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GLM 5.2 vs Grok 4.3

GLM 5.2 leads Grok 4.3 on coding, 61.1 to 37.4 on the Vibe Coding Index, and Grok 4.3 costs 1.8x less per output token ($2.50 vs $4.40 per million). Updated October 22, 2026; 86 benchmarks have a result for both.

Replace GLM 5.2 Replace Grok 4.3
GLM 5.2Z.ai
Coding
61.1
Overall
62.5
Output $/M
$4.40
79
Grok 4.3xAI
Coding
37.4
Overall
53.1
Output $/M
$2.50
7
vs boards won of 86 shared

GLM 5.2 or Grok 4.3: which to pick

Pick GLM 5.2 for coding: it scores 61.1 on the Vibe Coding Index against 37.4 for Grok 4.3.

Pick Grok 4.3 for output tokens 1.8x cheaper ($2.50 vs $4.40 per million) and a lower cost per solved task ($0.31 against $0.86).

Model AGLM 5.2Z.ai61.1/1004 independent benchmarks$0.86 per solved taskModel BGrok 4.3xAI37.4/1003 independent benchmarks$0.31 per solved task
Shared results
86
GLM 5.2 only
12
Grok 4.3 only
19
Like-for-like categories
8 of 9

Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.

Which one for your work

Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.

At a glance

Coding index
GLM 5.2 61.1 from 4 benchmarks, Grok 4.3 37.4 from 3 benchmarks.
Widest category
Agentic work, 23.2 points apart on average over 14 shared benchmarks.
Widest single gap
Vending-Bench 2: GLM 5.2 $8,314 against Grok 4.3 $35, 51.7 points apart on a 0 to 100 scale.
Output price
GLM 5.2 $4.40 against Grok 4.3 $2.50 per million output tokens, 1.8x apart.
Context window
GLM 5.2 takes 1.05M tokens, Grok 4.3 1M.

Benchmark by benchmark

First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.

