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Data

Data as of October 29, 2026 · How the score is built

Superseded.Z.AI has newer models in this line:GLM-5.3
SupersededOpen WeightReasoning

Released Jun 16, 20261M context

GLM-5.2

Decision readingGLM-5.2 scores 60 out of 100 and ranks #52 of 216. This profile shows 30 source-displayable benchmark rows; its strongest eligible category is Mathematics at #3. API pricing is $1.4 input and $4.4 output per million tokens.

Released Jun 16, 2026 — see all recent releases

Decision snapshot

Each value carries a field reference instead of floating alone. Markers compare this model with the current ranked and priced catalog; they are not absolute quality thresholds.

Capability

60/100

field median 50.5#52 of 216 ranked models

Public

#52of 216

Verified #34 of 77

Price

$1.40input / $4.40 output

input median $0.75blended $2.90

Speed

82tok/s

field median 92 tok/sFirst token 27.9 s

Context

1Mtokens

field median 256,000Reported for this model; direct source link not stored

Strongest published evidence

Mathematics ranks #3. Particularly strong for mathematical reasoning, scientific computing, and quantitative analysis.

Validate before choosing

30 published rows leave some tracked benchmark slots empty. Knowledge is its lowest eligible category at #56.

Source-linked · 30 displayable benchmark rows

Category score record

Scores and ranks appear only where published evidence can be displayed. The table keeps the score, weight, cohort, and evidence state together.

Category scores, ranks, weighting, benchmark coverage, and evidence status
CategoryScore
AgenticRank #38 of 135Percentile 72ndWeight 22%8 benchmarksVerified
57.1
CodingRank #34 of 156Percentile 79thWeight 20%9 benchmarksVerified
53.7
ReasoningRank Not rankedWeight 17%1 benchmarkVerified
78.8
MultimodalWeight 12%0 benchmarksNot measured
Not measured
KnowledgeRank #56 of 179Percentile 69thWeight 12%6 benchmarksVerified
56.4
MultilingualWeight 7%0 benchmarksNot measured
Not measured
Inst. FollowingRank #29 of 157Percentile 82ndWeight 5%Not listedUnlisted sources
89.9
MathRank #3 of 12Percentile 82ndWeight 5%6 benchmarksVerified
78.4

30 of 782 tracked benchmark slots have displayable evidence · bars run 0–100

Coverage details

How much of this is verified

Coverage is split by category so a strong number never hides a thin evidence base. Verified means the row is tied to a published source; provisional rows remain visible but separate.

  1. Agentic8/8 verified
  2. Coding9/9 verified
  3. Reasoning1/1 verified
  4. MultimodalNot measured
  5. Knowledge6/6 verified
  6. MultilingualNot measured
  7. Inst. FollowingNot measured
  8. Math6/6 verified
Verified sourceProvisionalNot measured

Capability shape

Each axis shows percentile within that category’s eligible cohort. The comparison outline is the median of the six nearest public-score peers; a collapsed vertex means the category is not rank-eligible.

GLM-5.2 category percentile values

  • Agentic72nd percentile
  • Coding79th percentile
  • ReasoningNot eligible
  • MultimodalNot eligible
  • Knowledge69th percentile
  • MultilingualNot eligible
  • Instruction following82nd percentile
  • Math82nd percentile

The dashed outline is median of 6 nearest peers.

Eligible category ranks

  1. Agentic#38/135
  2. Coding#34/156
  3. ReasoningNot ranked
  4. MultimodalNot ranked
  5. Knowledge#56/179
  6. MultilingualNot ranked
  7. Inst. Following#29/157
  8. Math#3/12
Top decileTop quartileMid-fieldNot eligible

Benchmark ledger

Coding opens by default. The marker compares each value with the best source-verified result in the catalog; provisional leaders do not set the reference. Expand the remaining categories for every published row.

