Z.ai

GLM 4.6

Tool callingStructured outputReasoning
GLM 4.6 model card cover

Late GLM-4 workhorse for coding agents, reasoning, and structured tasks

Context
205K
Released
1y ago
Parameters
—
Input / 1M tokens
$0.50
Output / 1M tokens
$2.00
Cached input / 1M tokens
$0.10

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

EvaluationReported score and source
Terminal Bench
24.5%
Terminal-Bench v2 Leaderboard · glm-4.6
Agent: Terminus 2
Terminal-Bench v2 Leaderboard
Original-source terms apply
Terminal Bench
24.5%
https://www.tbench.ai/leaderboard/terminal-bench/2.0 · glm-4.6
Agent: Terminus 2
https://www.tbench.ai/leaderboard/terminal-bench/2.0
Original-source terms apply

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
September 30, 2025
Model ID
z-ai/glm-4.6
Openness
Open weights
License
—
Parameters
—
Input modalities
text
Output modalities
text
Modality
text->text
Tokenizer
Other
Max output tokens
131K
Knowledge cutoff
2025-04
Hugging Face
zai-org/GLM-4.6
Source freshness
Metadata synced Oct 8, 2026, 10:17 PM UTC
Additional source evidence
Source model
zhipuai/glm-4.6
Source family
glm
Last updated
2025-09-30
Knowledge cutoff
2025-04
Model context
205K
Model output limit
131K
Open weights
Yes
File attachments
No
Reasoning
Yes
Tool calling
Yes
Temperature control
Yes

Downloadable weights

Published benchmarks

BenchmarkScoreEvaluation detailsEvidence
Artificial Analysis Coding Index29.5 · index2026-05-22Source
SciCode38.4 · percent correct2026-05-22Source
Terminal-Bench Hard25 · success rate2026-05-22Source
SWE-Bench Pro9.67 · resolve ratepublicSource
Complete source record (JSON)
{
  "id": "zhipuai/glm-4.6",
  "name": "GLM-4.6",
  "description": "Late GLM-4 workhorse for coding agents, reasoning, and structured tasks",
  "family": "glm",
  "last_updated": "2025-09-30",
  "knowledge": "2025-04",
  "attachment": false,
  "reasoning": true,
  "tool_call": true,
  "temperature": true,
  "modalities": {
    "input": [
      "text"
    ],
    "output": [
      "text"
    ]
  },
  "limit": {
    "context": 204800,
    "output": 131072
  },
  "benchmarks": [
    {
      "name": "Artificial Analysis Coding Index",
      "score": 29.5,
      "source": "https://openrouter.ai/z-ai/glm-4.6/benchmarks",
      "metric": "index",
      "date": "2026-05-22"
    },
    {
      "name": "SciCode",
      "score": 38.4,
      "source": "https://openrouter.ai/z-ai/glm-4.6/benchmarks",
      "metric": "percent correct",
      "date": "2026-05-22"
    },
    {
      "name": "Terminal-Bench Hard",
      "score": 25,
      "source": "https://openrouter.ai/z-ai/glm-4.6/benchmarks",
      "metric": "success rate",
      "date": "2026-05-22"
    },
    {
      "name": "SWE-Bench Pro",
      "score": 9.67,
      "source": "https://labs.scale.com/leaderboard/swe_bench_pro_public",
      "metric": "resolve rate",
      "dataset": "public"
    }
  ],
  "release_date": "2025-09-30",
  "open_weights": true,
  "weights": [
    {
      "label": "Hugging Face",
      "url": "https://huggingface.co/zai-org/GLM-4.6"
    }
  ]
}

Infrastructure

Provider availability

All providers
ProviderEndpointContextInput /M tokensOutput /M tokensCached input /M tokensCache write /M tokensUptime 1dData handling
Venicevenice/fp4198K$0.43$1.75$0.08—100.00%
ZDR: Yes

Training: Unknown

Prompt logging: Unknown

Retention: Unknown

Checked 2026-10-15

DeepInfradeepinfra/fp4203K$0.50$2.00$0.10—99.86%
ZDR: Yes

Training: Unknown

Prompt logging: Unknown

Retention: Unknown

Checked 2026-10-15

Novitanovita/bf16205K$0.55$2.20$0.11—100.00%
ZDR: Yes

Training: Unknown

Prompt logging: Unknown

Retention: Unknown

Checked 2026-10-15

Z.AIz-ai/fp4203K$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