Z.ai

GLM 5.2

Tool callingStructured outputReasoning
GLM 5.2 model card cover

Open flagship GLM for long-horizon coding agents and million-token context work

Context
1.0M
Released
4mo ago
Parameters
756B
Input / 1M tokens
$0.06
Output / 1M tokens
$7.00
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.

DeepSWE 1.1

Primary coding-agent benchmark · 113 tasks · Data generated 2026-09-22

Official leaderboard
43.8%
pass@1 · highest published configuration
mini-swe-agent · max reasoning
pass@4
77.0%
Average cost / attempt
$3.92
Benchmark runs
4

All published configurations

Harness / reasoningpass@1pass@4Cost / attempt
mini-swe-agent · max43.8%77.0%$3.92
mini-swe-agent · high36.3%68.1%$2.84

pass@1 is the success rate across scored attempts; pass@4 is the share of tasks solved in at least one of four attempts. Costs are reported by DeepSWE for each configuration. These results are not directly comparable to evaluations using different harnesses. Official source JSON.

Other published benchmarks

EvaluationReported score and source
ARC-AGI-2
22.8%
Third-party evaluator · glm-5.2_unknown
Reasoning effort: unknown
External evaluation via Epoch AI
Original-source terms apply
FrontierMath-Tier-4-v2-Private
29.3%
Epoch AI · glm-5.2_max
Reasoning effort: max
Epoch AI evaluation
FrontierMath-Tiers-1-3-v2-Private
54.7%
Epoch AI · glm-5.2_low
Reasoning effort: low
Epoch AI evaluation
FrontierMath-Tiers-1-3-v2-Private
59.2%
Epoch AI · glm-5.2_max
Reasoning effort: max
Epoch AI evaluation
FrontierMath-Tiers-1-3-v2-Private
42.5%
Epoch AI · glm-5.2_none
Reasoning effort: none
Epoch AI evaluation
GPQA diamond
87.9%
Epoch AI · glm-5.2_low
Reasoning effort: low
Epoch AI evaluation
GPQA diamond
91.9%
Epoch AI · glm-5.2_max
Reasoning effort: max
Epoch AI evaluation
GPQA diamond
71.2%
Epoch AI · glm-5.2_none
Reasoning effort: none
Epoch AI evaluation
OTIS Mock AIME 2024-2025
75.6%
Epoch AI · glm-5.2_low
Reasoning effort: low
Epoch AI evaluation
OTIS Mock AIME 2024-2025
86.4%
Epoch AI · glm-5.2_max
Reasoning effort: max
Epoch AI evaluation
OTIS Mock AIME 2024-2025
28.9%
Epoch AI · glm-5.2_none
Reasoning effort: none
Epoch AI evaluation
SimpleQA Verified
34.2%
Epoch AI · glm-5.2_max
Reasoning effort: max
Epoch AI evaluation
SWE-Bench verified
78.7%
Epoch AI · glm-5.2_max
Reasoning effort: max
Epoch AI evaluation

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
June 13, 2026
Model ID
z-ai/glm-5.2
Openness
Open weights
License
—
Parameters
756B
Architecture
glm5.2
Families
glm5.2
Input modalities
text
Output modalities
text
Modality
text->text
Native context
1.0M
Tokenizer
Other
Max output tokens
944K
Hugging Face
zai-org/GLM-5.2
Source freshness
Metadata synced Oct 8, 2026, 10:17 PM UTC

Open-weight builds

Ollama variants

Every published Ollama tag matched to this model, including parameter editions, instruction/base variants, and quantized builds.

1 builds
VariantParametersTypeQuantizationSizeContextInput
glm-5.2:cloud756Bcloud——976Ktext
Additional source evidence
Source model
zhipuai/glm-5.2
Source family
glm
Last updated
2026-06-13
Model context
1M
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

