AI model comparison
GLM 4.5 vs Kimi K2 0711
Pricing, context window, benchmarks, and features compared side by side. Or skip the guesswork and send one prompt to both.
Z.ai
GLM 4.5
GLM-4.5 is our latest flagship foundation model, purpose-built for agent-based applications. It leverages a Mixture-of-Experts (MoE) architecture and supports a context length of up to 128k tokens.
GLM 4.5 is scheduled to be retired on December 31, 2026. Consider GLM 5.3 instead.
- Input / 1M
- $0.60
- Output / 1M
- $2.20
- Context
- 131K
Moonshot AI
Kimi K2 0711
Kimi K2 Instruct is a large-scale Mixture-of-Experts (MoE) language model developed by Moonshot AI, featuring 1 trillion total parameters with 32 billion active per forward pass.
- Input / 1M
- $0.57
- Output / 1M
- $2.30
- Context
- 131K
At a glance
Quick verdict
How the two models stack up on the things people ask about most.
Lower price
Kimi K2 0711
1.0x cheaper for a typical chat
Higher intelligence score
GLM 4.5
12.8 vs 12.7 on Artificial Analysis
Larger context window
Tie
131K vs 131K tokens
Newer release
GLM 4.5
Released July 25, 2025
Benchmarks
Benchmark scores
Independent scores from Artificial Analysis. Higher is better.
Intelligence index
Coding index
Agentic index
Pricing
GLM 4.5 vs Kimi K2 0711 API pricing
Per-token API rates. Cheaper option highlighted.
| Metric | ||
|---|---|---|
| Input tokensPer 1M tokens | $0.60 | $0.57 |
| Output tokensPer 1M tokens | $2.20 | $2.30 |
| Cached input (read)Per 1M tokens | $0.11 | Not available |
| Cache writePer 1M tokens | Not available | Not available |
What it costs in practice
Estimated cost per 1,000 requests at standard rates. Coding and long-document figures also show the cost when the input is already cached.
Chat message
2K in, 500 out
Coding task
30K in, 4K out
Long document summary
150K in, 2K out
Specs
Context window and capabilities
How much each model can read, how much it can write, and what it accepts as input.
| Metric | ||
|---|---|---|
| Context window | 131K tokens | 131K tokens |
| Max output | 98K tokens | 98K tokens |
| Input types | Text | Text |
| Output types | Text | Text |
| Reasoning effort levels | Not available | Not available |
| Default reasoning effort | Not available | Not available |
| Release date | July 25, 2025 | July 11, 2025 |
Features
Supported features
API features available for each model.
| Metric | ||
|---|---|---|
| Tool calling | Supported | Supported |
| Structured outputs | Not supported | Not supported |
| JSON mode | Supported | Not supported |
| Reasoning | Supported | Not supported |
| Temperature | Supported | Supported |
| Stop sequences | Not supported | Supported |
| Deterministic seed | Not supported | Supported |
| Verbosity control | Not supported | Not supported |
Our take
Kimi K2 0711 is 1.0x cheaper for a typical chat. GLM 4.5 scores higher on the Artificial Analysis Intelligence Index (12.8 vs 12.7). The right pick depends on your workload, so the quickest way to settle it is to send the same prompt to both and compare.
FAQ
Frequently asked questions
Is GLM 4.5 or Kimi K2 0711 cheaper?
Kimi K2 0711 is cheaper for a typical chat (2K input tokens and 500 output tokens). Kimi K2 0711 costs $0.57 per 1M input tokens and $2.30 per 1M output tokens, while GLM 4.5 costs $0.60 per 1M input tokens and $2.20 per 1M output tokens.
Which has a bigger context window, GLM 4.5 or Kimi K2 0711?
GLM 4.5 supports up to 131K tokens of context, compared with 131K for Kimi K2 0711.
Is GLM 4.5 smarter than Kimi K2 0711?
On the Artificial Analysis Intelligence Index, GLM 4.5 scores 12.8 and Kimi K2 0711 scores 12.7, putting GLM 4.5 ahead. Benchmarks don't capture everything, so the best test is running your own prompts through both.
Can I use GLM 4.5 and Kimi K2 0711 at the same time?
Yes. Shortcut Chat sends one prompt to multiple models at once, so you can see GLM 4.5 and Kimi K2 0711 answer side by side and keep the better response.
Why choose? Ask both.
Send one prompt to GLM 4.5 and Kimi K2 0711 at the same time. Compare the answers side by side and keep the best one.