Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | GLM-5.1 (Reasoning) | Kimi K2.7 Code |
|---|---|---|
| Input ($/M tokens) | $1.28 | $0.95 |
| Output ($/M tokens) | $4.07 | $4 |
Verdict. Kimi K2.7 Code takes the aggregate benchmark matchup 2–0 across 2 categories. Real workloads usually care about a handful of specific tasks — see the per-benchmark table above.
Pricing. Both models sit in the mid-tier bracket for output-token pricing. At 1.0× the per-million-token cost, Kimi K2.7 Code is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). Kimi K2.7 Code makes more sense when output volume is low and absolute reasoning quality justifies the premium.
Strengths. GLM-5.1 (Reasoning) is strongest on Coding Index (55.8), Intelligence Index (41.0). Kimi K2.7 Code leads on Coding Index (60.8), Intelligence Index (43.0).
Speed. On throughput, GLM-5.1 (Reasoning) generates tokens at 51 tok/s versus 39 tok/s — about 23% faster. On time-to-first-token, GLM-5.1 (Reasoning) responds in 1820ms vs 2970ms, which matters most for chat-style UIs.
Provider. Z AI and Kimi sell to overlapping but distinct developer audiences: Z AI tends to ship frontier reasoning models with premium positioning, while Kimi often prices more aggressively. Your existing vendor relationships, billing, and SLA preferences may matter as much as the raw numbers above.
Workload cost. Workload scenarios (per million requests at 30M input + 15M output tokens): GLM-5.1 (Reasoning) costs $99.45 ($1193/year); Kimi K2.7 Code costs $88.50 ($1062/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): GLM-5.1 (Reasoning) ≈ $14.54/run, Kimi K2.7 Code ≈ $12.75/run. At agent/realtime scale (200M input / 100M output per million requests): GLM-5.1 (Reasoning) ≈ $663/run, Kimi K2.7 Code ≈ $590/run. Kimi K2.7 Code becomes more attractive at higher volume — the absolute per-token pricing difference compounds when you ship at scale.
Recommendation. Both models have legitimate use cases — the right answer depends on whether you are optimizing for benchmark ceiling, latency, or unit cost. Start with the cheaper / faster model, evaluate against your specific task, and only switch if the upgrade shows a meaningful lift.
Head-to-head deltas
Data from Artificial Analysis API — 12 benchmarks
Kimi K2.7 Code is cheaper overall. Its blended price (3:1 input/output ratio) is $1.71/M tokens vs $1.98/M for GLM-5.1 (Reasoning).
Kimi K2.7 Code wins 2 out of 12 benchmarks compared to 0 for GLM-5.1 (Reasoning). See the detailed benchmark chart above for per-category results.
GLM-5.1 (Reasoning) generates tokens faster at 51 tok/s vs 39 tok/s. GLM-5.1 (Reasoning) also has lower time-to-first-token (1.82s vs 2.97s).
Choose based on your priorities: Kimi K2.7 Code for lower cost, Kimi K2.7 Code for stronger benchmark performance, and GLM-5.1 (Reasoning) for faster generation. For latency-sensitive apps, check the TTFT comparison above.