Pairwise benchmark snapshot · Aug 1, 2026
In this head-to-head showdown, Kimi K2.5 Max delivers higher overall intelligence and human-preferred responses (Elo 1,515 vs 1,492), while Kimi K2.5 Max leads in SWE-bench software engineering benchmarks (71.3%), while GLM-5.2 is more budget-friendly at $1.8/1M per 1M output tokens. Review the complete breakdown below to determine which model best fits your performance and budget requirements.
7 direct benchmark disciplines evaluated across capability, speed, and cost
Zhipu flagship. Strong Chinese/English coding and agents.
Moonshot flagship reasoning model with high-fidelity coding, deep thinking, and tool use across a 200k context.
Currently comparing GLM-5.2 vs Kimi K2.5 Max. Alternative family tiers available.
Simulate accuracy gains vs added response time & token cost
Reasoning models “think before answering” by generating internal reasoning tokens. Higher effort improves math, coding, and logic accuracy, but increases response delay and token costs.
Δ 23 Arena Elo pts
84 tok/s
$1.8 / 1M output
Relative percentile scores computed across all active models in the benchmark catalog.
Comparing top Western standard models with China's leading frontier rival across 6 skill dimensions. Tap any spoke or dot to inspect.
Category wins across reasoning intelligence, generation speed, and token cost.
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
| Benchmark | GLM-5.2 | Kimi K2.5 Max | Advantage Delta |
|---|---|---|---|
| Preference Elo | +23 | ||
| Coding Elo | +16 | ||
| LiveBench | +2% | ||
| SWE-bench | +2% | ||
| GPQA Diamond | +8.1% | ||
| Time to first token | — | ||
| Output speed | 84 tok/s | 72 tok/s | +12 tok/s |
| Input price | $0.5/1M | $0.5/1M | — |
| Output price | $1.8/1M | +$0.2/1M | |
| Context window | — |
Simulate monthly production API costs in USD (US Dollar).
GLM-5.2 is estimated to save $3.00/month ($36/year).
Higher SWE-bench (71.3%).
Lower output list price ($1.8/1M).
Lower TTFT (270 ms).
Larger window (200k).
Both accept images. Defaulting to the higher-Elo side (Kimi K2.5 Max).
| Target Workload | Recommended Pick | Evaluation Rationale |
|---|---|---|
| Repo / coding agents | Kimi K2.5 Max | Higher SWE-bench (71.3%). |
| High-volume chat | GLM-5.2 | Lower output list price ($1.8/1M). |
| Voice / low-latency UI | GLM-5.2 | Lower TTFT (270 ms). |
| Long-document RAG | GLM-5.2 | Larger window (200k). |
| Screenshots / vision | Kimi K2.5 Max | Both accept images. Defaulting to the higher-Elo side (Kimi K2.5 Max). |
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