Model comparison
GLM-4.5V vs GLM-4.1V-9B-Thinking
- Input
- $0.60 / 1M tokens
- Output
- $1.80 / 1M tokens
- Context
- 64K
- Released
- 2025-08-11
- Input
- $0.035 / 1M tokens
- Output
- $0.14 / 1M tokens
- Context
- 64K
- Released
- 2025-07-01
GLM-4.5V scores higher on 5 of the 5 benchmarks both models report; GLM-4.1V-9B-Thinking is cheaper on input tokens ($0 vs $0.6 per 1M, 17.1×).
Specs and pricing side by side
| Spec | GLM-4.5V | GLM-4.1V-9B-Thinking |
|---|---|---|
| Maker | Z.ai (Zhipu / GLM) | Z.ai (Zhipu / GLM)same |
| Released | 2025-08-11 | 2025-07-01 |
| Context window | 64K | 64Ksame |
| Max output | 16K | 8K |
| Input price | $0.60 / 1M tokens | $0.035 / 1M tokens |
| Output price | $1.80 / 1M tokens | $0.14 / 1M tokens |
| Parameters | 106B total / 12B active (MoE) | 9B |
| License | MIT | MITsame |
| Weights | Open weights | Open weightssame |
| Modalities | Text, Vision, Video, PDF | Text, Vision, Video |
| Knowledge cutoff | December 2024 | Not disclosed |
Benchmarks both models report
| Benchmark | GLM-4.5V | GLM-4.1V-9B-Thinking |
|---|---|---|
| MMStar | 75.3% | 72.9% |
| MMMU Pro | 65.2% | 57.1% |
| MathVista | 84.6% | 80.7% |
| AI2D | 88.1% | 87.9% |
| OCRBench | 86.5% | 84.2% |
Scores are as published by each maker or its cited source; harness, reasoning effort and date can differ between rows, so treat small gaps as ties. Sources are listed on each model page.
Which one should you pick?
Pick GLM-4.5V if…
- You care about MMStar, MMMU Pro, MathVista (it scores higher on 5 shared benchmarks).
- You need PDF input, which GLM-4.1V-9B-Thinking lacks.
- Your use case is: document, chart and long-PDF understanding and information extraction.
Pick GLM-4.1V-9B-Thinking if…
- Cost matters: input tokens cost 17.1× less ($0 vs $0.6 per 1M).
- Your use case is: solving STEM and math problems presented as images, diagrams, or charts.
FAQ
Which is better, GLM-4.5V or GLM-4.1V-9B-Thinking?
GLM-4.5V scores higher on 5 of the 5 benchmarks both models report; GLM-4.1V-9B-Thinking is cheaper on input tokens ($0 vs $0.6 per 1M, 17.1×). The better pick depends on whether you value benchmark scores, price per token, context length or openness for your workload.
How much cheaper is GLM-4.1V-9B-Thinking?
GLM-4.5V: $0.60 / 1M tokens input, $1.80 / 1M tokens output. GLM-4.1V-9B-Thinking: $0.035 / 1M tokens input, $0.14 / 1M tokens output (list API prices as published by each maker).
What are the context windows of GLM-4.5V and GLM-4.1V-9B-Thinking?
GLM-4.5V offers 64K with up to 16K output; GLM-4.1V-9B-Thinking offers 64K with up to 8K output.
Is GLM-4.5V newer than GLM-4.1V-9B-Thinking?
GLM-4.5V was released 2025-08-11; GLM-4.1V-9B-Thinking was released 2025-07-01.