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GLM 4.6 vs GLM 5.2

Updated October 21, 2026. 57 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace GLM 4.6 Replace GLM 5.2
GLM 4.6Z.ai
Coding
52.2
Overall
18.5
Output $/M
$2.20
2
GLM 5.2Z.ai
Coding
57.7
Overall
33.7
Output $/M
$7.00
55
vs boards won of 57 shared

Decision reading

Pick GLM 5.2 for coding: it scores 57.7 on the Vibe Coding Index against 52.2 for GLM 4.6, though on 1 benchmark against 2.

Pick GLM 4.6 when output tokens cost 3.2x less, or when what matters is LiveCodeBench (Vals AI) or Design Arena: agents building Godot games, where it leads.

Model AGLM 4.6Z.ai52.2/1002 independent benchmarksNo cost per solved taskModel BGLM 5.2Z.ai57.7/1001 independent benchmark$0.86 per solved task
Shared results
57
GLM 4.6 only
9
GLM 5.2 only
40
Like-for-like categories
8 of 9

Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.

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 4.6 52.2 from 2 benchmarks, GLM 5.2 57.7 from 1 benchmark.
Widest category
Coding, 25.1 points apart on average over 9 shared benchmarks.
Widest single gap
Vibe Code Bench (Vals AI): GLM 4.6 3.1% against GLM 5.2 64.0%, 60.9 points apart on a 0 to 100 scale.
Output price
GLM 4.6 $2.20 against GLM 5.2 $7.00 per million output tokens, 3.2x apart.
Context window
GLM 4.6 takes 205K tokens, GLM 5.2 1.05M.

