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GLM 4.7 Flash vs GLM 5.3

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
GLM 4.7 FlashZ.ai
Coding
No data
Overall
14.9
Output $/M
$0.40
0
GLM 5.3Z.ai
Coding
70.6
Overall
44.8
Output $/M
$4.40
40
vs boards won of 40 shared

Decision reading

Only GLM 5.3 has a coding score, 70.6 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 11.0x less.

Model AGLM 4.7 FlashZ.aiNot benchmarkedNo coding benchmarkNo cost per solved taskModel BGLM 5.3Z.ai70.6/1006 independent benchmarks$0.36 per solved task
Shared results
40
GLM 4.7 Flash only
3
GLM 5.3 only
60
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 70.6 from 6 benchmarks.
Widest category
Intelligence, 29.9 points apart on average over 1 shared benchmark.
Widest single gap
Long context reasoning (AA-LCR): GLM 4.7 Flash 41.7% against GLM 5.3 79.7%, 38.0 points apart on a 0 to 100 scale.
Output price
GLM 4.7 Flash $0.40 against GLM 5.3 $4.40 per million output tokens, 11.0x apart.
Context window
GLM 4.7 Flash takes 200K tokens, GLM 5.3 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 5.3Ties
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
BenchmarkGLM 4.7 FlashGLM 5.3Leader
LiveBench codingNot measured82.8%Only GLM 5.3 measured
LMArena WebDevNot measured1621Only GLM 5.3 measured
AA Coding IndexNot measured74.8Only GLM 5.3 measured
LiveCodeBench (Vals AI)Not measured80.5%Only GLM 5.3 measured
SWE-bench Verified (Vals AI)Not measured95.4%Only GLM 5.3 measured
Terminal-Bench 4 (Vals AI)Not measured38.9%Only GLM 5.3 measured
Vibe Code Bench (Vals AI)Not measured78.1%Only GLM 5.3 measured
Code migration (Vals AI)Not measured44.2%Only GLM 5.3 measured
ProgramBench (Vals AI)Not measured1.5%Only GLM 5.3 measured
IOI (Vals AI)Not measured68.4%Only GLM 5.3 measured
Terminal-Bench 2.1 (Vals AI)Not measured71.5%Only GLM 5.3 measured
Vibe Code Bench 1-100 (Vals AI)Not measured20.0%Only GLM 5.3 measured
SkillsBench (Vals AI)Not measured47.5%Only GLM 5.3 measured
CyberBench (Vals AI)Not measured72.1%Only GLM 5.3 measured
SciCodeNot measured59.0%Only GLM 5.3 measured
Terminal-Bench Hard (Artificial Analysis run)22.0%Not measuredOnly GLM 4.7 Flash measured
FrontierCodeNot measured40.1%Only GLM 5.3 measured
FrontierSWENot measured30.2%Only GLM 5.3 measured
WeirdMLNot measured75.4%Only GLM 5.3 measured
ALE-BenchNot measured1317.4Only GLM 5.3 measured
LMArena Text: coding13821497GLM 5.3
LMArena Text: software and IT13891493GLM 5.3
LMArena WebDev: ReactNot measured1641Only GLM 5.3 measured
LMArena WebDev: HTMLNot measured1595Only GLM 5.3 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
BenchmarkGLM 4.7 FlashGLM 5.3Leader
AA Intelligence Index14.944.8GLM 5.3
Epoch Capabilities IndexNot measured156.0Only GLM 5.3 measured
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index17 boards, none shared
Agentic work benchmarks for GLM 4.7 Flash and GLM 5.3
BenchmarkGLM 4.7 FlashGLM 5.3Leader
AA Agentic IndexNot measured53.1Only GLM 5.3 measured
Design Arena: agents building mobile appsNot measured1194Only GLM 5.3 measured
Design Arena: HTML slidesNot measured1189Only GLM 5.3 measured
Design Arena: PowerPoint slidesNot measured1255Only GLM 5.3 measured
LiveBench researchNot measured73.2%Only GLM 5.3 measured
LiveBench browserNot measured0.0%Only GLM 5.3 measured
LiveBench long agenticNot measured35.9%Only GLM 5.3 measured
SREBench (Vals AI)Not measured9.2%Only GLM 5.3 measured
Finance Agent (Vals AI)Not measured55.8%Only GLM 5.3 measured
Tax Agent Bench (Vals AI)Not measured39.7%Only GLM 5.3 measured
Legal Research Bench (Vals AI)Not measured49.0%Only GLM 5.3 measured
Harvey's Legal Agent Benchmark (Vals AI)Not measured14.6%Only GLM 5.3 measured
Excel Modeling Benchmark (Vals AI)Not measured56.3%Only GLM 5.3 measured
Terminal-Bench-Science (Vals AI)Not measured5.7%Only GLM 5.3 measured
τ²-Bench Telecom98.8%Not measuredOnly GLM 4.7 Flash measured
APEX-AgentsNot measured56.6%Only GLM 5.3 measured
Vending-Bench 2Not measured$8,164Only GLM 5.3 measured
Neither model has a result on the other 22 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
BenchmarkGLM 4.7 FlashGLM 5.3Leader
GPQA DiamondNot measured88.1%Only GLM 5.3 measured
LiveBench reasoningNot measured85.8%Only GLM 5.3 measured
MysteryMechanism (Vals AI)Not measured23.0%Only GLM 5.3 measured
GPQA Diamond (Artificial Analysis run)58.1%91.7%GLM 5.3
Humanity's Last Exam (Artificial Analysis run)7.6%42.3%GLM 5.3
GPQA Diamond (Epoch AI run)60.5%90.9%GLM 5.3
