Z.ai GLM 5.2
Z.ai (Zhipu AI)family · GLM5.2docs.mistral.ai/models/zai-glm-5-2
A third-party open source text model from Z.ai with a 1M-token context window.
Updated 5 h ago · first seen 11 Sept 2026
model_01M2943ZSHC4RGJCVFH2K0A6B2
Overview
Identity
- Canonical model
- Yesidentity confidence: mediumOne row per real model release. Artifacts (checkpoints, quantisations, conversions) and folded evaluation variants point here.
- Official checkpoints
- official_checkpoints = hf_repo identifiers carried by the model itself; artifacts are separate entities pointing here through canonical_id.
- Artifacts
- 2 quantizations0 official · 0 third-partySeparate entities (checkpoint · quantization · conversion · packaging) pointing to this model through canonical_id.
- Provider deployments
- 3
- API aliases
- glm-5-2glm-5-2-non-reasoningz-ai/glm-5.2Identifiers under which providers and evaluators refer to this model.
- Folded evaluation variants
- 1Effort / thinking variants (…-high, …-non-reasoning) are result configurations of this model, not separate models. Their old URLs redirect here.
Openness
Open weights— weights downloadable under MIT; commercial use allowed; redistribution allowed; derivatives allowed; 4 dimensions unknown.
Weights downloadable under a permissive or Creative Commons licence allowing commercial use; code or data may be missing.
Weights
Yes
Inference code
—
Training code
—
Training data
—
Dataset
—
Commercial use
Yes
Redistribution
Yes
Derivatives
Yes
Licence: MIT License (permissive · SPDX MIT · stated as “mit”)
dimensions marked null are unknown, not false
Key facts
- Release date
Source:OpenRouter public model & pricing listingT2observed 16 h agomedium
- Version
Source:Mistral AI docsT1observed 16 h agohigh
- Official page
Source:Mistral AI docsT1observed 16 h agohigh
- Model card
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 16 h agomedium
- Openrouter id
Source:OpenRouter public model & pricing listingT2observed 16 h agomedium
Architecture
- Architecture
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 15 h agomedium
- Model type
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 15 h agomedium
- Parameters
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 16 h agomedium
- Weights dtype
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 15 h agomedium
- File size
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 15 h agomedium
- Library name
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 15 h agomedium
- Pipeline tag
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 16 h agomedium
- Hugging Face repo
Source:OpenRouter public model & pricing listingT2observed 16 h agomedium
Capabilities
Modalities
- Modalities
- text
- Input
- text
- Output
- text
Capabilities
Tool calling
Yes
Mistral AI docs · T1
Structured output
Yes
Mistral AI docs · T1
Reasoning
Yes
OpenRouter public model & pricing listing · T2
Vision
Unavailable
Audio
Unavailable
Fine-tuning available
Unavailable
- Context window
Source:OpenRouter public model & pricing listingT2observed 5 h agomedium
- Max output
Source:OpenRouter public model & pricing listingT2observed 16 h agomedium
- Languages
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 15 h agomedium
Benchmarks32
Compare with another model →Comparable same task and conditions · Partially comparable same task, conditions differ (effort, temperature, judge) · Not comparable different variant or metric
Current rows only, grouped by benchmark → canonical metric → comparability group (task configuration). Effort variants folded into this model appear as rows of the same group. 32 current rows in total. “vs leader” compares with the current leader of the benchmark's primary group only; other groups are not directly comparable. Comparability rules →
Providers & Pricing5
All offers in the price terminal →USD per 1M tokens as published by each provider; native units (per-request fees, flex/priority tiers) are kept verbatim. Rows are append-only — every price change is kept in the history below. Cost of a workload →
Price history
Output price · USD / 1M tokens 3 providers
- Mistral AI La Plateforme
- Z.ai API
- OpenRouter
- OpenRouter$2 → $2.212 Sept 2026
- OpenRouterfirst observed $212 Sept 2026
- Z.ai API$2 → $2.211 Sept 2026
- Z.ai APIfirst observed $211 Sept 2026
- Mistral AI La Plateformefirst observed $4.411 Sept 2026
Input price · USD / 1M tokens 3 providers
- Mistral AI La Plateforme
- Z.ai API
- OpenRouter
- OpenRouter$0.60 → $0.7012 Sept 2026
- OpenRouterfirst observed $0.6012 Sept 2026
- Z.ai API$0.60 → $0.7011 Sept 2026
- Z.ai APIfirst observed $0.6011 Sept 2026
- Mistral AI La Plateformefirst observed $1.411 Sept 2026
Hardware fit37
Assumptions (7)
- Estimated, not measured: weights = parameters × bytes/param × 1.15 runtime overhead (or the observed artifact file size when one is recorded).
- bytes/param: 4bit = 0.5, 8bit = 1.0, fp16 = 2.0 (uniform quantization, no per-layer exceptions).
- KV cache: 2 × layers × kv_heads × head_dim × 2 bytes × context × batch when the architecture is known; otherwise 0.5 GB per 8 192 tokens (× batch), independent of architecture (GQA/MLA models need less).
