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GLM 4.7 vs GPT-5.2

GPT-5.2 leads GLM 4.7 on coding, 55.9 to 55.1 on the Vibe Coding Index, and GLM 4.7 costs 6.4x less per output token ($2.20 vs $14.00 per million). Updated October 21, 2026; 77 benchmarks have a result for both.

Replace GLM 4.7 Replace GPT-5.2
GLM 4.7Z.ai
Coding
55.1
Overall
56.9
Output $/M
$2.20
37
GPT-5.2OpenAI
Coding
55.9
Overall
60.7
Output $/M
$14.00
39
vs boards won of 77 shared, 1 tied

GLM 4.7 or GPT-5.2: which to pick

GLM 4.7 and GPT-5.2 are level for coding, 55.1 against 55.9 on the Vibe Coding Index, so price and context decide.

Pick GLM 4.7 for output tokens 6.4x cheaper ($2.20 vs $14.00 per million) and a lower cost per solved task ($0.29 against $1.91). Pick GPT-5.2 for a larger context window (400K against 205K tokens) and work measured by LiveCodeBench (Vals AI) or SWE-bench Verified (Vals AI), where it leads.

Model AGLM 4.7Z.ai55.1/1002 independent benchmarks$0.29 per solved taskModel BGPT-5.2OpenAI55.9/1003 independent benchmarks$1.91 per solved task
Shared results
77
GLM 4.7 only
4
GPT-5.2 only
37
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 4.7 55.1 from 2 benchmarks, GPT-5.2 55.9 from 3 benchmarks.
Widest category
Intelligence, 10.4 points apart on average over 2 shared benchmarks.
Widest single gap
Terminal-Bench: GLM 4.7 33.4% against GPT-5.2 64.9%, 31.6 points apart on a 0 to 100 scale.
Output price
GLM 4.7 $2.20 against GPT-5.2 $14.00 per million output tokens, 6.4x apart.
Context window
GLM 4.7 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 4.7BarsGPT-5.2Ties
Coding451 tie
Intelligence02
Agentic work42
Reasoning36
Math08
Knowledge58
Web design62
General156
All shared boards37391 tie
CodingLike-for-likeSix of these boards feed the Vibe Coding Index45
Coding benchmarks for GLM 4.7 and GPT-5.2
BenchmarkGLM 4.7GPT-5.2Leader
SWE-bench VerifiedNot measured72.8%Only GPT-5.2 measured
SWE-bench MultilingualNot measured66.7%Only GPT-5.2 measured
LMArena WebDev14351417GLM 4.7
AA Coding Index45.3Not measuredOnly GLM 4.7 measured
LiveCodeBench (Vals AI)82.2%85.4%GPT-5.2
SWE-bench Verified (Vals AI)69.4%75.8%GPT-5.2
Vibe Code Bench (Vals AI)Not measured53.5%Only GPT-5.2 measured
LiveCodeBench (Artificial Analysis run)89.4%89.4%Tie
Terminal-Bench Hard (Artificial Analysis run)31.8%47.0%GPT-5.2
Terminal-Bench33.4%64.9%GPT-5.2
GSO-BenchNot measured27.5%Only GPT-5.2 measured
WeirdMLNot measured72.2%Only GPT-5.2 measured
ALE-Bench399.51293.5GPT-5.2
LMArena Text: coding14541442GLM 4.7
LMArena Text: software and IT14531434GLM 4.7
LMArena WebDev: React1420Not measuredOnly GLM 4.7 measured
LMArena WebDev: HTML14471427GLM 4.7
Neither model has a result on the other 22 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for GLM 4.7 and GPT-5.2
BenchmarkGLM 4.7GPT-5.2Leader
AA Intelligence Index22.230.4GPT-5.2
Epoch Capabilities Index143.6153.7GPT-5.2
Agentic workLike-for-likePublic boards, shown as published, kept out of the index42
Agentic work benchmarks for GLM 4.7 and GPT-5.2
BenchmarkGLM 4.7GPT-5.2Leader
Design Arena: agents building web appsNot measured1086Only GPT-5.2 measured
Design Arena: agents building full-stack apps10501043GLM 4.7
Design Arena: agents building mobile apps11051093GLM 4.7
Design Arena: agents building Android apps11121058GLM 4.7
Design Arena: agents building Godot games10581142GPT-5.2
τ²-Bench Telecom95.9%84.8%GLM 4.7
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 2$2,377$3,591GPT-5.2
Berkeley Function Calling Leaderboard (BFCL)Not measured55.9%Only GPT-5.2 measured
BrowseComp-Plus64.7%Not measuredOnly GLM 4.7 measured
Neither model has a result on the other 28 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index36
Reasoning benchmarks for GLM 4.7 and GPT-5.2
BenchmarkGLM 4.7GPT-5.2Leader
GPQA Diamond80.0%91.7%GPT-5.2
MMMU Pro (Vals AI)Not measured86.7%Only GPT-5.2 measured
GPQA Diamond (Artificial Analysis run)85.9%90.3%GPT-5.2
