Z.ai

GLM 4.5

GLM 4.5 model card cover

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

BenchmarkScoreEvaluation detailsEvidence
Artificial Analysis Coding Index26.3 · index2026-03-11Source
SciCode34.8 · percent correct2026-03-11Source
Terminal-Bench Hard22 · success rate2026-03-11Source
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

All providers
ProviderEndpointContextInput /M tokensOutput /M tokensCached input /M tokensCache write /M tokensUptime 1dData handling
Z.AIz-ai/fp8131K$0.60$2.20$0.11—100.00%
ZDR: Yes

Training: 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.

Browse hardware