GLM 5.1 vs Kimi K2.6

Choose GLM 5.1 if you want…

• ✓Faster output (~80.7 tokens/sec)

Choose Kimi K2.6 if you want…

• ✓Higher intelligence (Artificial Analysis index 54)
• ✓Lower price ($1.71 / 1M blended)
• ✓Larger context window (256K)

GLM 5.1

GLM-5.1 from Z.ai is built for one thing above all else: software engineering that runs on its own. A 754-billion parameter Mixture-of-Experts model, it tops the SWE-Bench Pro leaderboard at 58.4%, edging out both GPT-5.4 and Claude Opus 4.6 on real-world coding tasks. What sets it apart in practice is stamina — it can pursue a single engineering goal autonomously for up to eight hours, sustaining hundreds of iterations and thousands of tool calls without human intervention. Users consistently praise this long-horizon execution for agent-based workflows where other models stall. It also delivers fast responses, with a time-to-first-token of 1.33 seconds against a class median of 2.37 seconds. The honest trade-off: GLM-5.1 accepts text only, with no image input, making it a poor fit for visual debugging or UI-centric tasks. It also tends toward verbosity in practice, which can inflate token costs. For teams building autonomous coding pipelines, though, it earns its place at the top of the leaderboard.

Kimi K2.6

Kimi K2.6 is Moonshot AI's open-weight coding specialist built for the kind of work that takes hours, not seconds. Its signature capability is agent swarm orchestration — coordinating up to 300 sub-agents across 4,000 execution steps — enabling autonomous refactoring sessions that developers have run for over 13 hours straight. On SWE-Bench Verified it scores 80.2%, and it edges out GPT-5.4 on SWE-Bench Pro at 58.6%, making it the strongest open-weight coding model available at its price point. Users report up to 88% cost savings on coding workloads compared to proprietary alternatives, which is the real draw for teams running code-heavy pipelines at scale. The tradeoff is speed and occasional drift: at 40.6 tokens per second — well below the category median — it is not suited to real-time use. In long-running agentic tasks, users note the model can wander into unnecessary redesigns around the three-hour mark, requiring clear, constrained prompting to keep it on track. For deep, non-interactive coding work where cost efficiency and open-weight flexibility matter more than instant responses, K2.6 occupies a position few models can match.