Kimi K2 Thinking vs GLM 5

Pricing math on a real workload

Provider list price for input tokens is identical: $0.60/M for both models, and OpenKey pricing adds the flat 3% fee on top — $0.618/M for either. The split is on completion: Kimi K2 Thinking charges $2.50/M vs GLM 5's $1.92/M, which becomes $2.575/M and $1.9776/M on OpenKey. On a 10M-input/2M-output workload, Kimi K2 Thinking costs $11.00 and GLM 5 costs $9.84 — GLM 5 is about 10.5% cheaper on that mix, and the gap widens as completion tokens grow since that's where the two diverge. Cache-read pricing follows the same pattern: $0.15/M for Kimi vs $0.12/M for GLM 5. If your workload is completion-heavy (long agent traces, generated code), GLM 5's advantage compounds.

Coding and agent benchmarks

GLM 5 has Design Arena scores across 12 categories spanning both the agents arena (Android native, full-stack, Godot game dev, mobile apps) and the models arena (3D, code categories, data viz, game dev, SVG, UI components, website, ASCII art). Its best result is Godot game dev at rank 3 (1237 elo, 54.8% win rate) and Android native agent tasks at rank 6 (1244 elo, 62% win rate). Kimi K2 Thinking has one comparable data point — website category, rank 70, 1156 elo, 48.8% win rate — which is far behind GLM 5's website score of rank 18, 1290 elo, 55.1% win rate on the same category. On artificial_analysis metrics, Kimi K2 Thinking posts an intelligence index of 17.3, coding index of 21, and agentic index of 1.8; GLM 5 has no artificial_analysis data reported.

Context and output limits

Kimi K2 Thinking supports a 262,144-token context window against GLM 5's 202,752 — a context ratio of 1.29x in Kimi's favor. Kimi also specifies a hard max completion of 100,352 tokens; GLM 5 has no listed max completion cap in this data. If you're processing very long documents or need to guarantee a maximum output length for cost control, Kimi K2 Thinking's explicit ceiling is useful; GLM 5's larger completion budget (unspecified cap) suits open-ended agent runs where you don't want to hit a token wall mid-task.

Reasoning behavior

Kimi K2 Thinking makes reasoning mandatory on every request — you can't turn it off, which means every call carries the latency and token cost of a visible reasoning trace. GLM 5 makes reasoning optional but ships with it enabled by default, so you get the same behavior out of the box but can disable it for latency-sensitive, low-complexity calls. Both expose a `reasoning` and `include_reasoning` parameter, but only GLM 5 lets you flip it off without switching models. For agent loops calling the model many times per task, that switch matters for cost control.

Questions

Which model is cheaper for a typical agent workload?

GLM 5, on a 10M-input/2M-output workload GLM 5 costs $9.84 on OpenKey versus $11.00 for Kimi K2 Thinking. Input pricing is identical at $0.618/M on OpenKey for both; the difference comes entirely from completion tokens — $1.9776/M for GLM 5 vs $2.575/M for Kimi.

Does either model support disabling reasoning?

GLM 5 does — reasoning is enabled by default but optional, so you can turn it off for simple, latency-sensitive calls. Kimi K2 Thinking's reasoning is mandatory on every request and cannot be disabled, which adds token overhead to every call.

Which has a bigger context window?

Kimi K2 Thinking, with 262,144 tokens versus GLM 5's 202,752 — a 1.29x ratio. Kimi also caps max completion output at 100,352 tokens explicitly; GLM 5 has no stated completion cap in this data.

How do they compare on coding benchmarks?

GLM 5 has far more benchmark coverage — 12 Design Arena categories including code categories (rank 16, 1295 elo) and full-stack agents (rank 13). Kimi K2 Thinking has one comparable data point, website category at rank 70 (1156 elo), well behind GLM 5's rank 18 (1290 elo) on the same category.