepSWE 1.1). Kimi K2.7 Code shipped 63 days before GLM-5.3, so benchmark comparisons should account for the intervening progress.
Kimi K2.7 Code has 1T parameters, while GLM-5.3 has 743B. Kimi K2.7 Code is open weight, while GLM-5.3 is proprietary.
On DeepSWE 1.1, GLM-5.3 leads at 66.9% vs Kimi K2.7 Code at 31%.
Frequently asked questions
Kimi K2.7 Code was released by Moonshot AI on Jun 12 2026.
GLM-5.3 was released by Z.ai on Aug 14 2026.
GLM-5.3 leads on DeepSWE 1.1 — Kimi K2.7 Code 31% vs GLM-5.3 66.9%.
Kimi K2.7 Code is an open weight model released by Moonshot AI. GLM-5.3 is a proprietary model released by Z.ai.
Other comparisons
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