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GLM 4.7 vs GPT-5

GLM 4.7 leads GPT-5 on coding, 55.1 to 38.8 on the Vibe Coding Index, and GLM 4.7 costs 4.5x less per output token ($2.20 vs $10.00 per million). Updated October 22, 2026; 73 benchmarks have a result for both.

Replace GLM 4.7 Replace GPT-5
GLM 4.7Z.ai
Coding
55.1
Overall
56.9
Output $/M
$2.20
43
GPT-5OpenAI
Coding
38.8
Overall
51.5
Output $/M
$10.00
30
vs boards won of 73 shared

GLM 4.7 or GPT-5: which to pick

Pick GLM 4.7 for coding: it scores 55.1 on the Vibe Coding Index against 38.8 for GPT-5, though on 2 benchmarks against 3.

Pick GPT-5 for a larger context window (400K against 205K tokens) and work measured by LiveCodeBench (Vals AI) or Terminal-Bench Hard (Artificial Analysis run), where it leads.

Model AGLM 4.7Z.ai55.1/1002 independent benchmarks$0.29 per solved taskModel BGPT-5OpenAI38.8/1003 independent benchmarks$0.39 per solved task
Shared results
73
GLM 4.7 only
8
GPT-5 only
40
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 38.8 from 3 benchmarks.
Widest category
Agentic work, 12.3 points apart on average over 2 shared benchmarks.
Widest single gap
SimpleQA Verified: GLM 4.7 32.2% against GPT-5 50.1%, 17.9 points apart on a 0 to 100 scale.
Output price
GLM 4.7 $2.20 against GPT-5 $10.00 per million output tokens, 4.5x apart.
Context window
GLM 4.7 takes 205K tokens, GPT-5 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-5Ties
Coding74
Intelligence02
Agentic work11
Reasoning45
Math44
Knowledge49
Web design62
General173
All shared boards4330
CodingLike-for-likeSix of these boards feed the Vibe Coding Index74
Coding benchmarks for GLM 4.7 and GPT-5
BenchmarkGLM 4.7GPT-5Leader
Aider polyglotNot measured88.0%Only GPT-5 measured
SWE-bench VerifiedNot measured74.4%Only GPT-5 measured
LMArena WebDev14351418GLM 4.7
AA Coding Index45.337.8GLM 4.7
LiveCodeBench (Vals AI)82.2%85.9%GPT-5
SWE-bench Verified (Vals AI)69.4%69.0%GLM 4.7
Vibe Code Bench (Vals AI)Not measured20.1%Only GPT-5 measured
LiveCodeBench (Artificial Analysis run)89.4%84.6%GLM 4.7
Terminal-Bench Hard (Artificial Analysis run)31.8%37.9%GPT-5
Terminal-Bench33.4%49.6%GPT-5
GSO-BenchNot measured6.9%Only GPT-5 measured
WeirdMLNot measured60.7%Only GPT-5 measured
ALE-Bench399.5807.6GPT-5
LMArena Text: coding14541436GLM 4.7
LMArena Text: software and IT14531420GLM 4.7
LMArena WebDev: React1420Not measuredOnly GLM 4.7 measured
LMArena WebDev: HTML14471427GLM 4.7
SciCodeNot measured0.0%Only GPT-5 measured
Neither model has a result on the other 21 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for GLM 4.7 and GPT-5
BenchmarkGLM 4.7GPT-5Leader
AA Intelligence Index22.223.0GPT-5
Epoch Capabilities Index143.6150.0GPT-5
Agentic workLike-for-likePublic boards, shown as published, kept out of the index11
Agentic work benchmarks for GLM 4.7 and GPT-5
BenchmarkGLM 4.7GPT-5Leader
Design Arena: agents building full-stack apps1050Not measuredOnly GLM 4.7 measured
Design Arena: agents building mobile apps1105Not measuredOnly GLM 4.7 measured
Design Arena: agents building Android apps1112Not measuredOnly GLM 4.7 measured
Design Arena: agents building Godot games1058Not measuredOnly GLM 4.7 measured
τ²-Bench Telecom95.9%86.5%GLM 4.7
METR time horizonsNot measured69.6%Only GPT-5 measured
Remote Labor IndexNot measured1.7%Only GPT-5 measured