Boards won, where both have a result
CategoryGLM 5.2BarsGrok 4.3Ties
Coding161
Intelligence10
Agentic work140
Reasoning62
Math51
Knowledge82
Web design80
General211
All shared boards797
CodingLike-for-likeSix of these boards feed the Vibe Coding Index161
Coding benchmarks for GLM 5.2 and Grok 4.3
BenchmarkGLM 5.2Grok 4.3Leader
LMArena WebDev16041358GLM 5.2
AA Coding Index68.842.2GLM 5.2
LiveCodeBench (Vals AI)69.5%84.5%Grok 4.3
SWE-bench Verified (Vals AI)82.8%71.4%GLM 5.2
Vibe Code Bench (Vals AI)64.0%19.4%GLM 5.2
Code migration (Vals AI)37.9%6.8%GLM 5.2
ProgramBench (Vals AI)0.5%0.0%GLM 5.2
Terminal-Bench 2.1 (Vals AI)67.8%42.0%GLM 5.2
SkillsBench (Vals AI)45.1%40.6%GLM 5.2
SciCode51.2%48.3%GLM 5.2
Terminal-Bench Hard (Artificial Analysis run)50.8%37.9%GLM 5.2
FrontierCode24.5%Not measuredOnly GLM 5.2 measured
WeirdML70.1%49.9%GLM 5.2
ALE-Bench1047.0944.2GLM 5.2
LMArena Text: coding14871415GLM 5.2
LMArena Text: software and IT14861416GLM 5.2
LMArena WebDev: React16171353GLM 5.2
LMArena WebDev: HTML15651367GLM 5.2
LMArena WebDev: from an imageNot measured1375Only Grok 4.3 measured
Neither model has a result on the other 20 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for GLM 5.2 and Grok 4.3
BenchmarkGLM 5.2Grok 4.3Leader
AA Intelligence Index33.724.9GLM 5.2
Epoch Capabilities Index151.9Not measuredOnly GLM 5.2 measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index140
Agentic work benchmarks for GLM 5.2 and Grok 4.3
BenchmarkGLM 5.2Grok 4.3Leader
AA Agentic Index38.415.5GLM 5.2
Design Arena: agents building web apps12221126GLM 5.2
Design Arena: agents building full-stack apps12411006GLM 5.2
Design Arena: agents building mobile apps11591058GLM 5.2
Design Arena: agents building Android apps1178962GLM 5.2
Design Arena: agents building Godot games1142914GLM 5.2
Design Arena: agents building games12061007GLM 5.2
Design Arena: HTML slides1125959GLM 5.2
Design Arena: PowerPoint slides1170988GLM 5.2
Design Arena: spreadsheetsNot measured879Only Grok 4.3 measured
SREBench (Vals AI)0.0%Not measuredOnly GLM 5.2 measured
Finance Agent (Vals AI)49.7%37.7%GLM 5.2
Tax Agent Bench (Vals AI)Not measured6.2%Only Grok 4.3 measured
Legal Research Bench (Vals AI)31.3%15.4%GLM 5.2
Excel Modeling Benchmark (Vals AI)61.5%18.4%GLM 5.2
τ²-Bench Telecom99.1%97.7%GLM 5.2
APEX-Agents45.2%Not measuredOnly GLM 5.2 measured
PostTrainBench31.7%Not measuredOnly GLM 5.2 measured
Vending-Bench 2$8,314$35GLM 5.2
LMArena SearchNot measured1166Only Grok 4.3 measured
Neither model has a result on the other 21 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index62
Reasoning benchmarks for GLM 5.2 and Grok 4.3
BenchmarkGLM 5.2Grok 4.3Leader
GPQA Diamond85.6%91.4%Grok 4.3
MMMU Pro (Vals AI)Not measured83.1%Only Grok 4.3 measured
GPQA Diamond (Artificial Analysis run)89.5%90.1%Grok 4.3
Humanity's Last Exam (Artificial Analysis run)41.1%37.2%GLM 5.2
GPQA Diamond (Epoch AI run)91.9%88.8%GLM 5.2
ARC-AGI77.0%Not measuredOnly GLM 5.2 measured
ARC-AGI-222.8%Not measuredOnly GLM 5.2 measured
SimpleBench58.8%Not measuredOnly GLM 5.2 measured
Chess puzzles22.0%18.0%GLM 5.2
Mystery game puzzles19.0%Not measuredOnly GLM 5.2 measured
EBR-bench9.5%Not measuredOnly GLM 5.2 measured
LMArena Text: hard prompts14811396GLM 5.2
LMArena Text: expert14871384GLM 5.2
LMArena Text: hard prompts in English14811402GLM 5.2
LMArena Vision: diagramsNot measured1241Only Grok 4.3 measured
LMArena Vision: homeworkNot measured1241Only Grok 4.3 measured
Kagi LLM Benchmark62.6%Not measuredOnly GLM 5.2 measured
Neither model has a result on the other 5 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index51
Math benchmarks for GLM 5.2 and Grok 4.3
BenchmarkGLM 5.2Grok 4.3Leader
OTIS Mock AIME86.4%93.3%Grok 4.3
FrontierMath, tiers 1 to 359.2%42.8%GLM 5.2
FrontierMath, tier 429.3%14.6%GLM 5.2
ProofBench35.0%11.0%GLM 5.2
LMArena Text: math14831387GLM 5.2
LMArena Text: mathematical work14891396GLM 5.2
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index82
Knowledge benchmarks for GLM 5.2 and Grok 4.3
BenchmarkGLM 5.2Grok 4.3Leader
MMLU Pro86.7%85.8%GLM 5.2
LegalBench (Vals AI)84.1%84.5%Grok 4.3
CaseLaw (Vals AI)Not measured79.3%Only Grok 4.3 measured
CorpFin (Vals AI)66.1%68.5%Grok 4.3
TaxEval (Vals AI)73.3%70.8%GLM 5.2
MortgageTax (Vals AI)Not measured48.3%Only Grok 4.3 measured
MedCode (Vals AI)40.8%38.1%GLM 5.2
SimpleQA Verified34.2%33.2%GLM 5.2
LMArena Text: science14891399GLM 5.2
LMArena Text: medicine and healthcare14751384GLM 5.2
LMArena Text: legal and government14861386GLM 5.2
LMArena Text: business and finance14511381GLM 5.2
Neither model has a result on the other 6 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index80
Web design benchmarks for GLM 5.2 and Grok 4.3
BenchmarkGLM 5.2Grok 4.3Leader
Design Arena: websites12851196GLM 5.2
Design Arena: UI components12941190GLM 5.2
Design Arena: game dev12681173GLM 5.2
Design Arena: data viz12851179GLM 5.2
Design Arena: 3D12871124GLM 5.2
Design Arena: all code categories12871186GLM 5.2
Design Arena: SVG12131080GLM 5.2
Design Arena: ASCII art11911125GLM 5.2
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index211
General benchmarks for GLM 5.2 and Grok 4.3
BenchmarkGLM 5.2Grok 4.3Leader
Public Benefits Bench (Vals AI)Not measured51.7%Only Grok 4.3 measured
SAGE (Vals AI)Not measured19.7%Only Grok 4.3 measured
MedScribe (Vals AI)83.5%74.4%GLM 5.2
Vals Multimodal IndexNot measured43.3%Only Grok 4.3 measured
IFBench73.3%83.3%Grok 4.3
Long context reasoning (AA-LCR)78.3%75.0%GLM 5.2
DTBench93.6%90.7%GLM 5.2
LMCA45.8%38.3%GLM 5.2
Surface Evolver Bench55.6%Not measuredOnly GLM 5.2 measured
LMArena Text14701397GLM 5.2
LMArena Text: instruction following14661366GLM 5.2
LMArena Text: creative writing14631380GLM 5.2
LMArena VisionNot measured1229Only Grok 4.3 measured
LMArena Text: English14751404GLM 5.2
LMArena Text: non-English14591385GLM 5.2
LMArena Text: Chinese15221422GLM 5.2
LMArena Text: German14701395GLM 5.2
LMArena Text: Japanese14501379GLM 5.2
LMArena Text: Korean14401354GLM 5.2
LMArena Text: French14791411GLM 5.2
LMArena Text: Spanish14781399GLM 5.2
LMArena Text: Russian14681399GLM 5.2
LMArena Text: Polish14561398GLM 5.2
LMArena Text: multi-turn14691406GLM 5.2
LMArena Text: longer queries14801393GLM 5.2
LMArena Text: writing and language14631385GLM 5.2
LMArena Text: entertainment and media14501370GLM 5.2
LMArena Vision: EnglishNot measured1235Only Grok 4.3 measured
LMArena Vision: ChineseNot measured1257Only Grok 4.3 measured
LMArena Vision: reading textNot measured1235Only Grok 4.3 measured
LMArena Vision: humorNot measured1222Only Grok 4.3 measured
LMArena Vision: recognizing thingsNot measured1217Only Grok 4.3 measured
LMArena Vision: creative writingNot measured1237Only Grok 4.3 measured
Neither model has a result on the other 13 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 5.2 and Grok 4.3
BenchmarkGLM 5.2Grok 4.3Leader
Neither model has a result on 5 boards here

