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GLM 5.3 vs GLM 5.3 Flash

GLM 5.3 leads GLM 5.3 Flash on coding, 70.6 to 57.7 on the Vibe Coding Index, and GLM 5.3 Flash costs 8.8x less per output token ($0.50 vs $4.40 per million). Updated October 22, 2026; 89 benchmarks have a result for both.

Replace GLM 5.3 Replace GLM 5.3 Flash
GLM 5.3Z.ai
Coding
70.6
Overall
67.9
Output $/M
$4.40
72
GLM 5.3 FlashZ.ai
Coding
57.7
Overall
66.0
Output $/M
$0.50
17
vs boards won of 89 shared

GLM 5.3 or GLM 5.3 Flash: which to pick

Pick GLM 5.3 for coding: it scores 70.6 on the Vibe Coding Index against 57.7 for GLM 5.3 Flash.

Pick GLM 5.3 Flash for output tokens 8.8x cheaper ($0.50 vs $4.40 per million) and a lower cost per solved task ($0.02 against $0.36).

Model AGLM 5.3Z.ai70.6/1006 independent benchmarks$0.36 per solved taskModel BGLM 5.3 FlashZ.ai57.7/1006 independent benchmarks$0.02 per solved task
Shared results
89
GLM 5.3 only
11
GLM 5.3 Flash only
13
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 5.3 70.6 from 6 benchmarks, GLM 5.3 Flash 57.7 from 6 benchmarks.
Widest category
Agentic work, 10.9 points apart on average over 12 shared benchmarks.
Widest single gap
LiveBench browser: GLM 5.3 0.0% against GLM 5.3 Flash 84.0%, 84.0 points apart on a 0 to 100 scale.
Output price
GLM 5.3 $4.40 against GLM 5.3 Flash $0.50 per million output tokens, 8.8x apart.
Context window
Both take 1.05M tokens.

