GLM 5.3 Flash vs GPT-5.2

GLM 5.3 Flash leads GPT-5.2 on coding, 57.7 to 55.9 on the Vibe Coding Index, and GLM 5.3 Flash costs 28.0x less per output token ($0.50 vs $14.00 per million). Updated October 22, 2026; 70 benchmarks have a result for both.

Replace GLM 5.3 Flash Replace GPT-5.2

GLM 5.3 FlashZ.ai

Coding: 57.7
Overall: 66.0
Output $/M: $0.50

GPT-5.2OpenAI

Coding: 55.9
Overall: 60.7
Output $/M: $14.00

vs boards won of 70 shared

GLM 5.3 Flash or GPT-5.2: which to pick

GLM 5.3 Flash and GPT-5.2 are level for coding, 57.7 against 55.9 on the Vibe Coding Index, so price and context decide.

Pick GPT-5.2 for work measured by LiveCodeBench (Vals AI) or Vibe Code Bench (Vals AI), where it leads. Pick GLM 5.3 Flash for output tokens 28.0x cheaper ($0.50 vs $14.00 per million) and a lower cost per solved task ($0.02 against $1.91).

Model AGLM 5.3 FlashZ.ai57.7/1006 independent benchmarks$0.02 per solved task Model BGPT-5.2OpenAI55.9/1003 independent benchmarks$1.91 per solved task

703244

Shared results: 70
GLM 5.3 Flash only: 32
GPT-5.2 only: 44
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.

Coding
Vibe Coding Index, six public coding benchmarks
No call
Level: 57.7 against 55.9, inside 2 points.
6 and 3 benchmarks behind the scores
Intelligence
AA Intelligence Index
GLM 5.3 Flash
GLM 5.3 Flash leads, 41.8 against 30.4.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only GLM 5.3 Flash has a AA Agentic Index result, 50.9.
Directional only
Reasoning
GPQA Diamond
GPT-5.2
GPT-5.2 leads, 91.7% against 86.4%.
Same board, both measured
Math
AIME
No call
Only GPT-5.2 has a AIME result, 96.9%.
Directional only
Knowledge
MMLU Pro
GPT-5.2
GPT-5.2 leads, 86.2% against 86.1%.
Same board, both measured
Web design
Design Arena: websites
GLM 5.3 Flash
GLM 5.3 Flash leads, 1272 against 1195.
Same board, both measured
General
Vals Index
No call
Only GLM 5.3 Flash has a Vals Index result, 50.9%.
Directional only
Long documents
Listed context window
GLM 5.3 Flash
GLM 5.3 Flash takes 1.05M tokens against 400K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 5.3 Flash
GLM 5.3 Flash $0.0004, GPT-5.2 $0.0088 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GLM 5.3 Flash
GLM 5.3 Flash $0.02, GPT-5.2 $1.91.
Published attempt cost and pass rate

At a glance

Coding index: GLM 5.3 Flash 57.7 from 6 benchmarks, GPT-5.2 55.9 from 3 benchmarks.
Widest category: Coding, 18.9 points apart on average over 8 shared benchmarks.
Widest single gap: ALE-Bench: GLM 5.3 Flash 303.6 against GPT-5.2 1293.5, 33.0 points apart on a 0 to 100 scale.
Output price: GLM 5.3 Flash $0.50 against GPT-5.2 $14.00 per million output tokens, 28.0x apart.
Context window: GLM 5.3 Flash takes 1.05M tokens, GPT-5.2 400K.

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
Category	GLM 5.3 Flash	GPT-5.2
Coding	5	3
Intelligence	1	1
Reasoning	9	5
Math	3	3
Knowledge	5	2
Web design	8	0
General	24	1
All shared boards	55	15