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 4.6BarsGLM 5.2Ties
Coding18
Intelligence01
Agentic work14
Reasoning06
Math02
Knowledge08
Web design07
General019
All shared boards255
CodingLike-for-likeFour of these boards feed the Vibe Coding Index18
Coding benchmarks for GLM 4.6 and GLM 5.2
BenchmarkGLM 4.6GLM 5.2Leader
SWE-bench Verified68.2%Not measuredOnly GLM 4.6 measured
LMArena WebDev13411604GLM 5.2
AA Coding Index45.868.8GLM 5.2
LiveCodeBench (Vals AI)81.0%69.5%GLM 4.6
SWE-bench Verified (Vals AI)Not measured82.8%Only GLM 5.2 measured
Vibe Code Bench (Vals AI)3.1%64.0%GLM 5.2
Code migration (Vals AI)Not measured37.9%Only GLM 5.2 measured
ProgramBench (Vals AI)Not measured0.5%Only GLM 5.2 measured
Terminal-Bench 2.1 (Vals AI)Not measured67.8%Only GLM 5.2 measured
SkillsBench (Vals AI)Not measured45.1%Only GLM 5.2 measured
LiveCodeBench (Artificial Analysis run)69.5%Not measuredOnly GLM 4.6 measured
SciCodeNot measured51.2%Only GLM 5.2 measured
Terminal-Bench Hard (Artificial Analysis run)28.8%50.8%GLM 5.2
Terminal-Bench24.5%Not measuredOnly GLM 4.6 measured
FrontierCodeNot measured24.5%Only GLM 5.2 measured
WeirdMLNot measured70.1%Only GLM 5.2 measured
ALE-Bench340.81047.0GLM 5.2
LMArena Text: coding14491487GLM 5.2
LMArena Text: software and IT14551486GLM 5.2
LMArena WebDev: ReactNot measured1617Only GLM 5.2 measured
LMArena WebDev: HTML13501565GLM 5.2
Neither model has a result on the other 17 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for GLM 4.6 and GLM 5.2
BenchmarkGLM 4.6GLM 5.2Leader
AA Intelligence Index18.533.7GLM 5.2
Epoch Capabilities IndexNot measured151.9Only GLM 5.2 measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index14
Agentic work benchmarks for GLM 4.6 and GLM 5.2
BenchmarkGLM 4.6GLM 5.2Leader
AA Agentic IndexNot measured38.4Only GLM 5.2 measured
Design Arena: agents building web appsNot measured1222Only GLM 5.2 measured
Design Arena: agents building full-stack apps10291241GLM 5.2
Design Arena: agents building mobile apps10961160GLM 5.2
Design Arena: agents building Android apps10901184GLM 5.2
Design Arena: agents building Godot games11801142GLM 4.6
Design Arena: agents building gamesNot measured1206Only GLM 5.2 measured
Design Arena: HTML slidesNot measured1175Only GLM 5.2 measured
Design Arena: PowerPoint slidesNot measured1195Only GLM 5.2 measured
SREBench (Vals AI)Not measured0.0%Only GLM 5.2 measured
Finance Agent (Vals AI)Not measured49.7%Only GLM 5.2 measured
Legal Research Bench (Vals AI)Not measured31.3%Only GLM 5.2 measured
Excel Modeling Benchmark (Vals AI)Not measured61.5%Only GLM 5.2 measured
τ²-Bench Telecom76.9%99.1%GLM 5.2
APEX-AgentsNot measured45.2%Only GLM 5.2 measured
PostTrainBenchNot measured31.7%Only GLM 5.2 measured
Vending-Bench 2Not measured$8,314Only GLM 5.2 measured
Neither model has a result on the other 22 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index06
Reasoning benchmarks for GLM 4.6 and GLM 5.2
BenchmarkGLM 4.6GLM 5.2Leader
GPQA Diamond74.5%85.6%GLM 5.2
GPQA Diamond (Artificial Analysis run)78.0%89.5%GLM 5.2
Humanity's Last Exam (Artificial Analysis run)14.5%41.1%GLM 5.2
GPQA Diamond (Epoch AI run)Not measured91.9%Only GLM 5.2 measured
ARC-AGINot measured77.0%Only GLM 5.2 measured
ARC-AGI-2Not measured22.8%Only GLM 5.2 measured
SimpleBenchNot measured58.8%Only GLM 5.2 measured
Chess puzzlesNot measured22.0%Only GLM 5.2 measured
Mystery game puzzlesNot measured19.0%Only GLM 5.2 measured
EBR-benchNot measured9.5%Only GLM 5.2 measured
LMArena Text: hard prompts14401481GLM 5.2
LMArena Text: expert14321487GLM 5.2
LMArena Text: hard prompts in English14451481GLM 5.2
Neither model has a result on the other 8 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for GLM 4.6 and GLM 5.2
BenchmarkGLM 4.6GLM 5.2Leader
AIME92.7%Not measuredOnly GLM 4.6 measured
MGSM (Vals AI)89.8%Not measuredOnly GLM 4.6 measured
AA Math Index86.0Not measuredOnly GLM 4.6 measured
AIME 2025 (Artificial Analysis run)86.0%Not measuredOnly GLM 4.6 measured
OTIS Mock AIMENot measured86.4%Only GLM 5.2 measured
FrontierMath, tiers 1 to 3Not measured59.2%Only GLM 5.2 measured
FrontierMath, tier 4Not measured29.3%Only GLM 5.2 measured
ProofBenchNot measured35.0%Only GLM 5.2 measured
LMArena Text: math14321483GLM 5.2
LMArena Text: mathematical work14471489GLM 5.2
Neither model has a result on the other 3 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index08
Knowledge benchmarks for GLM 4.6 and GLM 5.2
BenchmarkGLM 4.6GLM 5.2Leader
MMLU Pro82.2%86.7%GLM 5.2
LegalBench (Vals AI)79.6%84.1%GLM 5.2
CorpFin (Vals AI)56.8%66.1%GLM 5.2
TaxEval (Vals AI)66.2%73.3%GLM 5.2
MedQA (Vals AI)92.2%Not measuredOnly GLM 4.6 measured
MedCode (Vals AI)Not measured40.8%Only GLM 5.2 measured
MMLU Pro (Artificial Analysis run)82.9%Not measuredOnly GLM 4.6 measured
SimpleQA VerifiedNot measured34.2%Only GLM 5.2 measured
LMArena Text: science14561489GLM 5.2
LMArena Text: medicine and healthcare14551475GLM 5.2
LMArena Text: legal and government14481486GLM 5.2
LMArena Text: business and finance14161451GLM 5.2
Neither model has a result on the other 5 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index07
Web design benchmarks for GLM 4.6 and GLM 5.2
BenchmarkGLM 4.6GLM 5.2Leader
Design Arena: websites11741285GLM 5.2
Design Arena: UI components11471296GLM 5.2
Design Arena: game dev11511268GLM 5.2
Design Arena: data viz11611285GLM 5.2
Design Arena: 3D11291288GLM 5.2
Design Arena: all code categories11661288GLM 5.2
Design Arena: SVG11081214GLM 5.2
Design Arena: ASCII artNot measured1190Only GLM 5.2 measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index019
General benchmarks for GLM 4.6 and GLM 5.2
BenchmarkGLM 4.6GLM 5.2Leader
MedScribe (Vals AI)Not measured83.5%Only GLM 5.2 measured
IFBench43.4%73.3%GLM 5.2
Long context reasoning (AA-LCR)54.0%78.3%GLM 5.2
DTBenchNot measured93.6%Only GLM 5.2 measured
LMCANot measured45.8%Only GLM 5.2 measured
Surface Evolver BenchNot measured55.6%Only GLM 5.2 measured
LMArena Text14401470GLM 5.2
LMArena Text: instruction following14101466GLM 5.2
LMArena Text: creative writing14121463GLM 5.2
LMArena Text: English14491475GLM 5.2
LMArena Text: non-English14261459GLM 5.2
LMArena Text: Chinese14991522GLM 5.2
LMArena Text: German14471470GLM 5.2
LMArena Text: Japanese13931450GLM 5.2
LMArena Text: Korean14001440GLM 5.2
LMArena Text: French14591479GLM 5.2
LMArena Text: Spanish14361478GLM 5.2
LMArena Text: Russian14181468GLM 5.2
LMArena Text: Polish14471456GLM 5.2
LMArena Text: multi-turn14271469GLM 5.2
LMArena Text: longer queries14221480GLM 5.2
LMArena Text: writing and language14151463GLM 5.2
LMArena Text: entertainment and media14091450GLM 5.2
Neither model has a result on the other 23 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 4.6 and GLM 5.2
BenchmarkGLM 4.6GLM 5.2Leader
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 4.6$0.0016Fits in one request
GLM 5.2$0.0035Fits in one request