Coding9 rows
Coding benchmark values, best verified comparison, weight, and source status
SWE-bench ProScore62.1%Versus best verified row

Best verified: Claude Opus 5.5 · 89.9%

Gap27.8 behindWeight26% ref. weight
CursorBench 3.2Score55.0%Versus best verified row

Best verified: Claude Fable 5.1 · 73.4%

Gap18.4 behindWeight10% ref. weight
LiveCodeBench (Vals)LiveCodeBench, Vals AI runScore69.5%Versus best verified row

Best verified: Claude Fable 5.1 · 90.5%

Gap21 behindWeight8% ref. weight
Terminal-Bench 2.1Score81.0%Versus best verified row

Best verified: SWE-2 · 92.8%

Gap11.8 behindWeightScored in Agentic
NL2RepoScore48.9%Versus best verified row

Best verified: DeepSeek V4.1 Flash 1017 · 68.2%

Gap19.3 behindWeightDisplay only
ProgramBenchProgramBench: Can Language Models Rebuild Programs From Scratch?Score63.7%Versus best verified row

Best verified: Claude Opus 5 · 93.0%

Gap29.3 behindWeightDisplay only
OpenHarmony BenchOpenHarmony Bench v1.0Score58.4%Versus best verified row

Best verified: Qwen3.8 Max · 60.8%

Gap2.4 behindWeightDisplay only
SWE-bench (Vals)SWE-bench, Vals AI runScore82.8%Versus best verified row

Best verified: Claude Opus 5 · 97.0%

Gap14.2 behindWeightDisplay only
PostTrainBench v1.1Score31.7%Versus best verified row

Best verified: Claude Opus 5.5 · 49.3%

Gap17.6 behindWeightDisplay only
Agentic8 rows
Agentic benchmark values, best verified comparison, weight, and source status
Terminal-Bench 2.1Score81.0%Versus best verified row

Best verified: SWE-2 · 92.8%

Gap11.8 behindWeight8% ref. weight
MCP AtlasScore76.8%Versus best verified row

Best verified: Muse Spark Open · 89.2%

Gap12.4 behindWeight4% ref. weight
Terminal-Bench 2.1 (Vals)Terminal-Bench 2.1, Vals AI runScore67.8%Versus best verified row

Best verified: GPT-6 Astra · 87.3%

Gap19.5 behindWeight4% ref. weight
Terminal-Bench 3.0Score4.6%Versus best verified row

Best verified: Claude Opus 5 · 42.7%

Gap38.1 behindWeight3% ref. weight
ToolathlonScore48.2%Versus best verified row

Best verified: Muse Spark 1.1 · 75.6%

Gap27.4 behindWeight3% ref. weight
HLE w/ toolsHumanity's Last Exam with toolsScore54.7%Versus best verified row

Best verified: Kimi K3.1 Pro · 70.0%

Gap15.3 behindWeight3% ref. weight
ResearchClawBenchScore20.7%Versus best verified row

Best verified: Claude Opus 4.8 · 21.1%

Gap0.4 behindWeightDisplay only
MazeBenchMazeBench: visual spatial reasoning in a 3D open worldScore0%Versus best verified row

Best verified: GPT-6 Astra · 23%

Gap23 behindWeightDisplay only
Reasoning1 row
Reasoning benchmark values, best verified comparison, weight, and source status
CritPtCritical Physics TasksScore20.9%Versus best verified row

Best verified: Gemini 4 Carbon · 36.0%

Gap15.1 behindWeightFeeds overall
Knowledge6 rows
Knowledge benchmark values, best verified comparison, weight, and source status
HLEHumanity's Last ExamScore40.5%Versus best verified row

Best verified: Kimi K3.1 Pro · 70%

Gap29.5 behindWeight44% ref. weight
HLE w/o toolsHumanity's Last Exam without toolsScore40.5%Versus best verified row

Best verified: Claude Opus 5.5 · 64.4%

Gap23.9 behindWeight7% ref. weight
MMLU-Pro (Vals)MMLU-Pro, Vals AI runScore86.7%Versus best verified row

Best verified: Claude Fable 5.1 · 92.4%

Gap5.7 behindWeight6% ref. weight
GPQAGraduate-Level Google-Proof Q&AScore91.2%Versus best verified row

Best verified: GPT-6 Astra · 96%

Gap4.8 behindWeight3% ref. weight
GPQA Diamond (Vals)GPQA Diamond, Vals AI runScore85.6%Versus best verified row

Best verified: Gemini 3.1 Pro · 95.5%

Gap9.9 behindWeight2% ref. weight
GPQA-DGPQA DiamondScore91.2%Versus best verified row

