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GLM 4.7 Flash vs GLM 5.3 Flash

Updated October 21, 2026. 40 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace GLM 4.7 Flash Replace GLM 5.3 Flash
GLM 4.7 FlashZ.ai
Coding
No data
Overall
14.9
Output $/M
$0.40
0
GLM 5.3 FlashZ.ai
Coding
57.7
Overall
41.8
Output $/M
$0.50
40
vs boards won of 40 shared

Decision reading

Only GLM 5.3 Flash has a coding score, 57.7 on the Vibe Coding Index; GLM 4.7 Flash has none, so the two cannot be ranked on coding.

GLM 4.7 Flash has the edge where output tokens cost 1.3x less.

Model AGLM 4.7 FlashZ.aiNot benchmarkedNo coding benchmarkNo cost per solved taskModel BGLM 5.3 FlashZ.ai57.7/1006 independent benchmarks$0.02 per solved task
Shared results
40
GLM 4.7 Flash only
3
GLM 5.3 Flash only
62
Like-for-like categories
7 of 9

Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.

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 Flash not benchmarked, GLM 5.3 Flash 57.7 from 6 benchmarks.
Widest category
Reasoning, 27.1 points apart on average over 6 shared benchmarks.
Widest single gap
Long context reasoning (AA-LCR): GLM 4.7 Flash 41.7% against GLM 5.3 Flash 80.0%, 38.3 points apart on a 0 to 100 scale.
Output price
GLM 4.7 Flash $0.40 against GLM 5.3 Flash $0.50 per million output tokens, 1.3x apart.
Context window
GLM 4.7 Flash takes 200K 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 4.7 FlashBarsGLM 5.3 FlashTies
Coding02
Intelligence01
Reasoning06
Math03
Knowledge04
Web design07
General017
All shared boards040
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
Coding benchmarks for GLM 4.7 Flash and GLM 5.3 Flash
BenchmarkGLM 4.7 FlashGLM 5.3 FlashLeader
LiveBench codingNot measured80.6%Only GLM 5.3 Flash measured
LMArena WebDevNot measured1607Only GLM 5.3 Flash measured
AA Coding IndexNot measured71.5Only GLM 5.3 Flash measured
LiveCodeBench (Vals AI)Not measured80.5%Only GLM 5.3 Flash measured
SWE-bench Verified (Vals AI)Not measured92.0%Only GLM 5.3 Flash measured
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
SciCodeNot measured51.6%Only GLM 5.3 Flash measured
Terminal-Bench Hard (Artificial Analysis run)22.0%Not measuredOnly GLM 4.7 Flash measured
FrontierCodeNot measured31.8%Only GLM 5.3 Flash measured
FrontierSWENot measured18.1%Only GLM 5.3 Flash measured
ALE-BenchNot measured303.6Only GLM 5.3 Flash measured
LMArena Text: coding13821507GLM 5.3 Flash
LMArena Text: software and IT13891500GLM 5.3 Flash
LMArena WebDev: ReactNot measured1624Only GLM 5.3 Flash measured
LMArena WebDev: HTMLNot measured1577Only GLM 5.3 Flash measured
LMArena WebDev: from an imageNot measured1578Only GLM 5.3 Flash measured
Neither model has a result on the other 14 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for GLM 4.7 Flash and GLM 5.3 Flash
BenchmarkGLM 4.7 FlashGLM 5.3 FlashLeader
AA Intelligence Index14.941.8GLM 5.3 Flash
Epoch Capabilities IndexNot measured153.1Only GLM 5.3 Flash measured
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index13 boards, none shared
Agentic work benchmarks for GLM 4.7 Flash and GLM 5.3 Flash
BenchmarkGLM 4.7 FlashGLM 5.3 FlashLeader
AA Agentic IndexNot measured50.9Only GLM 5.3 Flash 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 Telecom98.8%Not measuredOnly GLM 4.7 Flash measured
APEX-AgentsNot measured52.8%Only GLM 5.3 Flash measured
Neither model has a result on the other 26 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index06
Reasoning benchmarks for GLM 4.7 Flash and GLM 5.3 Flash
BenchmarkGLM 4.7 FlashGLM 5.3 FlashLeader
GPQA DiamondNot measured86.4%Only GLM 5.3 Flash measured
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)58.1%91.2%GLM 5.3 Flash
Humanity's Last Exam (Artificial Analysis run)7.6%39.9%GLM 5.3 Flash
GPQA Diamond (Epoch AI run)60.5%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
Chess puzzlesNot measured21.0%Only GLM 5.3 Flash measured
Mystery game puzzlesNot measured8.0%Only GLM 5.3 Flash measured
LMArena Text: hard prompts13561490GLM 5.3 Flash
LMArena Text: expert13571511GLM 5.3 Flash
LMArena Text: hard prompts in English13691488GLM 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 index03
Math benchmarks for GLM 4.7 Flash and GLM 5.3 Flash
BenchmarkGLM 4.7 FlashGLM 5.3 FlashLeader
OTIS Mock AIME58.3%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
ProofBenchNot measured21.0%Only GLM 5.3 Flash measured
LMArena Text: math13551503GLM 5.3 Flash
LMArena Text: mathematical work13691497GLM 5.3 Flash
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for GLM 4.7 Flash and GLM 5.3 Flash
BenchmarkGLM 4.7 FlashGLM 5.3 FlashLeader
MMLU ProNot measured86.1%Only GLM 5.3 Flash measured
LegalBench (Vals AI)Not measured83.9%Only GLM 5.3 Flash measured
TaxEval (Vals AI)Not measured75.6%Only GLM 5.3 Flash measured
LMArena Text: science13581484GLM 5.3 Flash
LMArena Text: medicine and healthcare13431475GLM 5.3 Flash
LMArena Text: legal and government13571473GLM 5.3 Flash
LMArena Text: business and finance13431460GLM 5.3 Flash
Neither model has a result on the other 10 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index07
Web design benchmarks for GLM 4.7 Flash and GLM 5.3 Flash
BenchmarkGLM 4.7 FlashGLM 5.3 FlashLeader
Design Arena: websites11941272GLM 5.3 Flash
Design Arena: UI components12081316GLM 5.3 Flash
Design Arena: game dev11351278GLM 5.3 Flash
Design Arena: data viz11231261GLM 5.3 Flash
Design Arena: 3D11211316GLM 5.3 Flash
Design Arena: all code categories11781282GLM 5.3 Flash
Design Arena: SVG10381284GLM 5.3 Flash
Design Arena: ASCII artNot measured1268Only GLM 5.3 Flash measured
LiveBench designNot measured77.2%Only GLM 5.3 Flash measured
GeneralLike-for-likePublic boards, shown as published, kept out of the index017
General benchmarks for GLM 4.7 Flash and GLM 5.3 Flash
BenchmarkGLM 4.7 FlashGLM 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)Not measured88.9%Only GLM 5.3 Flash measured
IFBench60.8%Not measuredOnly GLM 4.7 Flash measured
Long context reasoning (AA-LCR)41.7%80.0%GLM 5.3 Flash
Surface Evolver BenchNot measured52.5%Only GLM 5.3 Flash measured
LMArena Text13511471GLM 5.3 Flash
LMArena Text: instruction following13271478GLM 5.3 Flash
LMArena Text: creative writing12961441GLM 5.3 Flash
LMArena VisionNot measured1294Only GLM 5.3 Flash measured
LMArena Text: English13731474GLM 5.3 Flash
LMArena Text: non-English13301462GLM 5.3 Flash
LMArena Text: Chinese14031531GLM 5.3 Flash
LMArena Text: German13361462GLM 5.3 Flash
LMArena Text: JapaneseNot measured1444Only GLM 5.3 Flash measured
LMArena Text: Korean12831436GLM 5.3 Flash
LMArena Text: French13321491GLM 5.3 Flash
LMArena Text: Spanish13501475GLM 5.3 Flash
LMArena Text: Russian13331465GLM 5.3 Flash
LMArena Text: Polish12911450GLM 5.3 Flash
LMArena Text: multi-turn13421469GLM 5.3 Flash
LMArena Text: longer queries13451481GLM 5.3 Flash
LMArena Text: writing and language13201455GLM 5.3 Flash
LMArena Text: entertainment and media13041440GLM 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 17 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 4.7 Flash and GLM 5.3 Flash
BenchmarkGLM 4.7 FlashGLM 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 Flash$0.0003Fits in one request
GLM 5.3 Flash$0.0004Fits in one request