CodingLike-for-likeSix of these boards feed the Vibe Coding Index53

Coding benchmarks for GLM 5.3 Flash and GPT-5.2
Benchmark	GLM 5.3 Flash	GPT-5.2	Leader
SWE-bench Verified	Not measured	72.8%	Only GPT-5.2 measured
SWE-bench Multilingual	Not measured	66.7%	Only GPT-5.2 measured
LiveBench coding	80.6%	Not measured	Only GLM 5.3 Flash measured
LMArena WebDev	1607	1417	GLM 5.3 Flash
AA Coding Index	71.5	Not measured	Only GLM 5.3 Flash measured
LiveCodeBench (Vals AI)	80.5%	85.4%	GPT-5.2
SWE-bench Verified (Vals AI)	92.0%	75.8%	GLM 5.3 Flash
Terminal-Bench 4 (Vals AI)	37.4%	Not measured	Only GLM 5.3 Flash measured
Vibe Code Bench (Vals AI)	30.8%	53.5%	GPT-5.2
Code migration (Vals AI)	20.5%	Not measured	Only GLM 5.3 Flash measured
ProgramBench (Vals AI)	0.0%	Not measured	Only GLM 5.3 Flash measured
IOI (Vals AI)	52.5%	Not measured	Only GLM 5.3 Flash measured
Terminal-Bench 2.1 (Vals AI)	62.9%	Not measured	Only GLM 5.3 Flash measured
Vibe Code Bench 1-100 (Vals AI)	16.0%	Not measured	Only GLM 5.3 Flash measured
SkillsBench (Vals AI)	40.2%	Not measured	Only GLM 5.3 Flash measured
CyberBench (Vals AI)	67.9%	Not measured	Only GLM 5.3 Flash measured
LiveCodeBench (Artificial Analysis run)	Not measured	89.4%	Only GPT-5.2 measured
SciCode	51.6%	Not measured	Only GLM 5.3 Flash measured
Terminal-Bench Hard (Artificial Analysis run)	Not measured	47.0%	Only GPT-5.2 measured
Terminal-Bench	Not measured	64.9%	Only GPT-5.2 measured
FrontierCode	31.8%	Not measured	Only GLM 5.3 Flash measured
FrontierSWE	18.1%	Not measured	Only GLM 5.3 Flash measured
GSO-Bench	Not measured	27.5%	Only GPT-5.2 measured
WeirdML	Not measured	72.2%	Only GPT-5.2 measured
ALE-Bench	303.6	1293.5	GPT-5.2
LMArena Text: coding	1507	1442	GLM 5.3 Flash
LMArena Text: software and IT	1500	1434	GLM 5.3 Flash
LMArena WebDev: React	1624	Not measured	Only GLM 5.3 Flash measured
LMArena WebDev: HTML	1577	1427	GLM 5.3 Flash
LMArena WebDev: from an image	1578	Not measured	Only GLM 5.3 Flash measured
Neither model has a result on the other 9 boards here

IntelligenceLike-for-likePublic boards, shown as published, kept out of the index11

Intelligence benchmarks for GLM 5.3 Flash and GPT-5.2
Benchmark	GLM 5.3 Flash	GPT-5.2	Leader
AA Intelligence Index	41.8	30.4	GLM 5.3 Flash
Epoch Capabilities Index	153.1	153.7	GPT-5.2

Agentic workDirectional onlyPublic boards, shown as published, kept out of the index24 boards, none shared

Agentic work benchmarks for GLM 5.3 Flash and GPT-5.2
Benchmark	GLM 5.3 Flash	GPT-5.2	Leader
AA Agentic Index	50.9	Not measured	Only GLM 5.3 Flash measured
Design Arena: agents building web apps	Not measured	1086	Only GPT-5.2 measured
Design Arena: agents building full-stack apps	Not measured	1043	Only GPT-5.2 measured
Design Arena: agents building mobile apps	Not measured	1093	Only GPT-5.2 measured
Design Arena: agents building Android apps	Not measured	1059	Only GPT-5.2 measured
Design Arena: agents building Godot games	Not measured	1142	Only GPT-5.2 measured
LiveBench research	70.1%	Not measured	Only GLM 5.3 Flash measured
LiveBench browser	84.0%	Not measured	Only GLM 5.3 Flash measured
LiveBench long agentic	28.4%	Not measured	Only GLM 5.3 Flash measured
SREBench (Vals AI)	0.8%	Not measured	Only GLM 5.3 Flash measured
Finance Agent (Vals AI)	57.9%	Not measured	Only GLM 5.3 Flash measured
Tax Agent Bench (Vals AI)	31.6%	Not measured	Only GLM 5.3 Flash measured
Legal Research Bench (Vals AI)	45.2%	Not measured	Only GLM 5.3 Flash measured
Harvey's Legal Agent Benchmark (Vals AI)	10.4%	Not measured	Only GLM 5.3 Flash measured
Excel Modeling Benchmark (Vals AI)	55.9%	Not measured	Only GLM 5.3 Flash measured
Terminal-Bench-Science (Vals AI)	2.9%	Not measured	Only GLM 5.3 Flash measured
τ²-Bench Telecom	Not measured	84.8%	Only GPT-5.2 measured
APEX-Agents	52.8%	Not measured	Only GLM 5.3 Flash measured
METR time horizons	Not measured	75.3%	Only GPT-5.2 measured
Remote Labor Index	Not measured	2.5%	Only GPT-5.2 measured
GDPval	Not measured	49.7%	Only GPT-5.2 measured
DeepResearch Bench	Not measured	41.1%	Only GPT-5.2 measured
Vending-Bench 2	Not measured	$3,591	Only GPT-5.2 measured
Berkeley Function Calling Leaderboard (BFCL)	Not measured	55.9%	Only GPT-5.2 measured
Neither model has a result on the other 17 boards here

