Pairwise benchmark snapshot · Aug 17, 2026noindex (thin pair)
This page compares Z.ai: GLM 4.5V (Zhipu) and MoonshotAI: Kimi K2.5 (Moonshot) using the latest snapshots we have as of Aug 17, 2026. Z.ai: GLM 4.5V wins on output price at $1.8/1M. MoonshotAI: Kimi K2.5 has the larger context window (262k tokens). Every cell below is a dated snapshot from a named public source — we do not invent missing scores, and we do not run SWE-bench ourselves.
Zhipu
Z.ai: GLM 4.5VAuto-discovered from OpenRouter (z-ai/glm-4.5v). Preview until a second source matches.
Moonshot
MoonshotAI: Kimi K2.5Auto-discovered from OpenRouter (moonshotai/kimi-k2.5). Preview until a second source matches.
Instant pairwise benchmark recalculation across 400+ LLMs
Category wins across intelligence, speed, and pricing efficiency.
Preference Elo, Coding proficiency, SWE-bench & LiveBench accuracy
Generation throughput and time to first token responsiveness
Cost per million tokens and max context window length
Each spoke is a skill. Farther from the center is better (0–100th percentile). Tap any dot to inspect details.
No shared data
No shared data
$1.05/1M cheaper
Simulate monthly production API costs based on published $/1M tokens.
Z.ai: GLM 4.5V is estimated to save $14.70/month ($176/year).
Need SWE-bench on both sides.
Lower output list price ($1.8/1M).
Need TTFT on both sides.
Larger window (262k).
MoonshotAI: Kimi K2.5 is the side marked multimodal in the catalog.
| Target Workload | Recommended Pick | Evaluation Rationale |
|---|---|---|
| Repo / coding agents | insufficient data | Need SWE-bench on both sides. |
| High-volume chat | Z.ai: GLM 4.5V | Lower output list price ($1.8/1M). |
| Voice / low-latency UI | insufficient data | Need TTFT on both sides. |
| Long-document RAG | MoonshotAI: Kimi K2.5 | Larger window (262k). |
| Screenshots / vision | MoonshotAI: Kimi K2.5 | MoonshotAI: Kimi K2.5 is the side marked multimodal in the catalog. |
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Verified head-to-head card