GLM 4.7 Flash costs less

Repository review

50K input + 3K output tokens

GLM 4.7 Flash$0.0045Fits in one request
GLM 5.3 Flash$0.0090Fits in one request

GLM 4.7 Flash costs less

Agent loop

200K input + 10K output tokens

GLM 4.7 Flash$0.017Exceeds the context window
GLM 5.3 Flash$0.035Fits in one request

GLM 4.7 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 Flash and GLM 5.3 Flash
SpecificationZ.ai GLM 4.7 FlashZ.ai GLM 5.3 Flash
MakerZ.aiZ.ai
WeightsOpen weightsOpen weights
ReleasedJanuary 19, 2026August 26, 2026
Context window200K1.05M
Input $/Mtok$0.0653$0.15
Output $/Mtok$0.40$0.50
Coding indexNot benchmarked57.7
Independent measurements behind it06
Cost per solved taskNot computable$0.02
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.7 Flash better than GLM 5.3 Flash for coding?

Only GLM 5.3 Flash has a coding score, 57.7 on the Vibe Coding Index; GLM 4.7 Flash has none, so the two cannot be ranked on coding. GLM 4.7 Flash has the edge where output tokens cost 1.3x less.

Which is cheaper, GLM 4.7 Flash or GLM 5.3 Flash?

GLM 4.7 Flash costs $0.0653 per million input tokens and $0.40 per million output tokens; GLM 5.3 Flash costs $0.15 and $0.50. Cost per solved task is known for GLM 5.3 Flash only.

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

GLM 5.3 Flash, with 1.05M tokens against 200K.

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

40 benchmarks have a result for both models as of October 21, 2026. GLM 4.7 Flash's coding index rests on 0 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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