GLM 4.5
Hybrid-reasoning GLM release that made the 4.5 line broadly useful
- Context
- 131K
- Released
- 1y ago
- Parameters
- —
- Input / 1M tokens
- $0.60
- Output / 1M tokens
- $2.20
- Cached input / 1M tokens
- $0.11
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
- July 28, 2025
- Model ID
z-ai/glm-4.5- Openness
- Open weights
- License
- —
- Parameters
- —
- Input modalities
- text
- Output modalities
- text
- Modality
- text->text
- Tokenizer
- Other
- Max output tokens
- 98K
- Knowledge cutoff
- 2025-04
- Hugging Face
- zai-org/GLM-4.5
- Source freshness
- Metadata synced Oct 8, 2026, 10:17 PM UTC
Additional source evidence
- Source model
- zhipuai/glm-4.5
- Source family
- glm
- Last updated
- 2025-07-28
- Knowledge cutoff
- 2025-04
- Model context
- 131K
- Model output limit
- 98K
- Open weights
- Yes
- File attachments
- No
- Reasoning
- Yes
- Tool calling
- Yes
- Temperature control
- Yes
Downloadable weights
Published benchmarks
Complete source record (JSON)
{
"id": "zhipuai/glm-4.5",
"name": "GLM-4.5",
"description": "Hybrid-reasoning GLM release that made the 4.5 line broadly useful",
"family": "glm",
"last_updated": "2025-07-28",
"knowledge": "2025-04",
"attachment": false,
"reasoning": true,
"tool_call": true,
"temperature": true,
"modalities": {
"input": [
"text"
],
"output": [
"text"
]
},
"limit": {
"context": 131072,
"output": 98304
},
"benchmarks": [
{
"name": "Artificial Analysis Coding Index",
"score": 26.3,
"source": "https://openrouter.ai/z-ai/glm-4.5/benchmarks",
"metric": "index",
"date": "2026-03-11"
},
{
"name": "SciCode",
"score": 34.8,
"source": "https://openrouter.ai/z-ai/glm-4.5/benchmarks",
"metric": "percent correct",
"date": "2026-03-11"
},
{
"name": "Terminal-Bench Hard",
"score": 22,
"source": "https://openrouter.ai/z-ai/glm-4.5/benchmarks",
"metric": "success rate",
"date": "2026-03-11"
}
],
"release_date": "2025-07-28",
"open_weights": true,
"weights": [
{
"label": "Hugging Face",
"url": "https://huggingface.co/zai-org/GLM-4.5"
}
]
}Infrastructure
Provider availability
| Provider | Endpoint | Context | Input /M tokens | Output /M tokens | Cached input /M tokens | Cache write /M tokens | Uptime 1d | Data handling |
|---|---|---|---|---|---|---|---|---|
| z-ai/fp8 | 131K | $0.60 | $2.20 | $0.11 | — | 100.00% | 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.