Singularity Logs

THE MODEL, IN DETAIL / Z.ai (Zhipu AI)

GLM-5.2

z-ai/glm-5.2Open weightsReasoningUnspecified source configuration configuration

GLM 5.2 is a large-scale reasoning model from Z.ai. It supports text input and output with a 1M-token context window, and is suited for long-horizon agent workflows, project-level software engineering,...

OpenRouter
Source-linked model intelligenceCatalog added 16 Jun 2026Specifications observed 28 Oct 2026

Specifications and providers describe Z.ai: GLM 5.2. Evaluation scores retain the exact Unspecified source configuration configuration.

GPQA Diamond

91.9%

Source-reported

Context window

1M

943.7K max output tokens

Input price

$0.0068

USD / million input tokens

Output price

$7

USD / million output tokens

Inference providers

25

34 provider routes observed

Measured output speed

—

No model-level measurement

01 / PERFORMANCE

A clearer view of capability.

Read the score, understand the context, and explore where this model sits among its peers.

Performance fingerprint

Individual published accuracy scores · not a composite index

Individual benchmark accuracy. Axes represent different tasks, not a composite index.FrontierMath Ti…FrontierMath Ti…GPQA DiamondOTIS Mock AIME …SimpleQA VerifiedFrontierMath Tier 4 v2: 29.3FrontierMath Tiers 1–3 v2: 59.2GPQA Diamond: 91.9OTIS Mock AIME 2024–2025: 86.4SimpleQA Verified: 34.2Each axis: 0–100% · Individual evaluations

Performance meets price

GPQA Diamond · same published source and version

Higher score · lower cost80$085$2.87590$5.7595$8.625100$11.5Gemini 4 Argon (high) GPQA Diamond: 96.4 Input: $2/M tokensGPT-6 Astra (max) GPQA Diamond: 95.8 Input: $10/M tokensClaude Sonnet 5.5 (max) GPQA Diamond: 95.6 Input: $2/M tokensGemini 3.5 Flash GPQA Diamond: 92.8 Input: $1.5/M tokensGrok 4.7 (xhigh) GPQA Diamond: 92.7 Input: $2/M tokensQwen3.8 Max (0902) (xhigh) GPQA Diamond: 92.3 Input: $2/M tokensKimi K3 (high) GPQA Diamond: 91.9 Input: $0.192/M tokensGLM-5.2 GPQA Diamond: 91.9 Input: $0.0068/M tokensGLM-5.2DeepSeek V4 Pro 0813 (max) GPQA Diamond: 91.7 Input: $0.66/M tokensGPT-5.6 Luna (max) GPQA Diamond: 91.6 Input: $0.2/M tokensGPT-5.2 (xhigh) GPQA Diamond: 91.4 Input: $1.75/M tokensClaude Opus 4.8 GPQA Diamond: 91 Input: $5/M tokensDeepSeek V4 Flash 0731 (max) GPQA Diamond: 91 Input: $0.119/M tokensGLM-5.3 (max) GPQA Diamond: 90.9 Input: $0.0699/M tokensGLM-5.3-Flash (max) GPQA Diamond: 90.2 Input: $0.15/M tokensInput price · USD / million tokens
Google DeepMindOpenAIAnthropicxAIAlibabaMoonshotZ.ai (Zhipu AI)DeepSeek

In good company

GPQA Diamond · Epoch AI · benchmark data

96.4
Google DeepMind
95.8
OpenAI
95.6
Anthropic
92.8
Google DeepMind
92.7
xAI
92.7
Alibaba
92.6
Google DeepMind
92.3
Alibaba
91.9
Moonshot
91.9
This model

Rank #27 in this published cohort. 325 models in the source cohort. Showing selected nearby models. Evaluation settings can differ.

Evidence, by task.

Coverage is not a capability score.

5published results
1 evaluation source
Reasoning5 evaluations
CodingAwaiting evidence
Mathematics3 evaluations
VisionAwaiting evidence
AgenticAwaiting evidence
Long contextAwaiting evidence

Documented features, task evaluations, and preference rankings describe different things. Missing coverage does not imply a model cannot perform a task.

Explore our methodology

02 / BENCHMARK EXPLORER

The numbers behind the model.

