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Superseded.Z.AI has newer models in this line:GLM-5.3

GLM-5.2

SupersededReleased Jun 16, 2026Open WeightReasoning1M context

Released Jun 16, 2026 see all recent releases

Decision reading
GLM-5.2 scores 68.2 out of 100 and ranks #26 of 232. This profile shows 25 source-displayable benchmark rows; its strongest eligible category is Coding at #19. API pricing is $1.4 input and $4.4 output per million tokens.

Data as of September 14, 2026 · How the score is built

Strongest published evidence

Coding ranks #19. Particularly well-suited for software development and code generation tasks.

Validate before choosing

25 published rows leave some tracked benchmark slots empty. Knowledge is its lowest eligible category at #35.

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

68.2/100

field median 56.3

#26 of 232 ranked models

Price

$1.40input / $4.40 output

input median $0.97

blended $2.90

Speed

78tok/s

field median 91.5 tok/s

First token 33.51 s

Context

1Mtokens

field median 256,000

Reported for this model; direct source link not stored

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

  • Agentic82nd percentile
  • Coding88th percentile
  • ReasoningNot eligible
  • Knowledge81st percentile
  • MathNot eligible
  • MultilingualNot eligible
  • MultimodalNot eligible
  • Instruction followingNot eligible

The dashed outline is median of 6 nearest peers.

Eligible category ranks

  1. Agentic#29/153
  2. Coding#19/152
  3. ReasoningNot ranked
  4. Knowledge#35/183
  5. MathNot ranked
  6. MultilingualNot ranked
  7. MultimodalNot ranked
  8. Inst. FollowingNot ranked
Top decileTop quartileMid-fieldNot eligible

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.

Explore all models

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

Current modelGLM-5.2 · 68.2 score · $2.90 blended per million tokens
30405060708090$0.50$1$5$10$25↘ frontierGLM-5.2

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

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. Agentic6/6 verified
  2. Coding8/8 verified
  3. Reasoning1/1 verified
  4. Knowledge6/6 verified
  5. Math4/4 verified
  6. MultilingualNot measured
  7. MultimodalNot measured
  8. Inst. FollowingNot measured
Verified sourceProvisionalNot measured

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
Open weights available; hardware estimate not sourced
Rate limits
Not tracked yet

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.

Published weights are available, but BenchLM does not yet have a sourced parameter and VRAM profile for this exact model. Hardware cost estimates stay unavailable until that sizing record is complete.

Estimate VRAM from known parameters

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 #29 of 153Percentile 82ndWeight 22%6 benchmarksVerified58.5
CodingRank #19 of 152Percentile 88thWeight 20%8 benchmarksVerified61.0
ReasoningRank Not rankedWeight 17%1 benchmarkVerified74.8
KnowledgeRank #35 of 183Percentile 81stWeight 12%6 benchmarksVerified60.7
MathRank Not rankedWeight 5%4 benchmarksVerified80.7
MultilingualWeight 7%0 benchmarksNot measuredNot measured
MultimodalWeight 12%0 benchmarksNot measuredNot measured
Inst. FollowingWeight 5%0 benchmarksNot measuredNot measured

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.

Coding8 rows
Coding benchmark values, best verified comparison, weight, and source status
SWE-bench ProScore62.1%Versus best verified row

Best verified: Claude Fable 5.1 · 81.2%

Gap19.1 behindWeightWeighted 25%
LiveCodeBench (Vals)LiveCodeBench, Vals AI runScore69.5%Versus best verified row

Best verified: Claude Fable 5.1 · 90.5%

Gap21 behindWeightWeighted 15%
CursorBench 3.2Score55.0%Versus best verified row

Best verified: Claude Fable 5.1 · 73.4%

Gap18.4 behindWeightWeighted 10%
NL2RepoScore48.9%Versus best verified row

Best verified: DeepSeek V4.1 Flash · 65.4%

Gap16.5 behindWeightDisplay only
Terminal-Bench 2.0Score81.0%Versus best verified row

Best verified: GPT-5.6 Sol · 91.9%

Gap10.9 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
Agentic6 rows
Agentic benchmark values, best verified comparison, weight, and source status
Terminal-Bench 2.0Score81%Versus best verified row

Best verified: GPT-5.6 Sol · 91.9%

Gap10.9 behindWeightWeighted 30%
Terminal-Bench 3.0Score4.6%Versus best verified row