GLM 4.6 costs less

Repository review

50K input + 3K output tokens

GLM 4.6$0.034Fits in one request
GLM 5.2$0.023Fits in one request

GLM 5.2 costs less

Agent loop

200K input + 10K output tokens

GLM 4.6$0.132Exceeds the context window
GLM 5.2$0.077Fits in one request

GLM 5.2 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 4.6 and GLM 5.2
SpecificationZ.ai GLM 4.6Z.ai GLM 5.2
MakerZ.aiZ.ai
WeightsOpen weightsOpen weights
ReleasedSeptember 30, 2025June 16, 2026
Context window205K1.05M
Input $/Mtok$0.55$0.0341
Output $/Mtok$2.20$7.00
Coding index52.257.7
Independent measurements behind it21
Cost per solved taskNot computable$0.86
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.6 better than GLM 5.2 for coding?

Pick GLM 5.2 for coding: it scores 57.7 on the Vibe Coding Index against 52.2 for GLM 4.6, though on 1 benchmark against 2. Pick GLM 4.6 when output tokens cost 3.2x less, or when what matters is LiveCodeBench (Vals AI) or Design Arena: agents building Godot games, where it leads.

Which is cheaper, GLM 4.6 or GLM 5.2?

GLM 4.6 costs $0.55 per million input tokens and $2.20 per million output tokens; GLM 5.2 costs $0.0341 and $7.00. Cost per solved task is known for GLM 5.2 only.

Which has the bigger context window, GLM 4.6 or GLM 5.2?

GLM 5.2, with 1.05M tokens against 205K.

How many benchmarks is the GLM 4.6 vs GLM 5.2 comparison based on?

57 benchmarks have a result for both models as of October 21, 2026. GLM 4.6's coding index rests on 2 independent benchmarks and GLM 5.2's on 1. Where only one model has a result, the page says so instead of naming a winner.

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