comparison
Kimi K2 Thinking vs GLM 4.6
Token pricing, context window and real monthly cost, side by side. GLM 4.6 is the cheaper of the two for a typical workload — about 1.2× less.
Positioned by published specs — size, context and modality — not measured performance; a smaller model can sometimes outperform a larger one on your task.
Kimi K2 Thinking is one class larger (Flagship vs Mid). Lean to the cheaper GLM 4.6 unless your task is demanding enough to need the larger class.
| metric | Kimi K2 Thinking | GLM 4.6 |
|---|---|---|
| Input / 1M | $0.60 | $0.50 |
| Output / 1M | $2.50 | $2.00 |
| Context | 262K | 205K |
| Technical class | Flagship | Mid |
| Cost @ typical workload | $430/mo | $350/mo |
| Modality | Text only | Text only |
| Price source | routed | routed |
| Provider | Moonshot | Z.AI |
Snapshot . Cost uses a typical workload; tune it in the calculator. How we measure →
Which should you pick?
On a typical workload, GLM 4.6 costs $350/mo against Kimi K2 Thinking's $430/mo — roughly 1.2× cheaper. But the ranking depends on your output-to-input ratio: output is the pricier direction for both, so an output-heavy job (code generation, long answers) widens the gap while an input-heavy one (summarization, retrieval) narrows it. If you need to fit more in a single prompt, Kimi K2 Thinking has the larger 262K-token window (~393 pages). By technical class (size and context, not measured capability), Kimi K2 Thinking is a Flagship and GLM 4.6 a Mid — so the lower price partly reflects a smaller class, not just a discount.
These are list and routed market prices, not measured outcomes. Two models at the same rate can still cost different amounts to finish the same task, because verbose or reasoning-heavy models emit more tokens. That gap is exactly what measured cost-per-task captures. The technical-class read above is likewise spec-based — size, context and modality, not measured performance — so a smaller model can still outperform a larger one on your specific task.
Frequently asked questions
Is Kimi K2 Thinking or GLM 4.6 cheaper?
For a typical workload (1,500 input + 500 output tokens × 200,000 requests/month), GLM 4.6 costs $350/mo versus $430/mo for Kimi K2 Thinking — about 1.2× less. Because output is priced higher than input, the winner can flip if your workload writes much more or less than this; check your own numbers in the calculator.
What's the main difference between Kimi K2 Thinking and GLM 4.6?
On price, Kimi K2 Thinking is $0.60/$2.50 per 1M (in/out) and GLM 4.6 is $0.50/$2.00. By technical class (size & context) it's Flagship (Kimi K2 Thinking) versus Mid (GLM 4.6). Kimi K2 Thinking has the larger context window at 262K tokens.
More comparisons
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- Claude Haiku 4.5 vs GLM 4.6
- Claude Haiku 4.5 vs Kimi K2 Thinking
Related
- Kimi K2 Thinking and GLM 4.6 — full specs and price history.
- API cost calculator — compare on your own workload.
- All comparisons — the full head-to-head index.