Best verified: Claude Opus 5 · 42.7%

Gap38.1 behindWeightDisplay only
MCP AtlasScore76.8%Versus best verified row

Best verified: Muse Spark 1.1 · 88.1%

Gap11.3 behindWeightDisplay only
ToolathlonScore48.2%Versus best verified row

Best verified: Muse Spark 1.1 · 75.6%

Gap27.4 behindWeightDisplay only
ResearchClawBenchScore20.7%Versus best verified row

Best verified: Claude Opus 4.8 · 21.1%

Gap0.4 behindWeightDisplay only
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 behindWeightDisplay only
Reasoning1 row
Reasoning benchmark values, best verified comparison, weight, and source status
CritPtCritical Physics TasksScore20.9%Versus best verified row

Best verified: GPT-6 Astra · 31.7%

Gap10.8 behindWeightDisplay only
Knowledge6 rows
Knowledge benchmark values, best verified comparison, weight, and source status
HLEHumanity's Last ExamScore54.7%Versus best verified row

Best verified: Claude Fable 5.1 · 65%

Gap10.3 behindWeightWeighted 35%
HLE w/o toolsHumanity's Last Exam without toolsScore40.5%Versus best verified row

Best verified: Claude Fable 5.1 · 60.9%

Gap20.4 behindWeightWeighted 10%
MMLU-Pro (Vals)MMLU-Pro, Vals AI runScore86.7%Versus best verified row

Best verified: Claude Fable 5.1 · 92.4%

Gap5.7 behindWeightWeighted 10%
GPQAGraduate-Level Google-Proof Q&AScore91.2%Versus best verified row

Best verified: GPT-6 Astra · 96%

Gap4.8 behindWeightWeighted 7%
GPQA-DGPQA DiamondScore91.2%Versus best verified row

Best verified: GPT-6 Astra · 96.0%

Gap4.8 behindWeightDisplay only
GPQA Diamond (Vals)GPQA Diamond, Vals AI runScore85.6%Versus best verified row

Best verified: Gemini 3.1 Pro · 95.5%

Gap9.9 behindWeightDisplay only
Math4 rows
Math benchmark values, best verified comparison, weight, and source status
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%
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

Lineage

The sequence follows explicit supersedes links. A successor's displayed score stays at least 0.1 points above its predecessor; raw benchmark rows do not move. Scores and prices remain blank when the corresponding public row or first-party rate is unavailable.

  1. Mar 1, 2026

    GLM-5

    Score 61.5 · $1 / $3.2

  2. Apr 7, 2026

    GLM-5.1

    Score 63.3 · $1.4 / $4.4

  3. Jun 16, 2026 · you are here

    GLM-5.2

    Score 68.2 · $1.4 / $4.4

  4. Aug 14, 2026

    GLM-5.3

    Score 68.4 · Price not listed

flagship · 5.2

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 #26 of 232 on the public leaderboard with a score of 68.19/100. Its source-verified position is #20 of 130.

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. 25 of 435 tracked benchmark slots currently have displayable evidence. Missing categories stay blank.

Its strongest eligible category is Coding at #19, while its lowest eligible position is Knowledge at #35. particularly well-suited for software development and code generation tasks.

Radar

GLM-5.2 release history

Full release history

Radar confirmed these at the source. Use GLM-5.2 in your work? Explore Radar to follow supported changes and choose your alerts.

Frequently asked questions

How does GLM-5.2 perform overall in AI benchmarks?

GLM-5.2 ranks #26 out of 232 models on the public BenchAlign leaderboard, with a score of 68.19/100. Its evidence status is Supported, and this profile shows 25 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 #35 out of 183 eligible models for knowledge and understanding, with a public category score of 60.7/100. 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 coding and programming?

GLM-5.2 ranks #19 out of 152 eligible models for coding and programming, with a public category score of 61/100. 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 has source-displayable benchmark coverage for mathematics, 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 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 #29 out of 153 eligible models for agentic tool use and computer tasks, with a public category score of 58.5/100. 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 42 source-displayable rows across 435 tracked benchmark slots. 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.

Compare GLM-5.2 with every tracked model483 comparisons

Last updated September 14, 2026. Runtime fields remain blank until a sourced snapshot exists.

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