Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | Kimi K2.6 | GLM-5.2 (Non-reasoning) |
|---|---|---|
| Input ($/M tokens) | $0.95 | $1.351 |
| Output ($/M tokens) | $4 | $4.25 |
Verdict. Kimi K2.6 wins the overall benchmark matchup 7–0 across 7 overlapping categories, but raw benchmark score is only one input to the decision.
Pricing. Both models sit in the mid-tier bracket for output-token pricing. At 0.9× the per-million-token cost, Kimi K2.6 is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). Kimi K2.6 makes more sense when output volume is low and absolute reasoning quality justifies the premium.
Strengths. Kimi K2.6 is strongest on GPQA Diamond (91%), IFBench (76%), Coding Index (61.8). GLM-5.2 (Non-reasoning) leads on GPQA Diamond (69%), Coding Index (46.5), SciCode (36%).
Speed. On throughput, GLM-5.2 (Non-reasoning) generates tokens at 86 tok/s versus 0 tok/s — about 100% faster. On time-to-first-token, Kimi K2.6 responds in 0ms vs 1860ms, which matters most for chat-style UIs.
Provider. Kimi and Z AI sell to overlapping but distinct developer audiences: Kimi tends to ship frontier reasoning models with premium positioning, while Z AI 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): Kimi K2.6 costs $88.50 ($1062/year); GLM-5.2 (Non-reasoning) costs $104.28 ($1251/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): Kimi K2.6 ≈ $12.75/run, GLM-5.2 (Non-reasoning) ≈ $15.26/run. At agent/realtime scale (200M input / 100M output per million requests): Kimi K2.6 ≈ $590/run, GLM-5.2 (Non-reasoning) ≈ $695/run. Kimi K2.6 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 K2.6 is cheaper overall. Its blended price (3:1 input/output ratio) is $1.71/M tokens vs $2.08/M for GLM-5.2 (Non-reasoning).
Kimi K2.6 wins 7 out of 12 benchmarks compared to 0 for GLM-5.2 (Non-reasoning). See the detailed benchmark chart above for per-category results.
GLM-5.2 (Non-reasoning) generates tokens faster at 86 tok/s vs 0 tok/s. Kimi K2.6 also has lower time-to-first-token (0.00s vs 1.86s).
Choose based on your priorities: Kimi K2.6 for lower cost, Kimi K2.6 for stronger benchmark performance, and GLM-5.2 (Non-reasoning) for faster generation. For latency-sensitive apps, check the TTFT comparison above.