Humanity's Last Exam (Artificial Analysis run)27.4%37.7%GPT-5.2
GPQA Diamond (Epoch AI run)83.3%91.4%GPT-5.2
ARC-AGINot measured86.2%Only GPT-5.2 measured
ARC-AGI-2Not measured52.9%Only GPT-5.2 measured
SimpleBench47.7%45.8%GLM 4.7
Chess puzzles21.0%48.0%GPT-5.2
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 prompts14431428GLM 4.7
LMArena Text: expert14241445GPT-5.2
LMArena Text: hard prompts in English14521431GLM 4.7
LMArena Vision: diagramsNot measured1265Only GPT-5.2 measured
LMArena Vision: homeworkNot measured1276Only GPT-5.2 measured
Kagi LLM BenchmarkNot measured73.3%Only GPT-5.2 measured
Neither model has a result on the other 4 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index08
Math benchmarks for GLM 4.7 and GPT-5.2
BenchmarkGLM 4.7GPT-5.2Leader
AIME93.3%96.9%GPT-5.2
MGSM (Vals AI)88.2%94.0%GPT-5.2
AA Math Index95.099.0GPT-5.2
AIME 2025 (Artificial Analysis run)95.0%99.0%GPT-5.2
OTIS Mock AIME83.3%96.1%GPT-5.2
FrontierMath, tiers 1 to 3Not measured67.4%Only GPT-5.2 measured
FrontierMath, tier 4Not measured31.7%Only GPT-5.2 measured
ProofBench6.0%15.0%GPT-5.2
LMArena Text: math14241440GPT-5.2
LMArena Text: mathematical work14111442GPT-5.2
Neither model has a result on the other 3 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index58
Knowledge benchmarks for GLM 4.7 and GPT-5.2
BenchmarkGLM 4.7GPT-5.2Leader
MMLU Pro82.7%86.2%GPT-5.2
LegalBench (Vals AI)83.4%82.8%GLM 4.7
CaseLaw (Vals AI)54.9%66.0%GPT-5.2
CorpFin (Vals AI)46.4%65.9%GPT-5.2
TaxEval (Vals AI)68.8%75.8%GPT-5.2
MortgageTax (Vals AI)Not measured67.1%Only GPT-5.2 measured
MedQA (Vals AI)93.7%94.1%GPT-5.2
MedCode (Vals AI)32.8%49.8%GPT-5.2
MMLU Pro (Artificial Analysis run)85.6%87.4%GPT-5.2
SimpleQA Verified32.2%37.1%GPT-5.2
LMArena Text: science14571425GLM 4.7
LMArena Text: medicine and healthcare14691425GLM 4.7
LMArena Text: legal and government14441427GLM 4.7
LMArena Text: business and finance14291414GLM 4.7
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 index62
Web design benchmarks for GLM 4.7 and GPT-5.2
BenchmarkGLM 4.7GPT-5.2Leader
Design Arena: websites12261195GLM 4.7
Design Arena: UI components11991190GLM 4.7
Design Arena: game dev11891195GPT-5.2
Design Arena: data viz11951202GPT-5.2
Design Arena: 3D11911079GLM 4.7
Design Arena: all code categories12171170GLM 4.7
Design Arena: SVG11431139GLM 4.7
Design Arena: ASCII art11651152GLM 4.7
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index156
General benchmarks for GLM 4.7 and GPT-5.2
BenchmarkGLM 4.7GPT-5.2Leader
SAGE (Vals AI)Not measured49.3%Only GPT-5.2 measured
MedScribe (Vals AI)68.6%84.4%GPT-5.2
IFBench67.9%75.4%GPT-5.2
Long context reasoning (AA-LCR)71.0%82.7%GPT-5.2
DTBenchNot measured90.9%Only GPT-5.2 measured
LMCANot measured43.9%Only GPT-5.2 measured
CL-bench15.9%18.2%GPT-5.2
Furniture assemblyNot measured38.3%Only GPT-5.2 measured
CL-bench Life10.9%Not measuredOnly GLM 4.7 measured
LMArena Text14351416GLM 4.7
LMArena Text: instruction following14121409GLM 4.7
LMArena Text: creative writing14011376GLM 4.7
LMArena VisionNot measured1246Only GPT-5.2 measured
LMArena Text: English14531422GLM 4.7
LMArena Text: non-English14171405GLM 4.7
LMArena Text: Chinese14951447GLM 4.7
LMArena Text: German14261444GPT-5.2
LMArena Text: Japanese14391420GLM 4.7
LMArena Text: Korean13991374GLM 4.7
LMArena Text: French14321454GPT-5.2
LMArena Text: Spanish14341413GLM 4.7
LMArena Text: Russian14231415GLM 4.7
LMArena Text: Polish14351426GLM 4.7
LMArena Text: multi-turn14461419GLM 4.7
LMArena Text: longer queries14321413GLM 4.7
LMArena Text: writing and language14111392GLM 4.7
LMArena Text: entertainment and media14001378GLM 4.7
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 11 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 4.7 and GPT-5.2
BenchmarkGLM 4.7GPT-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 4.7$0.0017Fits in one request
GPT-5.2$0.0088Fits in one request

