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GLM 4.6 vs GPT-5.2

GPT-5.2 leads GLM 4.6 on coding, 55.9 to 8.8 on the Vibe Coding Index, and GLM 4.6 costs 6.4x less per output token ($2.20 vs $14.00 per million). Updated October 22, 2026; 67 benchmarks have a result for both.

Replace GLM 4.6 Replace GPT-5.2
GLM 4.6Z.ai
Coding
8.8
Overall
33.4
Output $/M
$2.20
30
GPT-5.2OpenAI
Coding
55.9
Overall
60.7
Output $/M
$14.00
37
vs boards won of 67 shared

GLM 4.6 or GPT-5.2: which to pick

Pick GPT-5.2 for coding: it scores 55.9 on the Vibe Coding Index against 8.8 for GLM 4.6.

Pick GLM 4.6 for output tokens 6.4x cheaper ($2.20 vs $14.00 per million) and work measured by LMArena Text: coding or LMArena Text: software and IT, where it leads.

Model AGLM 4.6Z.ai8.8/1002 independent benchmarksNo cost per solved taskModel BGPT-5.2OpenAI55.9/1003 independent benchmarks$1.91 per solved task
Shared results
67
GLM 4.6 only
2
GPT-5.2 only
47
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 4.6 8.8 from 2 benchmarks, GPT-5.2 55.9 from 3 benchmarks.
Widest category
Coding, 18.0 points apart on average over 11 shared benchmarks.
Widest single gap
Vibe Code Bench (Vals AI): GLM 4.6 3.1% against GPT-5.2 53.5%, 50.4 points apart on a 0 to 100 scale.
Output price
GLM 4.6 $2.20 against GPT-5.2 $14.00 per million output tokens, 6.4x apart.
Context window
GLM 4.6 takes 205K tokens, GPT-5.2 400K.

