GLM-5 vs Kimi K2.6
Detailed pricing comparison and cost analysis.
Updated September 2026
Cost Simulator
| Feature | GLM-5 | Kimi K2.6 |
|---|---|---|
| Provider | Zhipu AI | Moonshot AI |
| Input Price (1M) | $1.00 | $0.95 |
| Output Price (1M) | $3.20 | $4.00 |
| Context Window | 1,000,000 | 262,144 |
Verdict
GLM-5 costs $1.00 per 1M input tokens and $3.20 per 1M output tokens. Kimi K2.6 costs $0.95 per 1M input tokens and $4.00 per 1M output tokens. Kimi K2.6 is 5% cheaper on input tokens than GLM-5. For output tokens, GLM-5 is the more affordable option at $3.20/1M vs $4.00.
On context window, GLM-5 supports 1,000,000 tokens — meaning it can fit more conversation history, documents, or code in a single request. This matters for RAG pipelines, long document analysis, and agentic workflows where context builds up over many turns.
When to choose GLM-5
- ✓ Your workload is output-heavy — GLM-5 generates text cheaper
- ✓ You need a larger context window (1,000,000 tokens)
- ✓ You are already integrated with Zhipu AI
When to choose Kimi K2.6
- ✓ You need the lowest input token cost ($ 0.95/1M)
- ✓ You are already integrated with Moonshot AI
Use the calculator above to simulate your specific workload and find the exact break-even point. For most applications, the cheapest model is the one that minimises your total monthly bill given your input-to-output token ratio.
Frequently Asked Questions
Is GLM-5 cheaper than Kimi K2.6? ▼
Kimi K2.6 is cheaper on input tokens at $0.95/1M vs $1.00/1M for GLM-5 — a 5% saving.
What is the context window of GLM-5 vs Kimi K2.6? ▼
GLM-5 has a 1,000,000-token context window. Kimi K2.6 has a 262,144-token context window. GLM-5 supports the larger context, suitable for longer documents and agentic workflows.
Which model is better: GLM-5 or Kimi K2.6? ▼
The best choice depends on your use case. For cost efficiency on input tokens, Kimi K2.6 is the cheaper option. For maximum context length, GLM-5 supports 1,000,000 tokens. Use the comparison table above to find the right fit for your workload.