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 5.3BarsGLM 5.3 FlashTies
Coding202
Intelligence20
Agentic work102
Reasoning91
Math42
Knowledge52
Web design63
General165
All shared boards7217
CodingLike-for-likeSix of these boards feed the Vibe Coding Index202
Coding benchmarks for GLM 5.3 and GLM 5.3 Flash
BenchmarkGLM 5.3GLM 5.3 FlashLeader
LiveBench coding82.8%80.6%GLM 5.3
LMArena WebDev16211607GLM 5.3
AA Coding Index74.871.5GLM 5.3
LiveCodeBench (Vals AI)80.5%80.5%GLM 5.3
SWE-bench Verified (Vals AI)95.4%92.0%GLM 5.3
Terminal-Bench 4 (Vals AI)38.9%37.4%GLM 5.3
Vibe Code Bench (Vals AI)78.1%30.8%GLM 5.3
Code migration (Vals AI)44.2%20.5%GLM 5.3
ProgramBench (Vals AI)1.5%0.0%GLM 5.3
IOI (Vals AI)68.4%52.5%GLM 5.3
Terminal-Bench 2.1 (Vals AI)71.5%62.9%GLM 5.3
Vibe Code Bench 1-100 (Vals AI)20.0%16.0%GLM 5.3
SkillsBench (Vals AI)47.5%40.2%GLM 5.3
CyberBench (Vals AI)72.1%67.9%GLM 5.3
SciCode59.0%51.6%GLM 5.3
FrontierCode40.1%31.8%GLM 5.3
FrontierSWE30.2%18.1%GLM 5.3
WeirdML75.4%Not measuredOnly GLM 5.3 measured
ALE-Bench1317.4303.6GLM 5.3
LMArena Text: coding14971507GLM 5.3 Flash
LMArena Text: software and IT14931500GLM 5.3 Flash
LMArena WebDev: React16411624GLM 5.3
LMArena WebDev: HTML15951577GLM 5.3
LMArena WebDev: from an imageNot measured1578Only GLM 5.3 Flash measured
Neither model has a result on the other 15 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
Intelligence benchmarks for GLM 5.3 and GLM 5.3 Flash
BenchmarkGLM 5.3GLM 5.3 FlashLeader
AA Intelligence Index44.841.8GLM 5.3
Epoch Capabilities Index156.0153.1GLM 5.3
Agentic workLike-for-likePublic boards, shown as published, kept out of the index102
Agentic work benchmarks for GLM 5.3 and GLM 5.3 Flash
BenchmarkGLM 5.3GLM 5.3 FlashLeader
AA Agentic Index53.150.9GLM 5.3
Design Arena: agents building mobile apps1194Not measuredOnly GLM 5.3 measured
Design Arena: HTML slides1189Not measuredOnly GLM 5.3 measured
Design Arena: PowerPoint slides1230Not measuredOnly GLM 5.3 measured
LiveBench research73.2%70.1%GLM 5.3
LiveBench browser0.0%84.0%GLM 5.3 Flash
LiveBench long agentic35.9%28.4%GLM 5.3
SREBench (Vals AI)9.2%0.8%GLM 5.3
Finance Agent (Vals AI)55.8%57.9%GLM 5.3 Flash
Tax Agent Bench (Vals AI)39.7%31.6%GLM 5.3
Legal Research Bench (Vals AI)49.0%45.2%GLM 5.3
Harvey's Legal Agent Benchmark (Vals AI)14.6%10.4%GLM 5.3
Excel Modeling Benchmark (Vals AI)56.3%55.9%GLM 5.3
Terminal-Bench-Science (Vals AI)5.7%2.9%GLM 5.3
APEX-Agents56.6%52.8%GLM 5.3
Vending-Bench 2$8,164Not measuredOnly GLM 5.3 measured
Neither model has a result on the other 25 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index91
Reasoning benchmarks for GLM 5.3 and GLM 5.3 Flash
BenchmarkGLM 5.3GLM 5.3 FlashLeader
GPQA Diamond88.1%86.4%GLM 5.3
LiveBench reasoning85.8%77.6%GLM 5.3
MysteryMechanism (Vals AI)23.0%Not measuredOnly GLM 5.3 measured
MMMU Pro (Vals AI)Not measured86.0%Only GLM 5.3 Flash measured
GPQA Diamond (Artificial Analysis run)91.7%91.2%GLM 5.3
Humanity's Last Exam (Artificial Analysis run)42.3%39.9%GLM 5.3
GPQA Diamond (Epoch AI run)90.9%90.2%GLM 5.3
ARC-AGINot measured91.0%Only GLM 5.3 Flash measured
ARC-AGI-2Not measured65.8%Only GLM 5.3 Flash measured
Chess puzzles37.0%21.0%GLM 5.3
Mystery game puzzles33.0%8.0%GLM 5.3
LMArena Text: hard prompts14901490GLM 5.3 Flash
LMArena Text: expert15141511GLM 5.3
LMArena Text: hard prompts in English14961488GLM 5.3
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 index42
Math benchmarks for GLM 5.3 and GLM 5.3 Flash
BenchmarkGLM 5.3GLM 5.3 FlashLeader
OTIS Mock AIME91.1%93.9%GLM 5.3 Flash
FrontierMath, tiers 1 to 368.8%55.8%GLM 5.3
FrontierMath, tier 429.3%17.1%GLM 5.3
ProofBench49.0%21.0%GLM 5.3
LMArena Text: math14951503GLM 5.3 Flash
LMArena Text: mathematical work14971497GLM 5.3
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index52
Knowledge benchmarks for GLM 5.3 and GLM 5.3 Flash
BenchmarkGLM 5.3GLM 5.3 FlashLeader