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 4.6BarsGPT-5.2Ties
Coding29
Intelligence01
Agentic work42
Reasoning25
Math15
Knowledge46
Web design16
General163
All shared boards3037
CodingLike-for-likeSix of these boards feed the Vibe Coding Index29
Coding benchmarks for GLM 4.6 and GPT-5.2
BenchmarkGLM 4.6GPT-5.2Leader
SWE-bench Verified68.2%72.8%GPT-5.2
SWE-bench MultilingualNot measured66.7%Only GPT-5.2 measured
LMArena WebDev13411417GPT-5.2
AA Coding Index45.8Not measuredOnly GLM 4.6 measured
LiveCodeBench (Vals AI)81.0%85.4%GPT-5.2
SWE-bench Verified (Vals AI)Not measured75.8%Only GPT-5.2 measured
Vibe Code Bench (Vals AI)3.1%53.5%GPT-5.2
LiveCodeBench (Artificial Analysis run)69.5%89.4%GPT-5.2
Terminal-Bench Hard (Artificial Analysis run)28.8%47.0%GPT-5.2
Terminal-Bench24.5%64.9%GPT-5.2
GSO-BenchNot measured27.5%Only GPT-5.2 measured
WeirdMLNot measured72.2%Only GPT-5.2 measured
ALE-Bench340.81293.5GPT-5.2
LMArena Text: coding14491442GLM 4.6
LMArena Text: software and IT14551434GLM 4.6
LMArena WebDev: HTML13501427GPT-5.2
Neither model has a result on the other 23 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for GLM 4.6 and GPT-5.2
BenchmarkGLM 4.6GPT-5.2Leader
AA Intelligence Index18.530.4GPT-5.2
Epoch Capabilities IndexNot measured153.7Only GPT-5.2 measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index42
Agentic work benchmarks for GLM 4.6 and GPT-5.2
BenchmarkGLM 4.6GPT-5.2Leader
Design Arena: agents building web appsNot measured1086Only GPT-5.2 measured
Design Arena: agents building full-stack apps10291043GPT-5.2
Design Arena: agents building mobile apps10961093GLM 4.6
Design Arena: agents building Android apps10841059GLM 4.6
Design Arena: agents building Godot games11801142GLM 4.6
τ²-Bench Telecom76.9%84.8%GPT-5.2
METR time horizonsNot measured75.3%Only GPT-5.2 measured
Remote Labor IndexNot measured2.5%Only GPT-5.2 measured
GDPvalNot measured49.7%Only GPT-5.2 measured
DeepResearch BenchNot measured41.1%Only GPT-5.2 measured
Vending-Bench 2Not measured$3,591Only GPT-5.2 measured
Berkeley Function Calling Leaderboard (BFCL)72.4%55.9%GLM 4.6
BrowseComp-Plus48.2%Not measuredOnly GLM 4.6 measured
Neither model has a result on the other 28 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index25
Reasoning benchmarks for GLM 4.6 and GPT-5.2
BenchmarkGLM 4.6GPT-5.2Leader
GPQA Diamond74.5%91.7%GPT-5.2
MMMU Pro (Vals AI)Not measured86.7%Only GPT-5.2 measured
GPQA Diamond (Artificial Analysis run)78.0%90.3%GPT-5.2
Humanity's Last Exam (Artificial Analysis run)14.5%37.7%GPT-5.2
GPQA Diamond (Epoch AI run)Not measured91.4%Only GPT-5.2 measured
ARC-AGINot measured86.2%Only GPT-5.2 measured
ARC-AGI-2Not measured52.9%Only GPT-5.2 measured
SimpleBenchNot measured45.8%Only GPT-5.2 measured
Chess puzzlesNot measured48.0%Only GPT-5.2 measured
Mystery game puzzlesNot measured23.0%Only GPT-5.2 measured
EBR-benchNot measured23.0%Only GPT-5.2 measured
VPCTNot measured84.0%Only GPT-5.2 measured
LMArena Text: hard prompts14401428GLM 4.6
LMArena Text: expert14321445GPT-5.2
LMArena Text: hard prompts in English14451431GLM 4.6
LMArena Vision: diagramsNot measured1265Only GPT-5.2 measured
LMArena Vision: homeworkNot measured1276Only GPT-5.2 measured
Kagi LLM Benchmark45.7%73.3%GPT-5.2
Neither model has a result on the other 4 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index15
Math benchmarks for GLM 4.6 and GPT-5.2
BenchmarkGLM 4.6GPT-5.2Leader
AIME92.7%96.9%GPT-5.2
MGSM (Vals AI)89.8%94.0%GPT-5.2
AA Math Index86.099.0GPT-5.2
AIME 2025 (Artificial Analysis run)86.0%99.0%GPT-5.2
OTIS Mock AIMENot measured96.1%Only GPT-5.2 measured
FrontierMath, tiers 1 to 3Not measured67.4%Only GPT-5.2 measured
FrontierMath, tier 4Not measured31.7%Only GPT-5.2 measured
ProofBenchNot measured15.0%Only GPT-5.2 measured
LMArena Text: math14321440GPT-5.2
LMArena Text: mathematical work14471442GLM 4.6
Neither model has a result on the other 3 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index46
Knowledge benchmarks for GLM 4.6 and GPT-5.2
BenchmarkGLM 4.6GPT-5.2Leader
MMLU Pro82.2%86.2%GPT-5.2
LegalBench (Vals AI)79.6%82.8%GPT-5.2
CaseLaw (Vals AI)Not measured66.0%Only GPT-5.2 measured
CorpFin (Vals AI)56.8%65.9%GPT-5.2
TaxEval (Vals AI)66.2%75.8%GPT-5.2
MortgageTax (Vals AI)Not measured67.1%Only GPT-5.2 measured
MedQA (Vals AI)92.2%94.1%GPT-5.2
MedCode (Vals AI)Not measured49.8%Only GPT-5.2 measured
MMLU Pro (Artificial Analysis run)82.9%87.4%GPT-5.2
SimpleQA VerifiedNot measured37.1%Only GPT-5.2 measured
LMArena Text: science14561425GLM 4.6
LMArena Text: medicine and healthcare14551425GLM 4.6
LMArena Text: legal and government14481427GLM 4.6
LMArena Text: business and finance14161414GLM 4.6
FACTS Benchmark SuiteNot measured48.5%Only GPT-5.2 measured
Neither model has a result on the other 3 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index16
Web design benchmarks for GLM 4.6 and GPT-5.2
BenchmarkGLM 4.6GPT-5.2Leader
Design Arena: websites11741195GPT-5.2
Design Arena: UI components11451190GPT-5.2
Design Arena: game dev11501195GPT-5.2
Design Arena: data viz11611202GPT-5.2
Design Arena: 3D11281079GLM 4.6
Design Arena: all code categories11651170GPT-5.2
Design Arena: SVG11081139GPT-5.2
Design Arena: ASCII artNot measured1152Only GPT-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 index163
General benchmarks for GLM 4.6 and GPT-5.2
BenchmarkGLM 4.6GPT-5.2Leader
SAGE (Vals AI)Not measured49.3%Only GPT-5.2 measured
MedScribe (Vals AI)Not measured84.4%Only GPT-5.2 measured
IFBench43.4%75.4%GPT-5.2
Long context reasoning (AA-LCR)54.0%82.7%GPT-5.2
DTBenchNot measured90.9%Only GPT-5.2 measured
LMCANot measured43.9%Only GPT-5.2 measured
CL-benchNot measured18.2%Only GPT-5.2 measured
Furniture assemblyNot measured38.3%Only GPT-5.2 measured
LMArena Text14401416GLM 4.6
LMArena Text: instruction following14101409GLM 4.6
LMArena Text: creative writing14121376GLM 4.6
LMArena VisionNot measured1246Only GPT-5.2 measured
LMArena Text: English14491422GLM 4.6
LMArena Text: non-English14261405GLM 4.6
LMArena Text: Chinese14991447GLM 4.6
LMArena Text: German14471444GLM 4.6
LMArena Text: Japanese13931420GPT-5.2
LMArena Text: Korean14001374GLM 4.6
LMArena Text: French14591454GLM 4.6
LMArena Text: Spanish14361413GLM 4.6
LMArena Text: Russian14181415GLM 4.6
LMArena Text: Polish14471426GLM 4.6
LMArena Text: multi-turn14271419GLM 4.6
LMArena Text: longer queries14221413GLM 4.6
LMArena Text: writing and language14151392GLM 4.6
LMArena Text: entertainment and media14091378GLM 4.6
LMArena Vision: EnglishNot measured1243Only GPT-5.2 measured
LMArena Vision: ChineseNot measured1305Only GPT-5.2 measured
LMArena Vision: reading textNot measured1257Only GPT-5.2 measured
LMArena Vision: humorNot measured1227Only GPT-5.2 measured
LMArena Vision: captioningNot measured1267Only GPT-5.2 measured
LMArena Vision: recognizing thingsNot measured1232Only GPT-5.2 measured
LMArena Vision: creative writingNot measured1225Only GPT-5.2 measured
LMArena DocumentNot measured1405Only GPT-5.2 measured
Neither model has a result on the other 12 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 GPT-5.2
BenchmarkGLM 4.6GPT-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$0.0017Fits in one request
GPT-5.2$0.0088Fits in one request