BenchmarkScoreEvaluation detailsEvidence
SWE-Bench Pro62.1 · resolve rate2026-06-16Source
Terminal-Bench82.7 · success rateClaude Code · 2.1 · 2026-06-16Source
FrontierSWE74.4 · dominance2026-06-16Source
Humanity's Last Exam40.5 · accuracytext-only subset · 2026-06-16Source
Humanity's Last Exam54.7 · accuracytext-only subset · with tools · 2026-06-16Source
CritPt20.9 · accuracy2026-06-16Source
AIME99.2 · accuracy2026 · 2026-06-16Source
HMMT94.4 · accuracyNovember 2025 · 2026-06-16Source
HMMT92.5 · accuracyFebruary 2026 · 2026-06-16Source
IMOAnswerBench91 · accuracy2026-06-16Source
GPQA Diamond91.2 · accuracy2026-06-16Source
NL2Repo48.9 · resolve rate2026-06-16Source
DeepSWE46.2 · resolve rate2026-06-16Source
Program Bench63.7 · score2026-06-16Source
Terminal-Bench81 · success rateTerminus 2 · 2.1 · 2026-06-16Source
PostTrainBench34.3 · score2026-06-16Source
SWE Marathon13 · resolve rate2026-06-16Source
MCP Atlas76.8 · scorepublic subset · 2026-06-16Source
Tool-Decathlon48.2 · score2026-06-16Source
Complete source record (JSON)
{
  "id": "zhipuai/glm-5.2",
  "name": "GLM-5.2",
  "description": "Open flagship GLM for long-horizon coding agents and million-token context work",
  "family": "glm",
  "last_updated": "2026-06-13",
  "attachment": false,
  "reasoning": true,
  "tool_call": true,
  "structured_output": true,
  "temperature": true,
  "modalities": {
    "input": [
      "text"
    ],
    "output": [
      "text"
    ]
  },
  "limit": {
    "context": 1000000,
    "output": 131072
  },
  "benchmarks": [
    {
      "name": "SWE-Bench Pro",
      "score": 62.1,
      "source": "https://z.ai/blog/glm-5.2",
      "metric": "resolve rate",
      "date": "2026-06-16"
    },
    {
      "name": "Terminal-Bench",
      "score": 82.7,
      "source": "https://z.ai/blog/glm-5.2",
      "metric": "success rate",
      "harness": "Claude Code",
      "version": "2.1",
      "date": "2026-06-16"
    },
    {
      "name": "FrontierSWE",
      "score": 74.4,
      "source": "https://z.ai/blog/glm-5.2",
      "metric": "dominance",
      "date": "2026-06-16"
    },
    {
      "name": "Humanity's Last Exam",
      "score": 40.5,
      "source": "https://z.ai/blog/glm-5.2",
      "metric": "accuracy",
      "dataset": "text-only subset",
      "date": "2026-06-16"
    },
    {
      "name": "Humanity's Last Exam",
      "score": 54.7,
      "source": "https://z.ai/blog/glm-5.2",
      "metric": "accuracy",
      "variant": "with tools",
      "dataset": "text-only subset",
      "date": "2026-06-16"
    },
    {
      "name": "CritPt",
      "score": 20.9,
      "source": "https://z.ai/blog/glm-5.2",
      "metric": "accuracy",
      "date": "2026-06-16"
    },
    {
      "name": "AIME",
      "score": 99.2,
      "source": "https://z.ai/blog/glm-5.2",
      "metric": "accuracy",
      "version": "2026",
      "date": "2026-06-16"
    },
    {
      "name": "HMMT",
      "score": 94.4,
      "source": "https://z.ai/blog/glm-5.2",
      "metric": "accuracy",
      "version": "November 2025",
      "date": "2026-06-16"
    },
    {
      "name": "HMMT",
      "score": 92.5,
      "source": "https://z.ai/blog/glm-5.2",
      "metric": "accuracy",
      "version": "February 2026",
      "date": "2026-06-16"
    },
    {
      "name": "IMOAnswerBench",
      "score": 91,
      "source": "https://z.ai/blog/glm-5.2",
      "metric": "accuracy",
      "date": "2026-06-16"
    },
    {
      "name": "GPQA Diamond",
      "score": 91.2,
      "source": "https://z.ai/blog/glm-5.2",
      "metric": "accuracy",
      "date": "2026-06-16"
    },
    {
      "name": "NL2Repo",
      "score": 48.9,
      "source": "https://z.ai/blog/glm-5.2",
      "metric": "resolve rate",
      "date": "2026-06-16"
    },
    {
      "name": "DeepSWE",
      "score": 46.2,
      "source": "https://z.ai/blog/glm-5.2",
      "metric": "resolve rate",
      "date": "2026-06-16"
    },
    {
      "name": "Program Bench",
      "score": 63.7,
      "source": "https://z.ai/blog/glm-5.2",
      "metric": "score",
      "date": "2026-06-16"
    },
    {
      "name": "Terminal-Bench",
      "score": 81,
      "source": "https://z.ai/blog/glm-5.2",
      "metric": "success rate",
      "harness": "Terminus 2",
      "version": "2.1",
      "date": "2026-06-16"
    },
    {
      "name": "PostTrainBench",
      "score": 34.3,
      "source": "https://z.ai/blog/glm-5.2",
      "metric": "score",
      "date": "2026-06-16"
    },
    {
      "name": "SWE Marathon",
      "score": 13,
      "source": "https://z.ai/blog/glm-5.2",
      "metric": "resolve rate",
      "date": "2026-06-16"
    },
    {
      "name": "MCP Atlas",
      "score": 76.8,
      "source": "https://z.ai/blog/glm-5.2",
      "metric": "score",
      "dataset": "public subset",
      "date": "2026-06-16"
    },
    {
      "name": "Tool-Decathlon",
      "score": 48.2,
      "source": "https://z.ai/blog/glm-5.2",
      "metric": "score",
      "date": "2026-06-16"
    }
  ],
  "release_date": "2026-06-13",
  "open_weights": true,
  "weights": [
    {
      "label": "Hugging Face",
      "url": "https://huggingface.co/zai-org/GLM-5.2"
    }
  ]
}

