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GLM 4.7 vs GLM 5.3 Flash

GLM 5.3 Flash leads GLM 4.7 on coding, 57.7 to 55.1 on the Vibe Coding Index, and GLM 5.3 Flash costs 4.4x less per output token ($0.50 vs $2.20 per million). Updated October 22, 2026; 57 benchmarks have a result for both.

Replace GLM 4.7 Replace GLM 5.3 Flash
GLM 4.7Z.ai
Coding
55.1
Overall
56.9
Output $/M
$2.20
2
GLM 5.3 FlashZ.ai
Coding
57.7
Overall
66.0
Output $/M
$0.50
54
vs boards won of 57 shared, 1 tied

GLM 4.7 or GLM 5.3 Flash: which to pick

Pick GLM 5.3 Flash for coding: it scores 57.7 on the Vibe Coding Index against 55.1 for GLM 4.7.

Pick GLM 4.7 for work measured by LiveCodeBench (Vals AI) or ALE-Bench, where it leads.

Model AGLM 4.7Z.ai55.1/1002 independent benchmarks$0.29 per solved taskModel BGLM 5.3 FlashZ.ai57.7/1006 independent benchmarks$0.02 per solved task
Shared results
57
GLM 4.7 only
24
GLM 5.3 Flash only
45
Like-for-like categories
7 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 5.3 Flash 57.7 from 6 benchmarks.
Widest category
Coding, 16.3 points apart on average over 9 shared benchmarks.
Widest single gap
LMArena WebDev: React: GLM 4.7 1420 against GLM 5.3 Flash 1624, 30.0 points apart on a 0 to 100 scale.
Output price
GLM 4.7 $2.20 against GLM 5.3 Flash $0.50 per million output tokens, 4.4x apart.
Context window
GLM 4.7 takes 205K tokens, GLM 5.3 Flash 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 5.3 FlashTies
Coding27
Intelligence02
Reasoning071 tie
Math04
Knowledge07
Web design08
General019
All shared boards2541 tie
CodingLike-for-likeSix of these boards feed the Vibe Coding Index27
Coding benchmarks for GLM 4.7 and GLM 5.3 Flash
BenchmarkGLM 4.7GLM 5.3 FlashLeader
LiveBench codingNot measured80.6%Only GLM 5.3 Flash measured
LMArena WebDev14351607GLM 5.3 Flash
AA Coding Index45.371.5GLM 5.3 Flash
LiveCodeBench (Vals AI)82.2%80.5%GLM 4.7
SWE-bench Verified (Vals AI)69.4%92.0%GLM 5.3 Flash
Terminal-Bench 4 (Vals AI)Not measured37.4%Only GLM 5.3 Flash measured
Vibe Code Bench (Vals AI)Not measured30.8%Only GLM 5.3 Flash measured
Code migration (Vals AI)Not measured20.5%Only GLM 5.3 Flash measured
ProgramBench (Vals AI)Not measured0.0%Only GLM 5.3 Flash measured
IOI (Vals AI)Not measured52.5%Only GLM 5.3 Flash measured
Terminal-Bench 2.1 (Vals AI)Not measured62.9%Only GLM 5.3 Flash measured
Vibe Code Bench 1-100 (Vals AI)Not measured16.0%Only GLM 5.3 Flash measured
SkillsBench (Vals AI)Not measured40.2%Only GLM 5.3 Flash measured
CyberBench (Vals AI)Not measured67.9%Only GLM 5.3 Flash measured
LiveCodeBench (Artificial Analysis run)89.4%Not measuredOnly GLM 4.7 measured
SciCodeNot measured51.6%Only GLM 5.3 Flash measured
Terminal-Bench Hard (Artificial Analysis run)31.8%Not measuredOnly GLM 4.7 measured
Terminal-Bench33.4%Not measuredOnly GLM 4.7 measured
FrontierCodeNot measured31.8%Only GLM 5.3 Flash measured
FrontierSWENot measured18.1%Only GLM 5.3 Flash measured
ALE-Bench399.5303.6GLM 4.7
LMArena Text: coding14541507GLM 5.3 Flash
LMArena Text: software and IT14531500GLM 5.3 Flash
LMArena WebDev: React14201624GLM 5.3 Flash
LMArena WebDev: HTML14471577GLM 5.3 Flash
LMArena WebDev: from an imageNot measured1578Only GLM 5.3 Flash measured
Neither model has a result on the other 13 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for GLM 4.7 and GLM 5.3 Flash
BenchmarkGLM 4.7GLM 5.3 FlashLeader
AA Intelligence Index22.241.8GLM 5.3 Flash
Epoch Capabilities Index143.6153.1GLM 5.3 Flash
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index19 boards, none shared
Agentic work benchmarks for GLM 4.7 and GLM 5.3 Flash
BenchmarkGLM 4.7GLM 5.3 FlashLeader
AA Agentic IndexNot measured50.9Only GLM 5.3 Flash measured
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
LiveBench researchNot measured70.1%Only GLM 5.3 Flash measured
LiveBench browserNot measured84.0%Only GLM 5.3 Flash measured
LiveBench long agenticNot measured28.4%Only GLM 5.3 Flash measured
SREBench (Vals AI)Not measured0.8%Only GLM 5.3 Flash measured
Finance Agent (Vals AI)Not measured57.9%Only GLM 5.3 Flash measured
Tax Agent Bench (Vals AI)Not measured31.6%Only GLM 5.3 Flash measured
Legal Research Bench (Vals AI)Not measured45.2%Only GLM 5.3 Flash measured
Harvey's Legal Agent Benchmark (Vals AI)Not measured10.4%Only GLM 5.3 Flash measured
Excel Modeling Benchmark (Vals AI)Not measured55.9%Only GLM 5.3 Flash measured
Terminal-Bench-Science (Vals AI)Not measured2.9%Only GLM 5.3 Flash measured
τ²-Bench Telecom95.9%Not measuredOnly GLM 4.7 measured
APEX-AgentsNot measured52.8%Only GLM 5.3 Flash measured
Vending-Bench 2$2,377Not measuredOnly GLM 4.7 measured
BrowseComp-Plus64.7%Not measuredOnly GLM 4.7 measured
Neither model has a result on the other 22 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index07
Reasoning benchmarks for GLM 4.7 and GLM 5.3 Flash