Chess puzzlesNot measured37.0%Only GLM 5.3 measured
Mystery game puzzlesNot measured33.0%Only GLM 5.3 measured
LMArena Text: hard prompts13561490GLM 5.3
LMArena Text: expert13571514GLM 5.3
LMArena Text: hard prompts in English13691496GLM 5.3
Neither model has a result on the other 10 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
BenchmarkGLM 4.7 FlashGLM 5.3Leader
OTIS Mock AIME58.3%91.1%GLM 5.3
FrontierMath, tiers 1 to 3Not measured68.8%Only GLM 5.3 measured
FrontierMath, tier 4Not measured29.3%Only GLM 5.3 measured
ProofBenchNot measured49.0%Only GLM 5.3 measured
LMArena Text: math13551495GLM 5.3
LMArena Text: mathematical work13691497GLM 5.3
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
BenchmarkGLM 4.7 FlashGLM 5.3Leader
MMLU ProNot measured86.8%Only GLM 5.3 measured
LegalBench (Vals AI)Not measured84.8%Only GLM 5.3 measured
TaxEval (Vals AI)Not measured72.4%Only GLM 5.3 measured
MedCode (Vals AI)Not measured42.9%Only GLM 5.3 measured
SimpleQA VerifiedNot measured41.0%Only GLM 5.3 measured
LMArena Text: science13581498GLM 5.3
LMArena Text: medicine and healthcare13431481GLM 5.3
LMArena Text: legal and government13571473GLM 5.3
LMArena Text: business and finance13431459GLM 5.3
Neither model has a result on the other 8 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
BenchmarkGLM 4.7 FlashGLM 5.3Leader
Design Arena: websites11941295GLM 5.3
Design Arena: UI components12081314GLM 5.3
Design Arena: game dev11351311GLM 5.3
Design Arena: data viz11231259GLM 5.3
Design Arena: 3D11211332GLM 5.3
Design Arena: all code categories11781304GLM 5.3
Design Arena: SVG10381293GLM 5.3
Design Arena: ASCII artNot measured1213Only GLM 5.3 measured
LiveBench designNot measured77.3%Only GLM 5.3 measured
GeneralLike-for-likePublic boards, shown as published, kept out of the index017
General benchmarks for GLM 4.7 Flash and GLM 5.3
BenchmarkGLM 4.7 FlashGLM 5.3Leader
Vals IndexNot measured59.7%Only GLM 5.3 measured
LiveBench data analysisNot measured70.2%Only GLM 5.3 measured
Public Benefits Bench (Vals AI)Not measured68.5%Only GLM 5.3 measured
MedScribe (Vals AI)Not measured88.8%Only GLM 5.3 measured
IFBench60.8%Not measuredOnly GLM 4.7 Flash measured
Long context reasoning (AA-LCR)41.7%79.7%GLM 5.3
DTBenchNot measured87.7%Only GLM 5.3 measured
LMCANot measured55.5%Only GLM 5.3 measured
LMArena Text13511474GLM 5.3
LMArena Text: instruction following13271479GLM 5.3
LMArena Text: creative writing12961458GLM 5.3
LMArena Text: English13731482GLM 5.3
LMArena Text: non-English13301459GLM 5.3
LMArena Text: Chinese14031532GLM 5.3
LMArena Text: German13361488GLM 5.3
LMArena Text: JapaneseNot measured1460Only GLM 5.3 measured
LMArena Text: Korean12831460GLM 5.3
LMArena Text: French13321503GLM 5.3
LMArena Text: Spanish13501462GLM 5.3
LMArena Text: Russian13331469GLM 5.3
LMArena Text: Polish12911466GLM 5.3
LMArena Text: multi-turn13421472GLM 5.3
LMArena Text: longer queries13451483GLM 5.3
LMArena Text: writing and language13201469GLM 5.3
LMArena Text: entertainment and media13041447GLM 5.3
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 Flash and GLM 5.3
BenchmarkGLM 4.7 FlashGLM 5.3Leader
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$0.0036Fits 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$0.083Fits 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$0.324Fits 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
SpecificationZ.ai GLM 4.7 FlashZ.ai GLM 5.3
MakerZ.aiZ.ai
WeightsOpen weightsOpen weights
ReleasedJanuary 19, 2026August 18, 2026
Context window200K1.05M
Input $/Mtok$0.0653$1.40
Output $/Mtok$0.40$4.40
Coding indexNot benchmarked70.6
Independent measurements behind it06
Cost per solved taskNot computable$0.36
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.7 Flash better than GLM 5.3 for coding?

Only GLM 5.3 has a coding score, 70.6 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 11.0x less.

Which is cheaper, GLM 4.7 Flash or GLM 5.3?

GLM 4.7 Flash costs $0.0653 per million input tokens and $0.40 per million output tokens; GLM 5.3 costs $1.40 and $4.40. Cost per solved task is known for GLM 5.3 only.

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

GLM 5.3, with 1.05M tokens against 200K.

How many benchmarks is the GLM 4.7 Flash vs GLM 5.3 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's on 6. Where only one model has a result, the page says so instead of naming a winner.

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