Best verified: GPT-6 Astra · 96.0%

Gap4.8 behindWeightDisplay only
Math6 rows
Math benchmark values, best verified comparison, weight, and source status
FrontierMath v2 (Tiers 1-3)FrontierMath v2 Tiers 1-3Score59.206%Versus best verified row

Best verified: GPT-6 Astra · 93.684%

Gap34.5 behindWeightWeighted 30%
AIME26AIME 2026Score99.2%Versus best verified row

Best verified: GLM-5.2 · 99.2%

GapBest verifiedWeightWeighted 25%
HMMT Feb 2026Harvard-MIT Mathematics Tournament February 2026Score92.5%Versus best verified row

Best verified: Qwen3.7 Max · 97.1%

Gap4.6 behindWeightWeighted 25%
FrontierMath v2 (Tier 4)FrontierMath v2 Tier 4Score29.268%Versus best verified row

Best verified: GPT-6.1 Sol · 100.000%

Gap70.7 behindWeightWeighted 10%
HMMT Nov 2025Harvard-MIT Mathematics Tournament November 2025Score94.4%Versus best verified row

Best verified: Qwen3.6 Plus · 94.6%

Gap0.2 behindWeightDisplay only
MMAnswerBenchScore91.0%Versus best verified row

Best verified: GLM-5.2 · 91.0%

GapBest verifiedWeightDisplay only

Bars run 0–100; the dark tick marks the best source-verified value

All 30 rows

What it costs to get this score

Published API price against the public score. The x-axis uses a log scale; the dashed path marks models that are not beaten by a cheaper, higher-scoring option. Price uses average of published input and output rates.

Current modelExplore all models

GLM-5.2 · 60.0 score · $2.90 blended per million tokens

The chart opens on the current model. Scroll horizontally to inspect the full price axis.

405060708090100$0.10$0.50$1$5$10$25$50$100↘ frontierGLM-5.2

Horizontal: blended price per million tokens, log scale · Vertical: public score

Lineage

The sequence follows explicit supersedes links. Each score is estimated for that model; a relative can inform a sparse estimate but never sets a floor, so a newer release can score below an earlier one. Scores and prices remain blank when the corresponding public row or first-party rate is unavailable.

  1. Feb 12, 2026

    GLM-5

    Score 54.3 · $1 / $3.2

  2. Apr 7, 2026

    GLM-5.1

    Score 55.8 · $1.4 / $4.4

  3. Jun 16, 2026 · you are here

    GLM-5.2

    Score 60.0 · $1.4 / $4.4

  4. Aug 14, 2026

    GLM-5.3

    Score 61.0 · Price not listed

Spec sheet

Each documented value carries its source. Missing fields stay visible as not sourced or not published, rather than disappearing from the page.

API model ID
Not published
Context window
1M
Maximum output
Not sourced yet
Knowledge cutoff
Not sourced yet
Input modalities
Not sourced yet
Output modalities
Not sourced yet
Parameters
Not sourced yet
Availability
Not sourced yet
Cloud regions
Not tracked yet
Lifecycle
Superseded
API capabilities
Tool calling, structured outputs, and batch support are not tracked yet
Prompt caching
Not documented in the pricing record
Self-host
GLM-5.2 needs ~640GB VRAM (8× NVIDIA H100 (80GB)).
Rate limits
Not tracked yet

How to read this profile

The visual layer above carries the decisions. These notes preserve the model, ranking, coverage, and family context behind the numbers.

GLM-5.2 ranks #52 of 216 on the public leaderboard with a score of 60.03/100. Its source-verified position is #34 of 77.

GLM-5.2 is a open weight model with a 1M context window. It uses an explicit reasoning mode, which can improve complex problem solving while adding latency and token use.

Official exact-value snapshot from Z.AI's GLM-5.2 launch materials and Hugging Face model card. BenchLM maps directly comparable rows into existing keys; DeepSWE, FrontierSWE, PostTrainBench, SWE-Marathon, and other unsupported long-horizon rows remain excluded or external-only until stable local keys exist.