Infrastructure

Provider availability

All providers
ProviderEndpointContextInput /M tokensOutput /M tokensCached input /M tokensCache write /M tokensUptime 1dData handling
Relacerelace1.0M$0.02$4.00$0.02—100.00%
ZDR: Yes

Training: Unknown

Prompt logging: Unknown

Retention: Unknown

Checked 2026-10-15

Waferwafer/us1.0M$0.02$7.00$0.02—100.00%
ZDR: Yes

Training: Unknown

Prompt logging: Unknown

Retention: Unknown

Checked 2026-10-15

Morphmorph/fp81.0M$0.08$6.00$0.08—100.00%
ZDR: Yes

Training: Unknown

Prompt logging: Unknown

Retention: Unknown

Checked 2026-10-15

Cloudflarecloudflare262K$0.19$8.00$0.19—99.92%
ZDR: No

Training: Unknown

Prompt logging: Unknown

Retention: Unknown

Checked 2026-10-15

Waferwafer1.0M$0.19$8.00$0.18—99.93%
ZDR: Yes

Training: Unknown

Prompt logging: Unknown

Retention: Unknown

Checked 2026-10-15

DeepInfradeepinfra/fp41.0M$0.56$1.80$0.11—99.43%
ZDR: Yes

Training: Unknown

Prompt logging: Unknown

Retention: Unknown

Checked 2026-10-15

StreamLakestreamlake/fp81.0M$0.61$1.93$0.11—98.96%
ZDR: No

Training: Unknown

Prompt logging: Unknown

Retention: Unknown

Checked 2026-10-15

Baidubaidu/fp81.0M$0.62$1.94$0.11—99.86%
ZDR: No

Training: Unknown

Prompt logging: Unknown

Retention: Unknown

Checked 2026-10-15

CoreWeavecoreweave/fp41.0M$0.62$2.42$0.14—100.00%
ZDR: Yes

Training: Unknown

Prompt logging: Unknown

Retention: Unknown

Checked 2026-10-15

Novitanovita/fp81.0M$0.65$2.04$0.12—100.00%
ZDR: Yes

Training: Unknown

Prompt logging: Unknown

Retention: Unknown

Checked 2026-10-15

Inceptroninceptron/fp41.0M$0.69$3.15$0.14—99.39%
ZDR: Yes

Training: Unknown

Prompt logging: Unknown

Retention: Unknown

Checked 2026-10-15

DigitalOceandigitalocean1.0M$0.70$2.20$0.11—99.51%
ZDR: Yes

Training: Unknown

Prompt logging: Unknown

Retention: Unknown

Checked 2026-10-15

SiliconFlowsiliconflow/fp81.0M$0.70$2.20$0.13—100.00%
ZDR: Yes

Training: Unknown

Prompt logging: Unknown

Retention: Unknown

Checked 2026-10-15

AtlasCloudatlas-cloud/fp81.0M$0.94$2.95$0.17—100.00%
ZDR: No

Training: Unknown

Prompt logging: Unknown

Retention: Unknown

Checked 2026-10-15

Alibabaalibaba/fp81M$0.97$3.04$0.19—99.36%
ZDR: No

Training: Unknown

Prompt logging: Unknown

Retention: Unknown

Checked 2026-10-15

Alibabaalibaba/fp8/us-east-11M$1.12$3.52$0.22—85.71%
ZDR: No

Training: Unknown

Prompt logging: Unknown

Retention: Unknown

Checked 2026-10-15

Phalaphala/fp81.0M$1.26$3.00$0.22—0.00%