BenchmarkGLM 4.7GLM 5.3 FlashLeader
GPQA Diamond80.0%86.4%GLM 5.3 Flash
LiveBench reasoningNot measured77.6%Only GLM 5.3 Flash measured
MMMU Pro (Vals AI)Not measured86.0%Only GLM 5.3 Flash measured
GPQA Diamond (Artificial Analysis run)85.9%91.2%GLM 5.3 Flash
Humanity's Last Exam (Artificial Analysis run)27.4%39.9%GLM 5.3 Flash
GPQA Diamond (Epoch AI run)83.3%90.2%GLM 5.3 Flash
ARC-AGINot measured91.0%Only GLM 5.3 Flash measured
ARC-AGI-2Not measured65.8%Only GLM 5.3 Flash measured
SimpleBench47.7%Not measuredOnly GLM 4.7 measured
Chess puzzles21.0%21.0%Tie
Mystery game puzzlesNot measured8.0%Only GLM 5.3 Flash measured
LMArena Text: hard prompts14431490GLM 5.3 Flash
LMArena Text: expert14241511GLM 5.3 Flash
LMArena Text: hard prompts in English14521488GLM 5.3 Flash
LMArena Vision: diagramsNot measured1317Only GLM 5.3 Flash measured
LMArena Vision: homeworkNot measured1318Only GLM 5.3 Flash measured
Neither model has a result on the other 6 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index04
Math benchmarks for GLM 4.7 and GLM 5.3 Flash
BenchmarkGLM 4.7GLM 5.3 FlashLeader
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%93.9%GLM 5.3 Flash
FrontierMath, tiers 1 to 3Not measured55.8%Only GLM 5.3 Flash measured
FrontierMath, tier 4Not measured17.1%Only GLM 5.3 Flash measured
ProofBench6.0%21.0%GLM 5.3 Flash
LMArena Text: math14241503GLM 5.3 Flash
LMArena Text: mathematical work14111497GLM 5.3 Flash
Neither model has a result on the other 3 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index07
Knowledge benchmarks for GLM 4.7 and GLM 5.3 Flash
BenchmarkGLM 4.7GLM 5.3 FlashLeader
MMLU Pro82.7%86.1%GLM 5.3 Flash
LegalBench (Vals AI)83.4%83.9%GLM 5.3 Flash
CaseLaw (Vals AI)54.9%Not measuredOnly GLM 4.7 measured
CorpFin (Vals AI)46.4%Not measuredOnly GLM 4.7 measured
TaxEval (Vals AI)68.8%75.6%GLM 5.3 Flash
MedQA (Vals AI)93.7%Not measuredOnly GLM 4.7 measured
MedCode (Vals AI)32.8%Not measuredOnly GLM 4.7 measured
MMLU Pro (Artificial Analysis run)85.6%Not measuredOnly GLM 4.7 measured
SimpleQA Verified32.2%Not measuredOnly GLM 4.7 measured
LMArena Text: science14571484GLM 5.3 Flash
LMArena Text: medicine and healthcare14691475GLM 5.3 Flash
LMArena Text: legal and government14441473GLM 5.3 Flash
LMArena Text: business and finance14291460GLM 5.3 Flash
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 5.3 Flash
BenchmarkGLM 4.7GLM 5.3 FlashLeader
Design Arena: websites12251272GLM 5.3 Flash
Design Arena: UI components11961316GLM 5.3 Flash
Design Arena: game dev11881278GLM 5.3 Flash
Design Arena: data viz11951262GLM 5.3 Flash
Design Arena: 3D11901316GLM 5.3 Flash
Design Arena: all code categories12161282GLM 5.3 Flash
Design Arena: SVG11421285GLM 5.3 Flash
Design Arena: ASCII art11651267GLM 5.3 Flash
LiveBench designNot measured77.2%Only GLM 5.3 Flash measured
GeneralLike-for-likePublic boards, shown as published, kept out of the index019
General benchmarks for GLM 4.7 and GLM 5.3 Flash
BenchmarkGLM 4.7GLM 5.3 FlashLeader
Vals IndexNot measured50.9%Only GLM 5.3 Flash measured
LiveBench data analysisNot measured76.4%Only GLM 5.3 Flash measured
MedScribe (Vals AI)68.6%88.9%GLM 5.3 Flash
IFBench67.9%Not measuredOnly GLM 4.7 measured
Long context reasoning (AA-LCR)71.0%80.0%GLM 5.3 Flash
CL-bench15.9%Not measuredOnly GLM 4.7 measured
Surface Evolver BenchNot measured52.5%Only GLM 5.3 Flash measured
CL-bench Life10.9%Not measuredOnly GLM 4.7 measured
LMArena Text14351471GLM 5.3 Flash
LMArena Text: instruction following14121478GLM 5.3 Flash
LMArena Text: creative writing14011441GLM 5.3 Flash
LMArena VisionNot measured1294Only GLM 5.3 Flash measured
LMArena Text: English14531474GLM 5.3 Flash
LMArena Text: non-English14171462GLM 5.3 Flash
LMArena Text: Chinese14951531GLM 5.3 Flash
LMArena Text: German14261462GLM 5.3 Flash
LMArena Text: Japanese14391444GLM 5.3 Flash
LMArena Text: Korean13991436GLM 5.3 Flash
LMArena Text: French14321491GLM 5.3 Flash
LMArena Text: Spanish14341475GLM 5.3 Flash
LMArena Text: Russian14231465GLM 5.3 Flash
LMArena Text: Polish14351450GLM 5.3 Flash
LMArena Text: multi-turn14461469GLM 5.3 Flash
LMArena Text: longer queries14321481GLM 5.3 Flash
LMArena Text: writing and language14111455GLM 5.3 Flash
LMArena Text: entertainment and media14001440GLM 5.3 Flash
LMArena Vision: EnglishNot measured1295Only GLM 5.3 Flash measured
LMArena Vision: ChineseNot measured1363Only GLM 5.3 Flash measured
LMArena Vision: reading textNot measured1299Only GLM 5.3 Flash measured
LMArena Vision: humorNot measured1279Only GLM 5.3 Flash measured
LMArena Vision: creative writingNot measured1270Only GLM 5.3 Flash measured
Neither model has a result on the other 15 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 5.3 Flash
BenchmarkGLM 4.7GLM 5.3 FlashLeader
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 5.3 Flash$0.0004Fits in one request