Original results, exact versions, and a direct path to the evidence behind every score.

5 published results
020406080100FrontierMath Tier 4 v2: 29.3% · Epoch AI · independent runs · Epoch AI · benchmark data29.3%FrontierMath Tie…Higher is betterFrontierMath Tiers 1–3 v2: 59.2% · Epoch AI · independent runs · Epoch AI · benchmark data59.2%FrontierMath Tie…Higher is betterGPQA Diamond: 91.9% · Epoch AI · independent runs · Epoch AI · benchmark data91.9%GPQA DiamondHigher is betterOTIS Mock AIME 2024–2025: 86.4% · Epoch AI · independent runs · Epoch AI · benchmark data86.4%OTIS Mock AIME 2…Higher is betterSimpleQA Verified: 34.2% · Epoch AI · independent runs · Epoch AI · benchmark data34.2%SimpleQA VerifiedHigher is better

5 percentage-based evaluations. Each benchmark measures a different task; compare results within the same evaluation and protocol.

Published benchmark results for GLM-5.2
BenchmarkScoreVersion & metricSourcePublishedEvidence
Epoch AI · independent runsAccuracy · Higher is betterEpoch AI · benchmark dataSource-reportedNot available
Epoch AI · independent runsAccuracy · Higher is betterEpoch AI · benchmark dataSource-reportedNot available
Epoch AI · independent runsAccuracy · Higher is betterEpoch AI · benchmark dataSource-reportedNot available
Epoch AI · independent runsAccuracy · Higher is betterEpoch AI · benchmark dataSource-reportedNot available
Epoch AI · independent runsAccuracy · Higher is betterEpoch AI · benchmark dataSource-reportedNot available

03 / COST & VALUE

Know what your workload costs.

Explore token prices, see where the spend goes, and model a workload of your own.

Input$0.0068per million tokens
Output$7per million tokens
Cache read$0.0058per million tokens
Cache write—per million tokens

Workload cost calculator

Change the numbers. See the trade-offs.

Token estimate only. Excludes cache writes, tools, and other provider fees. Actual token usage and provider limits vary.

Your estimated spend

1,000 requests · 11M tokens

$7.068estimated total
Uncached input$0.068
Cached input$0
Output$7
Per request$0.0071
Without cache discount$7.068
Estimated cache savings$0

04 / INFERENCE NETWORK

One model. Different ways to run it.

Compare inference routes by price, context, quantization, and reported availability.

25 providers · 34 routes
Provider routes for GLM-5.2, observed 28 Oct 2026
Provider / routeInput / MOutput / MCache / MContextOutput limitSpeedLatencyAvailability · 1dRates
RRelaceQuantization not disclosed$0.0133$7.7344$0.01331M131.1K——100%
WWaferFP4$0.014$7$0.0131M943.7K——100%
WWaferFP4$0.19$8$0.181M943.7K——100%
CCloudflareQuantization not disclosed$0.19$8$0.19262.1K235.9K——100%
MMorphFP8$0.203$2.208$0.0691M943.7K——100%
DDeepInfraFP4 · flex$0.45$1.44$0.0841M163.8K——100%
DDeepInfraFP4$0.5625$1.8$0.1051M163.8K——100%
AAmbientFP8$0.6$2$0.15202.8K182.5K——100%
SStreamLakeFP8$0.6131$1.9268$0.11391M128K——100%
CCoreWeaveFP4$0.62$2.42$0.141M943.7K——99.79%
NNovitaFP8$0.6496$2.0416$0.12061M131.1K——100%
DDigitalOceanQuantization not disclosed$0.7$2.2$0.1051M131.1K——99.65%
SSiliconFlowFP8$0.7$2.2$0.131M262.1K——99.93%
AAtlasCloudFP8$0.938$2.948$0.17421M131.1K——100%
AAlibabaFP8$0.966$3.036$0.19321M131.1K——99.53%
IInceptronFP4$0.99$4.32$0.161M943.7K——99.62%
AAlibabaFP8$1.12$3.52$0.2241M131.1K——100%
DDecartFP4 · priority$1.2$8$0.481M943.7K——100%
NNebiusFP4$1.4$4.4$0.141M943.7K——77.49%
MMistralNVFP4$1.4$4.4$0.141M131.1K——98.6%
BBaseTenFP8$1.4$4.4$0.141M262.1K——100%
TTogetherQuantization not disclosed$1.4$4.4$0.261M943.7K——98.32%
BBaiduFP8$1.4$4.4$0.261M131.1K——100%
BBaseTenFP8$1.4$4.4$0.141M262.1K——100%
VVeniceFP8$1.4$4.4$0.261M131.1K——100%
GGMICloudFP8$1.4$4.4$0.261M943.7K——100%
PParasailFP4$1.4$4.4$0.26262.1K235.9K——99.74%
FFriendliQuantization not disclosed$1.4$4.4$0.261M943.7K——100%
ZZ.AIFP8$1.4$4.4$0.261M131.1K——100%
MMistralQuantization not disclosed$1.54$4.84$0.1541M128K——100%
BBaseTenFP8 · priority$2.1$6.6$0.211M262.1K——99.28%
BBaseTenFP8 · priority$2.1$6.6$0.211M262.1K——99.14%
BBaiduFP4 · priority$2.25$7.88$0.561M131.1K——100%
AAlibabaFP8 · priority$2.31$7.26$0.4621M131.1K——99.46%