GLM 4.7 costs less

Repository review

50K input + 3K output tokens

GLM 4.7$0.037Fits in one request
GPT-5.2$0.130Fits in one request

GLM 4.7 costs less

Agent loop

200K input + 10K output tokens

GLM 4.7$0.142Exceeds the context window
GPT-5.2$0.490Fits in one request

GLM 4.7 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 4.7 and GPT-5.2
SpecificationZ.ai GLM 4.7OpenAI GPT-5.2
MakerZ.aiOpenAI
WeightsOpen weightsNo public weights listed
ReleasedDecember 22, 2025December 10, 2025
Context window205K400K
Input $/Mtok$0.60$1.75
Output $/Mtok$2.20$14.00
Coding index55.155.9
Independent measurements behind it23
Cost per solved task$0.29$1.91
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.7 better than GPT-5.2 for coding?

GLM 4.7 and GPT-5.2 are level for coding, 55.1 against 55.9 on the Vibe Coding Index, so price and context decide. Pick GLM 4.7 for output tokens 6.4x cheaper ($2.20 vs $14.00 per million) and a lower cost per solved task ($0.29 against $1.91). Pick GPT-5.2 for a larger context window (400K against 205K tokens) and work measured by LiveCodeBench (Vals AI) or SWE-bench Verified (Vals AI), where it leads.

Which is cheaper, GLM 4.7 or GPT-5.2?

GLM 4.7 costs $0.60 per million input tokens and $2.20 per million output tokens; GPT-5.2 costs $1.75 and $14.00. Per solved SWE-bench Verified task, GLM 4.7 costs $0.29 and GPT-5.2 $1.91.

Which has the bigger context window, GLM 4.7 or GPT-5.2?

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

How many benchmarks is the GLM 4.7 vs GPT-5.2 comparison based on?

77 benchmarks have a result for both models as of October 21, 2026. GLM 4.7'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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