GLM 5.1
Strong GLM coding model for agentic engineering, terminals, and repository generation
- Context
- 205K
- Released
- 6mo ago
- Parameters
- —
- Input / 1M tokens
- $0.97
- Output / 1M tokens
- $3.04
- Cached input / 1M tokens
- $0.18
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.
Other published benchmarks
| Evaluation | Reported score and source |
|---|---|
| FrontierMath-2025-02-28-Private | |
| FrontierMath-Tier-4-2025-07-01-Private | |
| FrontierMath-Tiers-1-3-v2-Private | |
| FrontierMath-Tiers-1-3-v2-Private | |
| GPQA diamond | |
| OTIS Mock AIME 2024-2025 | |
| SimpleQA Verified | |
| SWE-Bench verified |
Benchmark compilation: Epoch AI, Capabilities & benchmarking (CC BY attribution). Scores are converted to percentages and model names matched to the Vertical Tabs catalog. Individual evaluations are credited to their original sources. External results may carry separate licences and evaluation configurations; do not interpret scores from different harnesses as directly comparable.
Specifications
- Developer
- Z.ai
- Model family
- GLM
- Type
- Language model
- Predecessor
- —
- Successor
- —
- Released
- April 7, 2026
- Model ID
z-ai/glm-5.1- Openness
- Open weights
- License
- —
- Parameters
- —
- Input modalities
- text
- Output modalities
- text
- Modality
- text->text
- Tokenizer
- Other
- Max output tokens
- 128K
- Hugging Face
- zai-org/GLM-5.1
- Source freshness
- Metadata synced Oct 8, 2026, 10:17 PM UTC
Additional source evidence
- Source model
- zhipuai/glm-5.1
- Source family
- glm
- Last updated
- 2026-04-07
- Model context
- 200K
- Model output limit
- 131K
- Open weights
- Yes
- File attachments
- No
- Reasoning
- Yes
- Tool calling
- Yes
- Structured output
- Yes
- Temperature control
- Yes
Downloadable weights
Published benchmarks
Complete source record (JSON)
{
"id": "zhipuai/glm-5.1",
"name": "GLM-5.1",
"description": "Strong GLM coding model for agentic engineering, terminals, and repository generation",
"family": "glm",
"last_updated": "2026-04-07",
"attachment": false,
"reasoning": true,
"tool_call": true,
"structured_output": true,
"temperature": true,
"modalities": {
"input": [
"text"
],
"output": [
"text"
]
},
"limit": {
"context": 200000,
"output": 131072
},
"benchmarks": [
{
"name": "Artificial Analysis Coding Agent Index",
"score": 52.7,
"source": "https://artificialanalysis.ai/agents/coding-agents",
"metric": "average pass@1",
"harness": "Claude Code"
},
{
"name": "SWE-Atlas Codebase QnA",
"score": 73.2,
"source": "https://artificialanalysis.ai/agents/coding-agents",
"metric": "pass@1",
"harness": "Claude Code"
},
{
"name": "SWE-Bench Pro",
"score": 19.8,
"source": "https://artificialanalysis.ai/agents/coding-agents",
"metric": "pass@1",
"harness": "Claude Code",
"dataset": "hard-aa"
},
{
"name": "Terminal-Bench",
"score": 65.1,
"source": "https://artificialanalysis.ai/agents/coding-agents",
"metric": "pass@1",
"harness": "Claude Code",
"version": "2.1"
}
],
"release_date": "2026-04-07",
"open_weights": true,
"weights": [
{
"label": "Hugging Face",
"url": "https://huggingface.co/zai-org/GLM-5.1"
}
]
}Infrastructure
Provider availability
| Provider | Endpoint | Context | Input /M tokens | Output /M tokens | Cached input /M tokens | Cache write /M tokens | Uptime 1d | Data handling |
|---|---|---|---|---|---|---|---|---|
| streamlake/fp8 | 200K | $0.97 | $3.04 | $0.18 | — | 98.87% | ZDR: NoTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 | |
| chutes/fp8 | 203K | $0.98 | $3.08 | $0.10 | — | 41.82% | ZDR: NoTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 | |
| siliconflow/fp8 | 205K | $1.19 | $3.74 | $0.60 | — | 99.93% | ZDR: YesTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 | |
| phala | 203K | $1.21 | $4.20 | $0.60 | — | 43.75% | ZDR: YesTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 | |
| atlas-cloud/fp8 | 203K | $1.26 | $3.96 | $0.23 | — | 100.00% | ZDR: NoTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 | |
| alibaba/fp8 | 203K | $1.33 | $4.18 | $0.25 | — | 96.55% | ZDR: NoTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 | |
| novita/fp8 | 205K | $1.38 | $4.40 | $0.26 | — | 100.00% | ZDR: YesTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 | |
| baidu/fp8 | 203K | $1.40 | $4.40 | $0.26 | — | 100.00% | ZDR: NoTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 | |
| friendli | 203K | $1.40 | $4.40 | $0.26 | — | 100.00% | ZDR: NoTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 | |
| gmicloud/fp8 | 203K | $1.40 | $4.40 | $0.26 | — | 100.00% | ZDR: NoTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 | |
| nebius/fp8 | 203K | $1.40 | $4.40 | — | — | 100.00% | ZDR: YesTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 | |
| z-ai/fp8 | 203K | $1.40 | $4.40 | $0.26 | — | 99.93% | ZDR: YesTraining: Unknown Prompt logging: Unknown Retention: Unknown Checked 2026-10-15 | |
| venice/fp8 | 200K | $1.40 | $4.40 | $0.26 | — | 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.