Snapshot observed 28 Oct 2026. Speed and latency are the reported 30-minute median when available. This is a dated snapshot, not live monitoring.

Provider price comparison

USD per million tokens · input and output rates

InputOutput
Relace
Input
$0.0133
Output
$7.7344
Waferfp4
Input
$0.014
Output
$7
Waferfp4
Input
$0.19
Output
$8
Cloudflare
Input
$0.19
Output
$8
Morphfp8
Input
$0.203
Output
$2.208
DeepInfrafp4 · flex
Input
$0.45
Output
$1.44
DeepInfrafp4
Input
$0.5625
Output
$1.8
Ambientfp8
Input
$0.6
Output
$2
StreamLakefp8
Input
$0.6131
Output
$1.9268
CoreWeavefp4
Input
$0.62
Output
$2.42

Reported availability

Provider availability over the selected observation window

Relace100%
Wafer · FP4100%
Wafer · FP4100%
Cloudflare100%
Morph · FP8100%
DeepInfra · flex · FP4100%
DeepInfra · FP4100%
Ambient · FP8100%
StreamLake · FP8100%
CoreWeave · FP499.79%

Output speed

Reported 30-minute p50 · tokens per second

No provider throughput measurements were returned in this snapshot.

Response latency

Reported 30-minute p50 · seconds

No provider latency measurements were returned in this snapshot.

Context by provider

Actual route limits can differ from the catalog maximum

Relace1M
Wafer · FP41M
Wafer · FP41M
Cloudflare262.1K
Morph · FP81M
DeepInfra · flex · FP41M
DeepInfra · FP41M
Ambient · FP8202.8K
StreamLake · FP81M
CoreWeave · FP41M

05 / CAPABILITIES & MODALITIES

What goes in. What comes out.

A map of documented interfaces and API features, separate from benchmark performance.

INPUT MODALITIES

Text
GLM-5.2Reasoning supported

OUTPUT MODALITIES

Text

Feature support

Listed catalog API parameters and documented pricing features

ReasoningSupported
Tool callingSupported
Tool choiceSupported
Structured outputsSupported
Response formatSupported
Reasoning detailsSupported
Seed controlSupported
Stop sequencesSupported
Prompt cachingSupported
TemperatureSupported

Room to work

Context capacity and completion limits

1Mtotal context tokens
Context window
943.7Kmax output

Output tokens share the total context budget. The maximum completion length is a separate limit, and individual provider routes may have smaller limits.

Catalog context
1,048,576 tokens
Maximum output
943,718 tokens
Top-provider context
1,048,576 tokens

Features vary by route

Check the provider before depending on a specific API feature.

