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GLM 4.6V vs GLM 5.3 Flash

GLM 5.3 Flash leads GLM 4.6V on the Artificial Analysis Intelligence Index, 41.8 to 11.2, and GLM 5.3 Flash costs 1.8x less per output token ($0.50 vs $0.90 per million). Updated October 21, 2026; 31 benchmarks have a result for both.

Replace GLM 4.6V Replace GLM 5.3 Flash
GLM 4.6VZ.ai
Coding
No data
Overall
11.2
Output $/M
$0.90
0
GLM 5.3 FlashZ.ai
Coding
57.7
Overall
41.8
Output $/M
$0.50
31
vs boards won of 31 shared

GLM 4.6V or GLM 5.3 Flash: which to pick

Only GLM 5.3 Flash has a coding score, 57.7 on the Vibe Coding Index; GLM 4.6V has none, so the two cannot be ranked on coding.

Compare them on price, context and the boards below.

Model AGLM 4.6VZ.aiNot benchmarkedNo coding benchmarkNo cost per solved taskModel BGLM 5.3 FlashZ.ai57.7/1006 independent benchmarks$0.02 per solved task
Shared results
31
GLM 4.6V only
7
GLM 5.3 Flash only
71
Like-for-like categories
5 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 4.6V not benchmarked, GLM 5.3 Flash 57.7 from 6 benchmarks.
Widest category
Intelligence, 30.6 points apart on average over 1 shared benchmark.
Widest single gap
Long context reasoning (AA-LCR): GLM 4.6V 48.7% against GLM 5.3 Flash 80.0%, 31.3 points apart on a 0 to 100 scale.
Output price
GLM 4.6V $0.90 against GLM 5.3 Flash $0.50 per million output tokens, 1.8x apart.
Context window
GLM 4.6V takes 131K 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 4.6VBarsGLM 5.3 FlashTies
Coding02
Intelligence01
Reasoning07
Knowledge04
General017
All shared boards031
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
Coding benchmarks for GLM 4.6V and GLM 5.3 Flash
BenchmarkGLM 4.6VGLM 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
LiveCodeBench (Artificial Analysis run)41.1%Not measuredOnly GLM 4.6V measured
SciCodeNot measured51.6%Only GLM 5.3 Flash measured
Terminal-Bench Hard (Artificial Analysis run)14.4%Not measuredOnly GLM 4.6V 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: coding13891507GLM 5.3 Flash
LMArena Text: software and IT13911500GLM 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 13 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for GLM 4.6V and GLM 5.3 Flash
BenchmarkGLM 4.6VGLM 5.3 FlashLeader
AA Intelligence Index11.241.8GLM 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 4.6V and GLM 5.3 Flash
BenchmarkGLM 4.6VGLM 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 Telecom31.6%Not measuredOnly GLM 4.6V 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 index07
Reasoning benchmarks for GLM 4.6V and GLM 5.3 Flash
BenchmarkGLM 4.6VGLM 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)71.9%91.2%GLM 5.3 Flash
Humanity's Last Exam (Artificial Analysis run)9.6%39.9%GLM 5.3 Flash
GPQA Diamond (Epoch AI run)Not measured90.2%Only GLM 5.3 Flash measured
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 prompts13671490GLM 5.3 Flash
LMArena Text: expert13711511GLM 5.3 Flash
LMArena Text: hard prompts in English13911488GLM 5.3 Flash
LMArena Vision: diagrams11821317GLM 5.3 Flash
LMArena Vision: homework11551318GLM 5.3 Flash
Neither model has a result on the other 6 boards here
MathDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Math benchmarks for GLM 4.6V and GLM 5.3 Flash
BenchmarkGLM 4.6VGLM 5.3 FlashLeader
AA Math Index85.3Not measuredOnly GLM 4.6V measured
AIME 2025 (Artificial Analysis run)85.3%Not measuredOnly GLM 4.6V measured
OTIS Mock AIMENot measured93.9%Only GLM 5.3 Flash measured
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: mathNot measured1503Only GLM 5.3 Flash measured
LMArena Text: mathematical workNot measured1497Only GLM 5.3 Flash measured
Neither model has a result on the other 5 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for GLM 4.6V and GLM 5.3 Flash
BenchmarkGLM 4.6VGLM 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