GLM 4.6 costs less

Repository review

50K input + 3K output tokens

GLM 4.6$0.037Fits in one request
GPT-5.2$0.130Fits in one request

GLM 4.6 costs less

Agent loop

200K input + 10K output tokens

GLM 4.6$0.142Exceeds the context window
GPT-5.2$0.490Fits in one request

GLM 4.6 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 GPT-5.2
SpecificationZ.ai GLM 4.6OpenAI GPT-5.2
MakerZ.aiOpenAI
WeightsOpen weightsNo public weights listed
ReleasedSeptember 30, 2025December 10, 2025
Context window205K400K
Input $/Mtok$0.60$1.75
Output $/Mtok$2.20$14.00
Coding index8.855.9
Independent measurements behind it23
Cost per solved taskNot computable$1.91
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.6 better than GPT-5.2 for coding?

Pick GPT-5.2 for coding: it scores 55.9 on the Vibe Coding Index against 8.8 for GLM 4.6. Pick GLM 4.6 for output tokens 6.4x cheaper ($2.20 vs $14.00 per million) and work measured by LMArena Text: coding or LMArena Text: software and IT, where it leads.

Which is cheaper, GLM 4.6 or GPT-5.2?

GLM 4.6 costs $0.60 per million input tokens and $2.20 per million output tokens; GPT-5.2 costs $1.75 and $14.00. Cost per solved task is known for GPT-5.2 only.

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

GPT-5.2, with 400K tokens against 205K.

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

67 benchmarks have a result for both models as of October 22, 2026. GLM 4.6's coding index rests on 2 independent benchmarks and GPT-5.2's on 3. Where only one model has a result, the page says so instead of naming a winner.

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