API feature support by provider
Provider routeReasoningToolsStructured outputResponse formatImplicit caching
RelaceSupportedSupportedNot listedSupportedSupported
Wafer · FP4SupportedSupportedSupportedSupportedSupported
Wafer · FP4SupportedSupportedSupportedSupportedSupported
CloudflareSupportedSupportedSupportedSupportedSupported
Morph · FP8SupportedSupportedSupportedSupportedSupported
DeepInfra · flex · FP4SupportedSupportedSupportedSupportedSupported
DeepInfra · FP4SupportedSupportedSupportedSupportedSupported
Ambient · FP8SupportedSupportedSupportedSupportedSupported
StreamLake · FP8SupportedSupportedSupportedSupportedSupported
CoreWeave · FP4SupportedSupportedSupportedSupportedSupported
Novita · FP8SupportedSupportedNot listedSupportedSupported
DigitalOceanSupportedSupportedSupportedSupportedSupported
SiliconFlow · FP8SupportedSupportedNot listedNot listedSupported
AtlasCloud · FP8SupportedSupportedNot listedSupportedSupported
Alibaba · FP8SupportedSupportedSupportedSupportedSupported
Inceptron · FP4SupportedSupportedSupportedSupportedSupported
Alibaba · FP8SupportedSupportedSupportedSupportedSupported
Decart · priority · FP4SupportedSupportedSupportedSupportedSupported
Nebius · FP4SupportedSupportedSupportedSupportedSupported
Mistral · NVFP4SupportedSupportedSupportedSupportedSupported
BaseTen · FP8SupportedSupportedSupportedSupportedSupported
TogetherSupportedSupportedSupportedSupportedSupported
Baidu · FP8SupportedSupportedSupportedSupportedSupported
BaseTen · FP8SupportedSupportedSupportedSupportedSupported
Venice · FP8SupportedSupportedSupportedSupportedSupported
GMICloud · FP8SupportedSupportedSupportedSupportedSupported
Parasail · FP4SupportedSupportedSupportedSupportedSupported
FriendliSupportedSupportedSupportedSupportedSupported
Z.AI · FP8SupportedSupportedNot listedSupportedSupported
MistralSupportedSupportedSupportedSupportedSupported
BaseTen · priority · FP8SupportedSupportedSupportedSupportedSupported
BaseTen · priority · FP8SupportedSupportedSupportedSupportedSupported
Baidu · priority · FP4SupportedSupportedSupportedSupportedSupported
Alibaba · priority · FP8SupportedSupportedSupportedSupportedSupported

06 / UNDER THE HOOD

The complete specification sheet.

Exact identities, model architecture, limits, and the defaults that shape a request.

Identity & architecture

Developer
Z.ai (Zhipu AI)
Exact identity
glm-5.2_max
Subject
Model Configuration
Access
Open weights
License
Not disclosed
Parameters
Not disclosed
Tokenizer
Other
Instruction format
Not disclosed

Reasoning & controls

Reasoning
Supported
Mandatory reasoning
No
Reasoning by default
Enabled
Published configuration
Unspecified source configuration
Supported reasoning efforts
xhigh, high, none
Default reasoning effort
high
API parameters
21
Provider moderation
No
Quantization variants
fp4, fp8, nvfp4

Dates & limits

Catalog added
16 Jun 2026
Knowledge cutoff
Not disclosed
Catalog expiration
Not documented
Specifications observed
28 Oct 2026
Providers observed
28 Oct 2026
Request limits
Provider-specific / not supplied
Canonical catalog ID
z-ai/glm-5.2-20260616

API parameters & defaults

Availability is catalog-wide. Defaults can vary across providers.

frequency_penaltySupported · default not supplied
include_reasoningSupported · default not supplied
logit_biasSupported · default not supplied
logprobsSupported · default not supplied
max_tokensSupported · default not supplied
min_pSupported · default not supplied
parallel_tool_callsSupported · default not supplied
presence_penaltySupported · default not supplied
reasoningSupported · default not supplied
reasoning_effortSupported · default not supplied
repetition_penaltySupported · default not supplied
response_formatSupported · default not supplied
seedSupported · default not supplied
stopSupported · default not supplied
structured_outputsSupported · default not supplied
temperatureDefault: 1
tool_choiceSupported · default not supplied
toolsSupported · default not supplied
top_kSupported · default not supplied
top_logprobsSupported · default not supplied
top_pDefault: 0.95

07 / BUILD WITH THIS MODEL

From exploration to your first request.

