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GLM 4.7 vs GLM 5.2

GLM 5.2 leads GLM 4.7 on coding, 61.1 to 55.1 on the Vibe Coding Index, and GLM 4.7 costs 2.0x less per output token ($2.20 vs $4.40 per million). Updated October 22, 2026; 69 benchmarks have a result for both.

Replace GLM 4.7 Replace GLM 5.2
GLM 4.7Z.ai
Coding
55.1
Overall
56.9
Output $/M
$2.20
1
GLM 5.2Z.ai
Coding
61.1
Overall
62.5
Output $/M
$4.40
68
vs boards won of 69 shared

GLM 4.7 or GLM 5.2: which to pick

Pick GLM 5.2 for coding: it scores 61.1 on the Vibe Coding Index against 55.1 for GLM 4.7.

Pick GLM 4.7 for output tokens 2.0x cheaper ($2.20 vs $4.40 per million) and a lower cost per solved task ($0.29 against $0.86).

Model AGLM 4.7Z.ai55.1/1002 independent benchmarks$0.29 per solved taskModel BGLM 5.2Z.ai61.1/1004 independent benchmarks$0.86 per solved task
Shared results
69
GLM 4.7 only
12
GLM 5.2 only
29
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, GLM 5.2 61.1 from 4 benchmarks.
Widest category
Coding, 17.5 points apart on average over 10 shared benchmarks.
Widest single gap
Vending-Bench 2: GLM 4.7 $2,377 against GLM 5.2 $8,314, 37.1 points apart on a 0 to 100 scale.
Output price
GLM 4.7 $2.20 against GLM 5.2 $4.40 per million output tokens, 2.0x apart.
Context window
GLM 4.7 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 4.7BarsGLM 5.2Ties
Coding19
Intelligence02
Agentic work06
Reasoning09
Math04
Knowledge010
Web design08
General020
All shared boards168
CodingLike-for-likeSix of these boards feed the Vibe Coding Index19
Coding benchmarks for GLM 4.7 and GLM 5.2
BenchmarkGLM 4.7GLM 5.2Leader
LMArena WebDev14351604GLM 5.2
AA Coding Index45.368.8GLM 5.2
LiveCodeBench (Vals AI)82.2%69.5%GLM 4.7
SWE-bench Verified (Vals AI)69.4%82.8%GLM 5.2
Vibe Code Bench (Vals AI)Not measured64.0%Only GLM 5.2 measured
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)89.4%Not measuredOnly GLM 4.7 measured
SciCodeNot measured51.2%Only GLM 5.2 measured
Terminal-Bench Hard (Artificial Analysis run)31.8%50.8%GLM 5.2
Terminal-Bench33.4%Not measuredOnly GLM 4.7 measured
FrontierCodeNot measured24.5%Only GLM 5.2 measured
WeirdMLNot measured70.1%Only GLM 5.2 measured
ALE-Bench399.51047.0GLM 5.2
LMArena Text: coding14541487GLM 5.2
LMArena Text: software and IT14531486GLM 5.2
LMArena WebDev: React14201617GLM 5.2
LMArena WebDev: HTML14471565GLM 5.2
Neither model has a result on the other 19 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for GLM 4.7 and GLM 5.2
BenchmarkGLM 4.7GLM 5.2Leader
AA Intelligence Index22.233.7GLM 5.2
Epoch Capabilities Index143.6151.9GLM 5.2
Agentic workLike-for-likePublic boards, shown as published, kept out of the index06
Agentic work benchmarks for GLM 4.7 and GLM 5.2
BenchmarkGLM 4.7GLM 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 apps10501241GLM 5.2
Design Arena: agents building mobile apps11051159GLM 5.2
Design Arena: agents building Android apps11121178GLM 5.2
Design Arena: agents building Godot games10581142GLM 5.2
Design Arena: agents building gamesNot measured1206Only GLM 5.2 measured
Design Arena: HTML slidesNot measured1125Only GLM 5.2 measured
Design Arena: PowerPoint slidesNot measured1170Only 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 Telecom95.9%99.1%GLM 5.2
APEX-AgentsNot measured45.2%Only GLM 5.2 measured
PostTrainBenchNot measured31.7%Only GLM 5.2 measured
Vending-Bench 2$2,377$8,314GLM 5.2
BrowseComp-Plus64.7%Not measuredOnly GLM 4.7 measured
Neither model has a result on the other 23 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index09
Reasoning benchmarks for GLM 4.7 and GLM 5.2
BenchmarkGLM 4.7GLM 5.2Leader
GPQA Diamond80.0%85.6%GLM 5.2
GPQA Diamond (Artificial Analysis run)85.9%89.5%GLM 5.2
Humanity's Last Exam (Artificial Analysis run)27.4%41.1%GLM 5.2
GPQA Diamond (Epoch AI run)83.3%91.9%GLM 5.2
ARC-AGINot measured77.0%Only GLM 5.2 measured
ARC-AGI-2Not measured22.8%Only GLM 5.2 measured
SimpleBench47.7%58.8%GLM 5.2
Chess puzzles21.0%22.0%GLM 5.2
Mystery game puzzlesNot measured19.0%Only GLM 5.2 measured
EBR-benchNot measured9.5%Only GLM 5.2 measured
LMArena Text: hard prompts14431481GLM 5.2
LMArena Text: expert14241487GLM 5.2
LMArena Text: hard prompts in English14521481GLM 5.2
Kagi LLM BenchmarkNot measured62.6%Only GLM 5.2 measured
Neither model has a result on the other 8 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index04
Math benchmarks for GLM 4.7 and GLM 5.2
BenchmarkGLM 4.7GLM 5.2Leader
AIME93.3%Not measuredOnly GLM 4.7 measured
MGSM (Vals AI)88.2%Not measuredOnly GLM 4.7 measured
AA Math Index95.0Not measuredOnly GLM 4.7 measured
AIME 2025 (Artificial Analysis run)95.0%Not measuredOnly GLM 4.7 measured
OTIS Mock AIME83.3%86.4%GLM 5.2
FrontierMath, tiers 1 to 3Not measured59.2%Only GLM 5.2 measured
FrontierMath, tier 4Not measured29.3%Only GLM 5.2 measured
ProofBench6.0%35.0%GLM 5.2
LMArena Text: math14241483GLM 5.2
LMArena Text: mathematical work14111489GLM 5.2
Neither model has a result on the other 3 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index010
Knowledge benchmarks for GLM 4.7 and GLM 5.2
BenchmarkGLM 4.7GLM 5.2Leader
MMLU Pro82.7%86.7%GLM 5.2
LegalBench (Vals AI)83.4%84.1%GLM 5.2
CaseLaw (Vals AI)54.9%Not measuredOnly GLM 4.7 measured
CorpFin (Vals AI)46.4%66.1%GLM 5.2
TaxEval (Vals AI)68.8%73.3%GLM 5.2
MedQA (Vals AI)93.7%Not measuredOnly GLM 4.7 measured
MedCode (Vals AI)32.8%40.8%GLM 5.2
MMLU Pro (Artificial Analysis run)85.6%Not measuredOnly GLM 4.7 measured
SimpleQA Verified32.2%34.2%GLM 5.2
LMArena Text: science14571489GLM 5.2
LMArena Text: medicine and healthcare14691475GLM 5.2
LMArena Text: legal and government14441486GLM 5.2
LMArena Text: business and finance14291451GLM 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 index08
Web design benchmarks for GLM 4.7 and GLM 5.2
BenchmarkGLM 4.7GLM 5.2Leader
Design Arena: websites12251285GLM 5.2
Design Arena: UI components11961294GLM 5.2
Design Arena: game dev11881268GLM 5.2
Design Arena: data viz11951285GLM 5.2
Design Arena: 3D11901287GLM 5.2
Design Arena: all code categories12161287GLM 5.2
Design Arena: SVG11421213GLM 5.2
Design Arena: ASCII art11651191GLM 5.2
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index020
General benchmarks for GLM 4.7 and GLM 5.2
BenchmarkGLM 4.7GLM 5.2Leader
MedScribe (Vals AI)68.6%83.5%GLM 5.2
IFBench67.9%73.3%GLM 5.2
Long context reasoning (AA-LCR)71.0%78.3%GLM 5.2
DTBenchNot measured93.6%Only GLM 5.2 measured
LMCANot measured45.8%Only GLM 5.2 measured
CL-bench15.9%Not measuredOnly GLM 4.7 measured
Surface Evolver BenchNot measured55.6%Only GLM 5.2 measured
CL-bench Life10.9%Not measuredOnly GLM 4.7 measured
LMArena Text14351470GLM 5.2
LMArena Text: instruction following14121466GLM 5.2
LMArena Text: creative writing14011463GLM 5.2
LMArena Text: English14531475GLM 5.2
LMArena Text: non-English14171459GLM 5.2
LMArena Text: Chinese14951522GLM 5.2
LMArena Text: German14261470GLM 5.2
LMArena Text: Japanese14391450GLM 5.2
LMArena Text: Korean13991440GLM 5.2
LMArena Text: French14321479GLM 5.2
LMArena Text: Spanish14341478GLM 5.2
LMArena Text: Russian14231468GLM 5.2
LMArena Text: Polish14351456GLM 5.2
LMArena Text: multi-turn14461469GLM 5.2
LMArena Text: longer queries14321480GLM 5.2
LMArena Text: writing and language14111463GLM 5.2
LMArena Text: entertainment and media14001450GLM 5.2
Neither model has a result on the other 21 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 GLM 5.2
BenchmarkGLM 4.7GLM 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
GLM 5.2$0.0036Fits in one request