MMLU Pro86.8%86.1%GLM 5.3
LegalBench (Vals AI)84.8%83.9%GLM 5.3
TaxEval (Vals AI)72.4%75.6%GLM 5.3 Flash
MedCode (Vals AI)42.9%Not measuredOnly GLM 5.3 measured
SimpleQA Verified41.0%Not measuredOnly GLM 5.3 measured
LMArena Text: science14981484GLM 5.3
LMArena Text: medicine and healthcare14811475GLM 5.3
LMArena Text: legal and government14731473GLM 5.3
LMArena Text: business and finance14591460GLM 5.3 Flash
Neither model has a result on the other 9 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index63
Web design benchmarks for GLM 5.3 and GLM 5.3 Flash
BenchmarkGLM 5.3GLM 5.3 FlashLeader
Design Arena: websites12951272GLM 5.3
Design Arena: UI components13141316GLM 5.3 Flash
Design Arena: game dev13111278GLM 5.3
Design Arena: data viz12601262GLM 5.3 Flash
Design Arena: 3D13321316GLM 5.3
Design Arena: all code categories13041282GLM 5.3
Design Arena: SVG12941285GLM 5.3
Design Arena: ASCII art12081267GLM 5.3 Flash
LiveBench design77.3%77.2%GLM 5.3
GeneralLike-for-likePublic boards, shown as published, kept out of the index165
General benchmarks for GLM 5.3 and GLM 5.3 Flash
BenchmarkGLM 5.3GLM 5.3 FlashLeader
Vals Index59.7%50.9%GLM 5.3
LiveBench data analysis70.2%76.4%GLM 5.3 Flash
Public Benefits Bench (Vals AI)68.5%Not measuredOnly GLM 5.3 measured
MedScribe (Vals AI)88.8%88.9%GLM 5.3 Flash
Long context reasoning (AA-LCR)79.7%80.0%GLM 5.3 Flash
DTBench87.7%Not measuredOnly GLM 5.3 measured
LMCA55.5%Not measuredOnly GLM 5.3 measured
Surface Evolver BenchNot measured52.5%Only GLM 5.3 Flash measured
LMArena Text14741471GLM 5.3
LMArena Text: instruction following14791478GLM 5.3
LMArena Text: creative writing14581441GLM 5.3
LMArena VisionNot measured1294Only GLM 5.3 Flash measured
LMArena Text: English14821474GLM 5.3
LMArena Text: non-English14591462GLM 5.3 Flash
LMArena Text: Chinese15321531GLM 5.3
LMArena Text: German14881462GLM 5.3
LMArena Text: Japanese14601444GLM 5.3
LMArena Text: Korean14601436GLM 5.3
LMArena Text: French15031491GLM 5.3
LMArena Text: Spanish14621475GLM 5.3 Flash
LMArena Text: Russian14691465GLM 5.3
LMArena Text: Polish14661450GLM 5.3
LMArena Text: multi-turn14721469GLM 5.3
LMArena Text: longer queries14831481GLM 5.3
LMArena Text: writing and language14691455GLM 5.3
LMArena Text: entertainment and media14471440GLM 5.3
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 5.3 and GLM 5.3 Flash
BenchmarkGLM 5.3GLM 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 5.3$0.0036Fits 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 5.3$0.083Fits 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 5.3$0.324Fits in one request
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 5.3 and GLM 5.3 Flash
SpecificationZ.ai GLM 5.3Z.ai GLM 5.3 Flash
MakerZ.aiZ.ai
WeightsOpen weightsOpen weights
ReleasedAugust 18, 2026August 26, 2026
Context window1.05M1.05M
Input $/Mtok$1.40$0.15
Output $/Mtok$4.40$0.50
Coding index70.657.7
Independent measurements behind it66
Cost per solved task$0.36$0.02
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 5.3 better than GLM 5.3 Flash for coding?

Pick GLM 5.3 for coding: it scores 70.6 on the Vibe Coding Index against 57.7 for GLM 5.3 Flash. Pick GLM 5.3 Flash for output tokens 8.8x cheaper ($0.50 vs $4.40 per million) and a lower cost per solved task ($0.02 against $0.36).

Which is cheaper, GLM 5.3 or GLM 5.3 Flash?

GLM 5.3 costs $1.40 per million input tokens and $4.40 per million output tokens; GLM 5.3 Flash costs $0.15 and $0.50. Per solved SWE-bench Verified task, GLM 5.3 costs $0.36 and GLM 5.3 Flash $0.02.

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

Neither: both take 1.05M tokens.

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

89 benchmarks have a result for both models as of October 22, 2026. GLM 5.3's coding index rests on 6 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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