GLM 4.6V
GLM vision model for visual reasoning, documents, and multimodal agents
- Context
- 131K
- Released
- 10mo ago
- Parameters
- —
- Input / 1M tokens
- $0.30
- Output / 1M tokens
- $0.90
- Cached input / 1M tokens
- $0.06
Sources:models.devOpenRouter
Benchmarks
DeepSWE is the primary coding benchmark where an exact model evaluation exists. Other sourced evaluations appear below; missing scores are not zero.
Not evaluated in DeepSWE 1.1 as of 2026-09-22. Results are only shown for exact model versions in the official dataset. Official leaderboard.
No benchmark results have been matched to this model yet.
Specifications
- Developer
- Z.ai
- Model family
- GLM
- Type
- Language model
- Predecessor
- —
- Successor
- —
- Released
- December 8, 2025
- Model ID
z-ai/glm-4.6v- Openness
- Open weights
- License
- —
- Parameters
- —
- Input modalities
- image, text, video
- Output modalities
- text
- Modality
- text+image+video->text
- Tokenizer
- Other
- Max output tokens
- 33K
- Knowledge cutoff
- 2025-04
- Hugging Face
- zai-org/GLM-4.6V
- Source freshness
- Metadata synced Oct 8, 2026, 10:17 PM UTC
Additional source evidence
- Source model
- zhipuai/glm-4.6v
- Source family
- glm
- Last updated
- 2025-12-08
- Knowledge cutoff
- 2025-04
- Model context
- 128K
- Model output limit
- 33K
- Open weights
- Yes
- File attachments
- Yes
- Reasoning
- Yes
- Tool calling
- Yes
- Temperature control
- Yes
Downloadable weights
Complete source record (JSON)
{
"id": "zhipuai/glm-4.6v",
"name": "GLM-4.6V",
"description": "GLM vision model for visual reasoning, documents, and multimodal agents",
"family": "glm",
"last_updated": "2025-12-08",
"knowledge": "2025-04",
"attachment": true,
"reasoning": true,
"tool_call": true,
"temperature": true,
"modalities": {
"input": [
"text",
"image",
"video"
],
"output": [
"text"
]
},
"limit": {
"context": 128000,
"output": 32768
},
"release_date": "2025-12-08",
"open_weights": true,
"weights": [
{
"label": "Hugging Face",
"url": "https://huggingface.co/zai-org/GLM-4.6V"
}
]
}Infrastructure
Provider availability
| Provider | Endpoint | Context | Input /M tokens | Output /M tokens | Cached input /M tokens | Cache write /M tokens | Uptime 1d | Data handling |
|---|---|---|---|---|---|---|---|---|
| novita/bf16 | 131K | $0.30 | $0.90 | $0.06 | — | 100.00% | ZDR: YesTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 | |
| z-ai/fp8 | 131K | $0.30 | $0.90 | $0.05 | — | 98.74% | ZDR: YesTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 |
Provider data:OpenRouter
Deployment graph
Can I run this?
Vertical Tabs only shows compatibility when it has structured evidence tying this exact model to hardware, runtime, quantization, memory use, and observed performance.