Use the documented catalog identifier and keep your API key in an environment variable.

Make an API request

const response = await fetch(
  "https://openrouter.ai/api/v1/chat/completions",
  {
    method: "POST",
    headers: {
      Authorization: `Bearer ${process.env.OPENROUTER_API_KEY}`,
      "Content-Type": "application/json",
    },
    body: JSON.stringify({
      "model": "z-ai/glm-5.2",
      "messages": [
        {
          "role": "user",
          "content": "Explain the trade-offs in this design."
        }
      ],
      "reasoning": {
        "effort": "unspecified source configuration"
      }
    }),
  },
);
if (!response.ok) throw new Error(`HTTP ${response.status}`);
const data = await response.json();
console.log(data.choices[0].message.content);

08 / PROVENANCE & HISTORY

Every number has a story.

Follow the source, check the observation date, and distinguish published results from model specifications.

The observation trail

Recorded events, in chronological order

Added to the model catalog

Catalog creation date, as recorded by OpenRouter.

Provider routes observed

34 inference routes in this snapshot.

Model specifications observed

Catalog pricing, limits, and supported parameters captured.

Observation dates describe when data was captured. They do not represent newly run evaluations or continuous performance monitoring.

Published observation ledger

Historical evidence, with source and version preserved.

Published observation history for GLM-5.2
ObservedBenchmarkScoreVersionSourceVerification
28 Oct 2026SimpleQA VerifiedEpoch AI · independent runsEpoch AI · benchmark dataSource-reported
28 Oct 2026FrontierMath Tier 4 v2Epoch AI · independent runsEpoch AI · benchmark dataSource-reported
28 Oct 2026FrontierMath Tiers 1–3 v2Epoch AI · independent runsEpoch AI · benchmark dataSource-reported
28 Oct 2026OTIS Mock AIME 2024–2025Epoch AI · independent runsEpoch AI · benchmark dataSource-reported
28 Oct 2026GPQA DiamondEpoch AI · independent runsEpoch AI · benchmark dataSource-reported
28 Oct 2026SimpleQA VerifiedEpoch AI · independent runsEpoch AI · benchmark dataSource-reported
28 Oct 2026FrontierMath Tier 4 v2Epoch AI · independent runsEpoch AI · benchmark dataSource-reported
28 Oct 2026FrontierMath Tiers 1–3 v2Epoch AI · independent runsEpoch AI · benchmark dataSource-reported
28 Oct 2026OTIS Mock AIME 2024–2025Epoch AI · independent runsEpoch AI · benchmark dataSource-reported
28 Oct 2026GPQA DiamondEpoch AI · independent runsEpoch AI · benchmark dataSource-reported

09 / A FEW MORE DETAILS

Questions worth asking.

Quick answers, drawn from this model’s connected specifications.

How much does GLM-5.2 cost?

Catalog starting rates are $0.0068 per million input tokens and $7 per million output tokens. Cache-read pricing is $0.0058 per million tokens. Compare provider routes and service tiers above for their specific prices.

How much context can it handle?

1,048,576 tokens in the catalog context window. The catalog lists a maximum of 943,718 completion tokens. Provider limits can differ.

Does it support tools and structured output?

Tool calling: listed as supported. Structured outputs: listed as supported. See the provider feature matrix for route-specific differences.

How should I interpret these benchmark results?

Read the benchmark name, exact version, metric, source, and model configuration together. Different evaluation settings can change the result. Preference ratings and source-defined composite indices remain separate from task accuracy.

Are these prices and provider measurements live?

This page uses dated source snapshots. Catalog data was observed on 28 Oct 2026. Provider routes were observed on 28 Oct 2026. Missing measurements are not estimated or treated as zero.

KEEP EXPLORING

More from Z.ai (Zhipu AI).

Explore related identities from the same developer, or take them into a side-by-side comparison.

THE RIGHT MODEL STARTS WITH THE RIGHT CONTEXT.

Put the details side by side.

Explore the trade-offs in a direct model comparison.

Compare GLM-5.2
Source-backed evidence. Different tasks. A clearer picture.
AboutMethodologyBenchmark dashboard
GLM-5.2 — Singularity Logs