GLM 5.3 Flash costs less

Repository review

50K input + 3K output tokens

GLM 4.7$0.037Fits in one request
GLM 5.3 Flash$0.0090Fits in one request

GLM 5.3 Flash costs less

Agent loop

200K input + 10K output tokens

GLM 4.7$0.142Exceeds the context window
GLM 5.3 Flash$0.035Fits in one request

GLM 5.3 Flash 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 5.3 Flash
SpecificationZ.ai GLM 4.7Z.ai GLM 5.3 Flash
MakerZ.aiZ.ai
WeightsOpen weightsOpen weights
ReleasedDecember 22, 2025August 26, 2026
Context window205K1.05M
Input $/Mtok$0.60$0.15
Output $/Mtok$2.20$0.50
Coding index55.157.7
Independent measurements behind it26
Cost per solved task$0.29$0.02
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.7 better than GLM 5.3 Flash for coding?

Pick GLM 5.3 Flash for coding: it scores 57.7 on the Vibe Coding Index against 55.1 for GLM 4.7. Pick GLM 4.7 for work measured by LiveCodeBench (Vals AI) or ALE-Bench, where it leads.

Which is cheaper, GLM 4.7 or GLM 5.3 Flash?

GLM 4.7 costs $0.60 per million input tokens and $2.20 per million output tokens; GLM 5.3 Flash costs $0.15 and $0.50. Per solved SWE-bench Verified task, GLM 4.7 costs $0.29 and GLM 5.3 Flash $0.02.

Which has the bigger context window, GLM 4.7 or GLM 5.3 Flash?

GLM 5.3 Flash, with 1.05M tokens against 205K.

How many benchmarks is the GLM 4.7 vs GLM 5.3 Flash comparison based on?

57 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 5.3 Flash's on 6. Where only one model has a result, the page says so instead of naming a winner.

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