Model comparison
GLM-4.1V-9B-Thinking vs Qwen2.5-Omni
- Input
- $0.035 / 1M tokens
- Output
- $0.14 / 1M tokens
- Context
- 64K
- Released
- 2025-07-01
- Input
- $0.10
- Output
- $0.40
- Context
- 32K
- Released
- 2025-03-26
GLM-4.1V-9B-Thinking scores higher on 2 of the 2 benchmarks both models report; GLM-4.1V-9B-Thinking is cheaper on input tokens ($0 vs $0.1 per 1M, 2.9×); GLM-4.1V-9B-Thinking has the larger context window (64K vs 32K).
Specs and pricing side by side
| Spec | GLM-4.1V-9B-Thinking | Qwen2.5-Omni |
|---|---|---|
| Maker | Z.ai (Zhipu / GLM) | Alibaba (Qwen) |
| Released | 2025-07-01 | 2025-03-26 |
| Context window | 64K | 32K |
| Max output | 8K | not published |
| Input price | $0.035 / 1M tokens | $0.10 |
| Output price | $0.14 / 1M tokens | $0.40 |
| Parameters | 9B | 7B base LLM (~11B total with Talker); also a 3B variant |
| License | MIT | Apache-2.0 |
| Weights | Open weights | Open weightssame |
| Modalities | Text, Vision, Video | Text, Vision, Audio, Video |
| Knowledge cutoff | Not disclosed | not published |
Benchmarks both models report
| Benchmark | GLM-4.1V-9B-Thinking | Qwen2.5-Omni |
|---|---|---|
| MMStar | 72.9% | 64% |
| MMMU | 68% | 59.2% |
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-4.1V-9B-Thinking if…
- You care about MMStar, MMMU (it scores higher on 2 shared benchmarks).
- Cost matters: input tokens cost 2.9× less ($0 vs $0.1 per 1M).
- You need the longer context (64K vs 32K).
- Your use case is: solving STEM and math problems presented as images, diagrams, or charts.
Pick Qwen2.5-Omni if…
- You need Audio input, which GLM-4.1V-9B-Thinking lacks.
- Your use case is: real-time voice assistants and conversational agents that listen and talk back.
FAQ
Which is better, GLM-4.1V-9B-Thinking or Qwen2.5-Omni?
GLM-4.1V-9B-Thinking scores higher on 2 of the 2 benchmarks both models report; GLM-4.1V-9B-Thinking is cheaper on input tokens ($0 vs $0.1 per 1M, 2.9×); GLM-4.1V-9B-Thinking has the larger context window (64K vs 32K). 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-4.1V-9B-Thinking: $0.035 / 1M tokens input, $0.14 / 1M tokens output. Qwen2.5-Omni: $0.10 input, $0.40 output (list API prices as published by each maker).
What are the context windows of GLM-4.1V-9B-Thinking and Qwen2.5-Omni?
GLM-4.1V-9B-Thinking offers 64K with up to 8K output; Qwen2.5-Omni offers 32K.
Is GLM-4.1V-9B-Thinking newer than Qwen2.5-Omni?
GLM-4.1V-9B-Thinking was released 2025-07-01; Qwen2.5-Omni was released 2025-03-26.