- A model 'fits' when the estimate is at most the device memory minus 2 GB reserved for the OS and framework.
- Mixture-of-experts models are estimated on total parameters (all experts must be resident); active parameters are ignored.
- Device memory uses the largest configuration when several are listed (e.g. Apple silicon tiers).
- Multi-GPU: device memories are summed; interconnect bandwidth, tensor-parallel replication and pipeline bubbles are not modelled.
Lineage
Open in Graph →Versions & Artifacts2
Version history
Context window1 change
11 Sept 2026→12 Sept 2026current
Licensefirst observation only
11 Sept 2026current
Max outputfirst observation only
11 Sept 2026current
Opennessfirst observation only
11 Sept 2026current
Parametersfirst observation only
11 Sept 2026current
Each hop is a claim: click a value for its source, tier and observation time. Nothing is overwritten — a new observation closes the previous claim.
Artifacts 2
quantizations 2
- amd/GLM-5.2-MXFP4AMD · BF16/F32/U8438.0 GB
- nvidia/GLM-5.2-NVFP4NVIDIA · BF16/F32/U8464.9 GB
Papers2
- arXiv:2603.12201Active35
- arXiv:2602.15763Active35
Timeline17
Full timeline →OpenRouter lists Z.ai GLM 5.2 at $0.7 in / $2.2 out per 1M tokens
openrouterOpenRouter lists Z.ai GLM 5.2 at $0.6 in / $2 out per 1M tokens
openrouterZ.ai GLM 5.2: reasoning changed from false to true
ReasoningNo→YesopenrouterZ.ai GLM 5.2: context length changed from 1000000 to 1048576
Context window1M tokens→1.05M tokensopenrouterZ.ai GLM 5.2 scores 51.69% on Terminal-Bench
artificial_analysisZ.ai GLM 5.2 scores 9.78% on Humanity's Last Exam
artificial_analysisZ.ai GLM 5.2 scores 68.59% on GPQA Diamond
artificial_analysisZ.ai GLM 5.2 scores 22.43 on Artificial Analysis Intelligence Index
artificial_analysisZ.ai GLM 5.2 scores 50.76% on Terminal-Bench
artificial_analysisZ.ai GLM 5.2 scores 77.9% on Terminal-Bench
artificial_analysisZ.ai GLM 5.2 scores 99.12% on τ²-bench
artificial_analysisZ.ai GLM 5.2 scores 73.33% on IFBench
artificial_analysisZ.ai GLM 5.2 scores 51.16% on SciCode
artificial_analysisZ.ai GLM 5.2 scores 41.15% on Humanity's Last Exam
artificial_analysisZ.ai GLM 5.2 scores 89.49% on GPQA Diamond
artificial_analysisZ.ai GLM 5.2 scores 38.64 on Artificial Analysis Intelligence Index
artificial_analysisMistral AI La Plateforme lists Z.ai GLM 5.2 at $1.4 in / $4.4 out per 1M tokens
mistral
Change history57
Viewing AI Atlas as of 1 Sept 2026 — attributes exactly as the atlas knew them on that day; later corrections are not shown.
Back to today →Z.ai GLM 5.2 was not yet in AI Atlas on 1 Sept 2026
Versionversion1
Release daterelease_date1
Opennessopenness2
Licenselicense1
Architecturearchitecture1
Parametersparameter_count1
Context windowcontext_length10conflicting claims
Max outputmax_output_tokens1
Modalitiesmodalities1
Input modalitiesmodalities_input1
Output modalitiesmodalities_output1
Languageslanguages1
File sizefile_size_gb1
Hugging Face repohf_repo1
Pipeline tagpipeline_tag1
Official pageofficial_url1
Model cardmodel_card_url1
Downloadsmetric.downloads1
Likesmetric.likes3
Aa context windowaa_context_window1
Aa opennessaa_openness1
Commercial use allowedcommercial_use_allowed1
Derivatives allowedderivatives_allowed1
Descriptiondescription1
Gatedgated1
Hf inference providershf_inference_providers1
Last modifiedlast_modified1
Library namelibrary_name1
Livebench hf linklivebench_hf_link1
Aa median output tokens per secondmetric.aa_median_output_tokens_per_second2
Downloads all timemetric.downloads_all_time1
Model typemodel_type1
Openrouter idopenrouter_id1
Openrouter listed atopenrouter_listed_at1
Reasoningreasoning3
Redistribution allowedredistribution_allowed1
Structured outputstructured_output1
Supported parameterssupported_parameters1
Tool callingtool_calling1
Weights availableweights_available1
Weights dtypeweights_dtype1
Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →
Provenance
Attributed facts
44
Source tiers
T1T25 / 39
Freshest observation
5 h ago
Conflicts
7 flagged
Source documents 10
Tier 1 = official/primary, 2 = quality secondary, 3 = community, 4 = unverified. Every snapshot is archived; see all sources and the methodology.
Data quality (76/100) measures how well AI Atlas knows this entity — completeness, primary-source ratio, freshness, conflicts — never how good the model is. Methodology →