GLM 4.5V
Z.ai · released Aug 11, 2025
GLM-4.5V is a vision-language foundation model for multimodal agent applications. Built on a Mixture-of-Experts (MoE) architecture with 106B parameters and 12B activated parameters, it achieves state-of-the-art results in video understanding,...
Specification
- Context window
- 66K
- Max output
- 16K
- Knowledge cutoff
- Dec 31, 2024
- Parameters
- 107.7B
- Licence
- mit
- Serving providers
- 2
- Moderated
- No
Intelligence
6.8
23th percentile
Coding
—
Coding Index
Agentic
—
Agentic Index
Output speed
42 t/s
Median across providers
Latency
2.01s
Time to first token
Cost per task
$0.17
Estimated
Benchmarks
Where the score comes from
The Intelligence Index is a composite. These are the underlying evaluations this model was actually measured on.
Evaluation scores
Percentage correct · higher is better
- MMLU-Pro75.1%
- GPQA Diamond57.3%
- LiveCodeBench35.2%
- IFBench28.6%
- τ²-bench (Telecom)19.6%
- SciCode18.8%
- AIME 202515.3%
- Terminal-Bench Hard6.8%
- Humanity's Last Exam3.5%
- AA-LCR (long context)0.0%
An evaluation missing from this list was not run for this model — it is not a zero.
View as table
| Evaluation | Score |
|---|---|
| MMLU-Pro | 75.1% |
| GPQA Diamond | 57.3% |
| LiveCodeBench | 35.2% |
| IFBench | 28.6% |
| τ²-bench (Telecom) | 19.6% |
| SciCode | 18.8% |
| AIME 2025 | 15.3% |
| Terminal-Bench Hard | 6.8% |
| Humanity's Last Exam | 3.5% |
| AA-LCR (long context) | 0.0% |
Against its peers
Intelligence Index · this model highlighted, nearest peers in grey
- Mistral Large 2407Mistral AI7.0
- 6.9
- GLM 4.5VZ.ai6.8
- GPT-4OpenAI6.8
- Gemini 2.5 Flash LiteGoogle6.7
- GPT-4o-miniOpenAI6.7
- GPT-4o-mini (2024-07-18)OpenAI6.7
- Granite 4.1 8BIBM Granite6.4
Peers are the models sitting closest on the Intelligence Index — the set you would realistically choose between.
View as table
| Model | Intelligence |
|---|---|
| Mistral Large 2407 | 7.0 |
| Qwen2.5 Coder 32B Instruct | 6.9 |
| GLM 4.5V | 6.8 |
| GPT-4 | 6.8 |
| Gemini 2.5 Flash Lite | 6.7 |
| GPT-4o-mini | 6.7 |
| GPT-4o-mini (2024-07-18) | 6.7 |
| Granite 4.1 8B | 6.4 |
Percentile among all indexed models
Pricing
What it costs to run
List prices per million tokens, plus what one representative task works out to.
List price
- Input / 1M tokens
- $0.6
- Output / 1M tokens
- $1.8
- Cached input / 1M
- $0.11
- Blended 3:1
- $0.9
One task, estimated
$0.17
- Input tokens
- 50,000
- Output tokens
- 80,000
- Profile
- Reasoning
Estimated from list pricing: 50K input tokens plus 80K output tokens for reasoning models (25K for non-reasoning).