What each workload costs

Three fixed token mixes at each model's listed price, so per-token rates become a bill.

Chat turn

1K input + 500 output tokens

GLM 5.2$0.0036Fits in one request
Grok 4.3$0.0025Fits in one request

Grok 4.3 costs less

Repository review

50K input + 3K output tokens

GLM 5.2$0.083Fits in one request
Grok 4.3$0.070Fits in one request

Grok 4.3 costs less

Agent loop

200K input + 10K output tokens

GLM 5.2$0.324Fits in one request
Grok 4.3$0.275Fits in one request

Grok 4.3 costs less

Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.

Specification differences

The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.

Specifications of GLM 5.2 and Grok 4.3
SpecificationZ.ai GLM 5.2SpaceXAI Grok 4.3
MakerZ.aixAI
WeightsOpen weightsNo public weights listed
ReleasedJune 16, 2026April 30, 2026
Context window1.05M1M
Input $/Mtok$1.40$1.25
Output $/Mtok$4.40$2.50
Coding index61.137.4
Independent measurements behind it43
Cost per solved task$0.86$0.31
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 5.2 better than Grok 4.3 for coding?

Pick GLM 5.2 for coding: it scores 61.1 on the Vibe Coding Index against 37.4 for Grok 4.3. Pick Grok 4.3 for output tokens 1.8x cheaper ($2.50 vs $4.40 per million) and a lower cost per solved task ($0.31 against $0.86).

Which is cheaper, GLM 5.2 or Grok 4.3?

GLM 5.2 costs $1.40 per million input tokens and $4.40 per million output tokens; Grok 4.3 costs $1.25 and $2.50. Per solved SWE-bench Verified task, GLM 5.2 costs $0.86 and Grok 4.3 $0.31.

Which has the bigger context window, GLM 5.2 or Grok 4.3?

GLM 5.2, with 1.05M tokens against 1M.

How many benchmarks is the GLM 5.2 vs Grok 4.3 comparison based on?

86 benchmarks have a result for both models as of October 22, 2026. GLM 5.2's coding index rests on 4 independent benchmarks and Grok 4.3's on 3. Where only one model has a result, the page says so instead of naming a winner.

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