MMLU Pro (Artificial Analysis run)79.9%Not measuredOnly GLM 4.6V measured
LMArena Text: science13891484GLM 5.3 Flash
LMArena Text: medicine and healthcare13891475GLM 5.3 Flash
LMArena Text: legal and government13931473GLM 5.3 Flash
LMArena Text: business and finance13591460GLM 5.3 Flash
Neither model has a result on the other 9 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
Web design benchmarks for GLM 4.6V and GLM 5.3 Flash
BenchmarkGLM 4.6VGLM 5.3 FlashLeader
Design Arena: websitesNot measured1272Only GLM 5.3 Flash measured
Design Arena: UI componentsNot measured1316Only GLM 5.3 Flash measured
Design Arena: game devNot measured1278Only GLM 5.3 Flash measured
Design Arena: data vizNot measured1261Only GLM 5.3 Flash measured
Design Arena: 3DNot measured1316Only GLM 5.3 Flash measured
Design Arena: all code categoriesNot measured1282Only GLM 5.3 Flash measured
Design Arena: SVGNot measured1284Only GLM 5.3 Flash measured
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 4.6V and GLM 5.3 Flash
BenchmarkGLM 4.6VGLM 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
IFBench30.1%Not measuredOnly GLM 4.6V measured
Long context reasoning (AA-LCR)48.7%80.0%GLM 5.3 Flash
Surface Evolver BenchNot measured52.5%Only GLM 5.3 Flash measured
LMArena Text13761471GLM 5.3 Flash
LMArena Text: instruction following13501478GLM 5.3 Flash
LMArena Text: creative writing13471441GLM 5.3 Flash
LMArena Vision11641294GLM 5.3 Flash
LMArena Text: English13951474GLM 5.3 Flash
LMArena Text: non-English13581462GLM 5.3 Flash
LMArena Text: Chinese14251531GLM 5.3 Flash
LMArena Text: GermanNot measured1462Only GLM 5.3 Flash measured
LMArena Text: JapaneseNot measured1444Only GLM 5.3 Flash measured
LMArena Text: KoreanNot measured1436Only GLM 5.3 Flash measured
LMArena Text: FrenchNot measured1491Only GLM 5.3 Flash measured
LMArena Text: SpanishNot measured1475Only GLM 5.3 Flash measured
LMArena Text: Russian13391465GLM 5.3 Flash
LMArena Text: PolishNot measured1450Only GLM 5.3 Flash measured
LMArena Text: multi-turn13591469GLM 5.3 Flash
LMArena Text: longer queries13571481GLM 5.3 Flash
LMArena Text: writing and language13471455GLM 5.3 Flash
LMArena Text: entertainment and media13431440GLM 5.3 Flash
LMArena Vision: English11791295GLM 5.3 Flash
LMArena Vision: Chinese12171363GLM 5.3 Flash
LMArena Vision: reading text11711299GLM 5.3 Flash
LMArena Vision: humorNot measured1279Only GLM 5.3 Flash measured
LMArena Vision: creative writing11131270GLM 5.3 Flash
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 4.6V and GLM 5.3 Flash
BenchmarkGLM 4.6VGLM 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 4.6V$0.0008Fits 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 4.6V$0.018Fits 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 4.6V$0.069Exceeds 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 4.6V and GLM 5.3 Flash
SpecificationZ.ai GLM 4.6VZ.ai GLM 5.3 Flash
MakerZ.aiZ.ai
WeightsOpen weightsOpen weights
ReleasedDecember 8, 2025August 26, 2026
Context window131K1.05M
Input $/Mtok$0.30$0.15
Output $/Mtok$0.90$0.50
Coding indexNot benchmarked57.7
Independent measurements behind it06
Cost per solved taskNot computable$0.02
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.6V 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 4.6V has none, so the two cannot be ranked on coding. Compare them on price, context and the boards below.

Which is cheaper, GLM 4.6V or GLM 5.3 Flash?

GLM 4.6V costs $0.30 per million input tokens and $0.90 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 4.6V or GLM 5.3 Flash?

GLM 5.3 Flash, with 1.05M tokens against 131K.

How many benchmarks is the GLM 4.6V vs GLM 5.3 Flash comparison based on?

31 benchmarks have a result for both models as of October 21, 2026. GLM 4.6V'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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