GDPvalNot measured34.8%Only GPT-5 measured
BALROGNot measured32.8%Only GPT-5 measured
DeepResearch BenchNot measured49.6%Only GPT-5 measured
Vending-Bench 2$2,377Not measuredOnly GLM 4.7 measured
BrowseComp-Plus64.7%80.0%GPT-5
Neither model has a result on the other 29 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index45
Reasoning benchmarks for GLM 4.7 and GPT-5
BenchmarkGLM 4.7GPT-5Leader
GPQA Diamond80.0%85.6%GPT-5
MMMU Pro (Vals AI)Not measured81.5%Only GPT-5 measured
GPQA Diamond (Artificial Analysis run)85.9%85.4%GLM 4.7
Humanity's Last Exam (Artificial Analysis run)27.4%28.5%GPT-5
GPQA Diamond (Epoch AI run)83.3%86.2%GPT-5
ARC-AGINot measured65.7%Only GPT-5 measured
ARC-AGI-2Not measured9.9%Only GPT-5 measured
SimpleBench47.7%56.7%GPT-5
Chess puzzles21.0%36.0%GPT-5
Mystery game puzzlesNot measured23.0%Only GPT-5 measured
EBR-benchNot measured12.7%Only GPT-5 measured
VPCTNot measured66.0%Only GPT-5 measured
LMArena Text: hard prompts14431415GLM 4.7
LMArena Text: expert14241419GLM 4.7
LMArena Text: hard prompts in English14521419GLM 4.7
LMArena Vision: diagramsNot measured1252Only GPT-5 measured
LMArena Vision: homeworkNot measured1278Only GPT-5 measured
Kagi LLM BenchmarkNot measured72.7%Only GPT-5 measured
Neither model has a result on the other 4 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index44
Math benchmarks for GLM 4.7 and GPT-5
BenchmarkGLM 4.7GPT-5Leader
AIME93.3%93.4%GPT-5
MATH 500 (Vals AI)Not measured96.0%Only GPT-5 measured
MGSM (Vals AI)88.2%92.8%GPT-5
AA Math Index95.094.3GLM 4.7
AIME 2025 (Artificial Analysis run)95.0%94.3%GLM 4.7
OTIS Mock AIME83.3%91.4%GPT-5
FrontierMath, tiers 1 to 3Not measured55.4%Only GPT-5 measured
FrontierMath, tier 4Not measured22.0%Only GPT-5 measured
ProofBench6.0%18.0%GPT-5
MATH level 5Not measured98.1%Only GPT-5 measured
LMArena Text: math14241407GLM 4.7
LMArena Text: mathematical work14111405GLM 4.7
Neither model has a result on the other 1 board here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index49
Knowledge benchmarks for GLM 4.7 and GPT-5
BenchmarkGLM 4.7GPT-5Leader
MMLU Pro82.7%86.5%GPT-5
LegalBench (Vals AI)83.4%86.0%GPT-5
CaseLaw (Vals AI)54.9%66.5%GPT-5
CorpFin (Vals AI)46.4%61.1%GPT-5
TaxEval (Vals AI)68.8%73.4%GPT-5
MortgageTax (Vals AI)Not measured65.5%Only GPT-5 measured
MedQA (Vals AI)93.7%96.3%GPT-5
MedCode (Vals AI)32.8%49.6%GPT-5
MMLU Pro (Artificial Analysis run)85.6%87.1%GPT-5
SimpleQA Verified32.2%50.1%GPT-5
GeoBenchNot measured81.0%Only GPT-5 measured
LMArena Text: science14571416GLM 4.7
LMArena Text: medicine and healthcare14691413GLM 4.7
LMArena Text: legal and government14441431GLM 4.7
LMArena Text: business and finance14291400GLM 4.7
FACTS Benchmark SuiteNot measured56.5%Only GPT-5 measured
Neither model has a result on the other 2 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
BenchmarkGLM 4.7GPT-5Leader
Design Arena: websites12251184GLM 4.7
Design Arena: UI components11961177GLM 4.7
Design Arena: game dev11881185GLM 4.7
Design Arena: data viz11951221GPT-5
Design Arena: 3D11901055GLM 4.7
Design Arena: all code categories12161167GLM 4.7
Design Arena: SVG11421188GPT-5
Design Arena: ASCII art11651141GLM 4.7
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index173
General benchmarks for GLM 4.7 and GPT-5