GLM-5.2 sits in the GLM-5 family with GLM-5, GLM-5.1, GLM-5.3, GLM-5.3-Flash, GLM-5-Turbo, GLM-5V-Turbo, GLM-5 (Reasoning). Its explicit predecessor is GLM-5.1. 30 of 782 tracked benchmark slots currently have displayable evidence. Missing categories stay blank.

Its strongest eligible category is Mathematics at #3, while its lowest eligible position is Knowledge at #56. particularly strong for mathematical reasoning, scientific computing, and quantitative analysis.

Last updated October 29, 2026. Runtime fields remain blank until a sourced snapshot exists.

Deployment options

Self-host and provider-specific paths stay separate from benchmark evidence so operating constraints are visible before a score becomes the whole decision.

Where to run GLM-5.2 by API

6 hosts publish a per-token price for it. DeepInfra has the lowest output rate, $1.80 per million tokens. Prices read from each host’s own pages on October 20, 2026.

Inference hosts for GLM-5.2 with input, output and cached-input prices per million tokens.
HostInput /1MOutput /1MCached /1MContextListed speed
DeepInfra$0.56$1.80$0.101.05M—
OpenRouter$0.62$2.42$0.141.05M—
Baseten$1.40$4.40$0.14——
Nebius Token Factory$1.40$4.40—1.05M25 tok/s
Novita AI$1.40$4.40$0.261.05M—
Together AI$1.40$4.40$0.261.05M—
Compare inference hosts

Self-host vs API cost

Estimates at 50,000 req/day · 1000 tokens/req average.

GLM-5.2
API / mo$4,350
Self-host / mo$27,962
Break-even405M/day
Model the full break-even

Questions

How does GLM-5.2 perform overall in AI benchmarks?

GLM-5.2 ranks #52 out of 216 models on the public BenchAlign leaderboard, with a score of 60.03/100. Its evidence status is Supported, and this profile shows 30 source-displayable benchmark rows. The label describes evidence depth, not a provider quality claim; inspect category rows before choosing a workload.

Is GLM-5.2 good for knowledge and understanding?

GLM-5.2 ranks #56 out of 179 eligible models for knowledge and understanding, with a public category score of 56.4/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.

Is GLM-5.2 good for coding and programming?

GLM-5.2 ranks #34 out of 156 eligible models for coding and programming, with a public category score of 53.7/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.

Is GLM-5.2 good for mathematics?

GLM-5.2 ranks #3 out of 12 eligible models for mathematics, with a public category score of 78.4/100. That places it in the current top ten for this category. Check the underlying rows before treating the aggregate as a workload guarantee.

Is GLM-5.2 good for reasoning and logic?

GLM-5.2 has source-displayable benchmark coverage for reasoning and logic, but the public category table does not assign it a rank there. The individual rows remain available for inspection. A missing category position means the evidence threshold was not met; it does not convert the model's unmeasured work into a zero.

Is GLM-5.2 good for agentic tool use and computer tasks?

GLM-5.2 ranks #38 out of 135 eligible models for agentic tool use and computer tasks, with a public category score of 57.1/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.

Is GLM-5.2 open source?

GLM-5.2 is an open-weight model from Z.AI. Its weights can be downloaded for local or hosted deployment, subject to the published license. Open weight does not automatically mean open source: training data and training code may remain private, and commercial restrictions can still apply.

Which sibling models are related to GLM-5.2?

GLM-5.2 belongs to the GLM-5 family. Related tracked variants include GLM-5, GLM-5.1, GLM-5.3, plus 4 more. A sibling link indicates shared lineage or a documented configuration relationship; it does not mean the variants have identical pricing, context limits, benchmark evidence, or deployment behavior. Compare before switching.

Does GLM-5.2 have full benchmark coverage on BenchLM?

No. GLM-5.2 currently has 49 source-displayable rows. The profile exposes published, non-generated evidence and leaves missing categories blank until an exact evaluation is available. Coverage describes how much was measured; it is not a penalty added to an individual benchmark result.

What is the context window size of GLM-5.2?

GLM-5.2 has a reported context window of 1M in the exact-model catalog record. The value stays visible, but the profile marks its source link as unavailable instead of presenting it as directly documented. Maximum output length remains separate because providers often publish a different limit.

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Compare GLM-5.2 with every tracked model1003 comparisons