AI model comparison
GLM 4.5 vs R1 0528
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
DeepSeek
R1 0528
May 28th update to the [original DeepSeek R1](/deepseek/deepseek-r1) Performance on par with [OpenAI o1](/openai/o1), but open-sourced and with fully open reasoning tokens.
- Input / 1M
- $0.50
- Output / 1M
- $2.15
- Context
- 164K
At a glance
Quick verdict
How the two models stack up on the things people ask about most.
Lower price
R1 0528
1.1x cheaper for a typical chat
Higher intelligence score
R1 0528
13.1 vs 12.8 on Artificial Analysis
Larger context window
R1 0528
164K 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 R1 0528 API pricing
Per-token API rates. Cheaper option highlighted.
| Metric | ||
|---|---|---|
| Input tokensPer 1M tokens | $0.60 | $0.50 |
| Output tokensPer 1M tokens | $2.20 | $2.15 |
| Cached input (read)Per 1M tokens | $0.11 | $0.35 |
| 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 | 164K tokens |
| Max output | 98K tokens | 33K 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 | May 28, 2025 |
Features
Supported features
API features available for each model.
| Metric | ||
|---|---|---|
| Tool calling | Supported | Supported |
| Structured outputs | Not supported | Supported |
| JSON mode | Supported | Supported |
| Reasoning | Supported | Supported |
| Temperature | Supported | Supported |
| Stop sequences | Not supported | Supported |
| Deterministic seed | Not supported | Supported |
| Verbosity control | Not supported | Not supported |
Our take
R1 0528 is 1.1x cheaper for a typical chat. R1 0528 scores higher on the Artificial Analysis Intelligence Index (13.1 vs 12.8). R1 0528 has the larger context window (164K vs 131K tokens). 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 R1 0528 cheaper?
R1 0528 is cheaper for a typical chat (2K input tokens and 500 output tokens). R1 0528 costs $0.50 per 1M input tokens and $2.15 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 R1 0528?
R1 0528 supports up to 164K tokens of context, compared with 131K for GLM 4.5.
Is GLM 4.5 smarter than R1 0528?
On the Artificial Analysis Intelligence Index, GLM 4.5 scores 12.8 and R1 0528 scores 13.1, putting R1 0528 ahead. Benchmarks don't capture everything, so the best test is running your own prompts through both.
Can I use GLM 4.5 and R1 0528 at the same time?
Yes. Shortcut Chat sends one prompt to multiple models at once, so you can see GLM 4.5 and R1 0528 answer side by side and keep the better response.
Why choose? Ask both.
Send one prompt to GLM 4.5 and R1 0528 at the same time. Compare the answers side by side and keep the best one.