ZDR: Yes

Training: Unknown

Prompt logging: Unknown

Retention: Unknown

Checked 2026-10-15

BaseTenbaseten/fp81.0M$1.40$4.40$0.14—100.00%
ZDR: Yes

Training: Unknown

Prompt logging: Unknown

Retention: Unknown

Checked 2026-10-15

BaseTenbaseten/fp8/us1.0M$1.40$4.40$0.14—100.00%
ZDR: Yes

Training: Unknown

Prompt logging: Unknown

Retention: Unknown

Checked 2026-10-15

Friendlifriendli1.0M$1.40$4.40$0.26—99.93%
ZDR: No

Training: Unknown

Prompt logging: Unknown

Retention: Unknown

Checked 2026-10-15

GMICloudgmicloud/fp81.0M$1.40$4.40$0.26—100.00%
ZDR: No

Training: Unknown

Prompt logging: Unknown

Retention: Unknown

Checked 2026-10-15

Mistralmistral/zdr1.0M$1.40$4.40$0.14—100.00%
ZDR: Yes

Training: Unknown

Prompt logging: Unknown

Retention: Unknown

Checked 2026-10-15

Nebiusnebius/fp41.0M$1.40$4.40——95.50%
ZDR: Yes

Training: Unknown

Prompt logging: Unknown

Retention: Unknown

Checked 2026-10-15

Parasailparasail/fp4262K$1.40$4.40$0.26—97.97%
ZDR: Yes

Training: Unknown

Prompt logging: Unknown

Retention: Unknown

Checked 2026-10-15

Togethertogether1.0M$1.40$4.40$0.26—97.14%
ZDR: Yes

Training: Unknown

Prompt logging: Unknown

Retention: Unknown

Checked 2026-10-15

Venicevenice/fp81M$1.40$4.40$0.26—99.86%
ZDR: Yes

Training: Unknown

Prompt logging: Unknown

Retention: Unknown

Checked 2026-10-15

Z.AIz-ai/fp81.0M$1.40$4.40$0.26—100.00%
ZDR: Yes

Training: Unknown

Prompt logging: Unknown

Retention: Unknown

Checked 2026-10-15

Mistralmistral/eu1.0M$1.54$4.84$0.15—100.00%
ZDR: Yes

Training: Unknown

Prompt logging: Unknown

Retention: Unknown

Checked 2026-10-15

BaseTenbaseten/fast1.0M$2.10$6.60$0.21—100.00%
ZDR: Yes

Training: Unknown

Prompt logging: Unknown

Retention: Unknown

Checked 2026-10-15

BaseTenbaseten/us/fast1.0M$2.10$6.60$0.21—100.00%
ZDR: Yes

Training: Unknown

Prompt logging: Unknown

Retention: Unknown

Checked 2026-10-15

Baidubaidu/fast1.0M$2.25$7.88$0.56—100.00%
ZDR: No

Training: Unknown

Prompt logging: Unknown

Retention: Unknown

Checked 2026-10-15

Alibabaalibaba/fast1M$2.31$7.26$0.46—91.89%
ZDR: No

Training: Unknown

Prompt logging: Unknown

Retention: Unknown

Checked 2026-10-15

Decartdecart/fast1.0M$2.50$9.00$0.54—100.00%
ZDR: Yes

Training: Unknown

Prompt logging: Unknown

Retention: Unknown

Checked 2026-10-15

Provider data:OpenRouter

Deployment graph

Can I run this?

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