BenchmarkGLM 4.7GPT-5Leader
SAGE (Vals AI)Not measured43.7%Only GPT-5 measured
MedScribe (Vals AI)68.6%83.7%GPT-5
IFBench67.9%73.1%GPT-5
Long context reasoning (AA-LCR)71.0%78.2%GPT-5
DTBenchNot measured90.7%Only GPT-5 measured
LMCANot measured40.0%Only GPT-5 measured
Fiction.LiveBenchNot measured97.2%Only GPT-5 measured
Creative writing (Lech Mazur)Not measured86.0%Only GPT-5 measured
CL-bench15.9%Not measuredOnly GLM 4.7 measured
CL-bench Life10.9%Not measuredOnly GLM 4.7 measured
LMArena Text14351406GLM 4.7
LMArena Text: instruction following14121388GLM 4.7
LMArena Text: creative writing14011365GLM 4.7
LMArena VisionNot measured1232Only GPT-5 measured
LMArena Text: English14531409GLM 4.7
LMArena Text: non-English14171397GLM 4.7
LMArena Text: Chinese14951422GLM 4.7
LMArena Text: German14261416GLM 4.7
LMArena Text: Japanese14391409GLM 4.7
LMArena Text: Korean13991360GLM 4.7
LMArena Text: French14321409GLM 4.7
LMArena Text: Spanish14341399GLM 4.7
LMArena Text: Russian14231407GLM 4.7
LMArena Text: Polish14351431GLM 4.7
LMArena Text: multi-turn14461426GLM 4.7
LMArena Text: longer queries14321399GLM 4.7
LMArena Text: writing and language14111384GLM 4.7
LMArena Text: entertainment and media14001379GLM 4.7
LMArena Vision: EnglishNot measured1231Only GPT-5 measured
LMArena Vision: ChineseNot measured1267Only GPT-5 measured
LMArena Vision: reading textNot measured1246Only GPT-5 measured
LMArena Vision: humorNot measured1219Only GPT-5 measured
LMArena Vision: captioningNot measured1212Only GPT-5 measured
LMArena Vision: recognizing thingsNot measured1263Only GPT-5 measured
LMArena Vision: creative writingNot measured1210Only GPT-5 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
BenchmarkGLM 4.7GPT-5Leader
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$0.0063Fits 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$0.092Fits 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$0.350Fits 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
SpecificationZ.ai GLM 4.7OpenAI GPT-5
MakerZ.aiOpenAI
WeightsOpen weightsNo public weights listed
ReleasedDecember 22, 2025August 7, 2025
Context window205K400K
Input $/Mtok$0.60$1.25
Output $/Mtok$2.20$10.00
Coding index55.138.8
Independent measurements behind it23
Cost per solved task$0.29$0.39
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.7 better than GPT-5 for coding?

Pick GLM 4.7 for coding: it scores 55.1 on the Vibe Coding Index against 38.8 for GPT-5, though on 2 benchmarks against 3. Pick GPT-5 for a larger context window (400K against 205K tokens) and work measured by LiveCodeBench (Vals AI) or Terminal-Bench Hard (Artificial Analysis run), where it leads.

Which is cheaper, GLM 4.7 or GPT-5?

GLM 4.7 costs $0.60 per million input tokens and $2.20 per million output tokens; GPT-5 costs $1.25 and $10.00. Per solved SWE-bench Verified task, GLM 4.7 costs $0.29 and GPT-5 $0.39.

Which has the bigger context window, GLM 4.7 or GPT-5?

GPT-5, with 400K tokens against 205K.

How many benchmarks is the GLM 4.7 vs GPT-5 comparison based on?

73 benchmarks have a result for both models as of October 22, 2026. GLM 4.7's coding index rests on 2 independent benchmarks and GPT-5's on 3. Where only one model has a result, the page says so instead of naming a winner.

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