GLM 4.7 costs less

Repository review

50K input + 3K output tokens

GLM 4.7$0.037Fits in one request
GLM 5.2$0.083Fits in one request

GLM 4.7 costs less

Agent loop

200K input + 10K output tokens

GLM 4.7$0.142Exceeds the context window
GLM 5.2$0.324Fits 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 GLM 5.2
SpecificationZ.ai GLM 4.7Z.ai GLM 5.2
MakerZ.aiZ.ai
WeightsOpen weightsOpen weights
ReleasedDecember 22, 2025June 16, 2026
Context window205K1.05M
Input $/Mtok$0.60$1.40
Output $/Mtok$2.20$4.40
Coding index55.161.1
Independent measurements behind it24
Cost per solved task$0.29$0.86
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.7 better than GLM 5.2 for coding?

Pick GLM 5.2 for coding: it scores 61.1 on the Vibe Coding Index against 55.1 for GLM 4.7. Pick GLM 4.7 for output tokens 2.0x cheaper ($2.20 vs $4.40 per million) and a lower cost per solved task ($0.29 against $0.86).

Which is cheaper, GLM 4.7 or GLM 5.2?

GLM 4.7 costs $0.60 per million input tokens and $2.20 per million output tokens; GLM 5.2 costs $1.40 and $4.40. Per solved SWE-bench Verified task, GLM 4.7 costs $0.29 and GLM 5.2 $0.86.

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

GLM 5.2, with 1.05M tokens against 205K.

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

69 benchmarks have a result for both models as of October 22, 2026. GLM 4.7's coding index rests on 2 independent benchmarks and GLM 5.2's on 4. Where only one model has a result, the page says so instead of naming a winner.

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