ReasoningLike-for-likePublic boards, shown as published, kept out of the index95

Reasoning benchmarks for GLM 5.3 Flash and GPT-5.2
Benchmark	GLM 5.3 Flash	GPT-5.2	Leader
GPQA Diamond	86.4%	91.7%	GPT-5.2
LiveBench reasoning	77.6%	Not measured	Only GLM 5.3 Flash measured
MMMU Pro (Vals AI)	86.0%	86.7%	GPT-5.2
GPQA Diamond (Artificial Analysis run)	91.2%	90.3%	GLM 5.3 Flash
Humanity's Last Exam (Artificial Analysis run)	39.9%	37.7%	GLM 5.3 Flash
GPQA Diamond (Epoch AI run)	90.2%	91.4%	GPT-5.2
ARC-AGI	91.0%	86.2%	GLM 5.3 Flash
ARC-AGI-2	65.8%	52.9%	GLM 5.3 Flash
SimpleBench	Not measured	45.8%	Only GPT-5.2 measured
Chess puzzles	21.0%	48.0%	GPT-5.2
Mystery game puzzles	8.0%	23.0%	GPT-5.2
EBR-bench	Not measured	23.0%	Only GPT-5.2 measured
VPCT	Not measured	84.0%	Only GPT-5.2 measured
LMArena Text: hard prompts	1490	1428	GLM 5.3 Flash
LMArena Text: expert	1511	1445	GLM 5.3 Flash
LMArena Text: hard prompts in English	1488	1431	GLM 5.3 Flash
LMArena Vision: diagrams	1317	1265	GLM 5.3 Flash
LMArena Vision: homework	1318	1276	GLM 5.3 Flash
Kagi LLM Benchmark	Not measured	73.3%	Only GPT-5.2 measured
Neither model has a result on the other 3 boards here

MathLike-for-likePublic boards, shown as published, kept out of the index33

Math benchmarks for GLM 5.3 Flash and GPT-5.2
Benchmark	GLM 5.3 Flash	GPT-5.2	Leader
AIME	Not measured	96.9%	Only GPT-5.2 measured
MGSM (Vals AI)	Not measured	94.0%	Only GPT-5.2 measured
AA Math Index	Not measured	99.0	Only GPT-5.2 measured
AIME 2025 (Artificial Analysis run)	Not measured	99.0%	Only GPT-5.2 measured
OTIS Mock AIME	93.9%	96.1%	GPT-5.2
FrontierMath, tiers 1 to 3	55.8%	67.4%	GPT-5.2
FrontierMath, tier 4	17.1%	31.7%	GPT-5.2
ProofBench	21.0%	15.0%	GLM 5.3 Flash
LMArena Text: math	1503	1440	GLM 5.3 Flash
LMArena Text: mathematical work	1497	1442	GLM 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 index52

Knowledge benchmarks for GLM 5.3 Flash and GPT-5.2
Benchmark	GLM 5.3 Flash	GPT-5.2	Leader
MMLU Pro	86.1%	86.2%	GPT-5.2
LegalBench (Vals AI)	83.9%	82.8%	GLM 5.3 Flash
CaseLaw (Vals AI)	Not measured	66.0%	Only GPT-5.2 measured
CorpFin (Vals AI)	Not measured	65.9%	Only GPT-5.2 measured
TaxEval (Vals AI)	75.6%	75.8%	GPT-5.2
MortgageTax (Vals AI)	Not measured	67.1%	Only GPT-5.2 measured
MedQA (Vals AI)	Not measured	94.1%	Only GPT-5.2 measured
MedCode (Vals AI)	Not measured	49.8%	Only GPT-5.2 measured
MMLU Pro (Artificial Analysis run)	Not measured	87.4%	Only GPT-5.2 measured
SimpleQA Verified	Not measured	37.1%	Only GPT-5.2 measured
LMArena Text: science	1484	1425	GLM 5.3 Flash
LMArena Text: medicine and healthcare	1475	1425	GLM 5.3 Flash
LMArena Text: legal and government	1473	1427	GLM 5.3 Flash
LMArena Text: business and finance	1460	1414	GLM 5.3 Flash
FACTS Benchmark Suite	Not measured	48.5%	Only GPT-5.2 measured
Neither model has a result on the other 3 boards here

Web designLike-for-likePublic boards, shown as published, kept out of the index80

Web design benchmarks for GLM 5.3 Flash and GPT-5.2
Benchmark	GLM 5.3 Flash	GPT-5.2	Leader
Design Arena: websites	1272	1195	GLM 5.3 Flash
Design Arena: UI components	1316	1190	GLM 5.3 Flash
Design Arena: game dev	1278	1195	GLM 5.3 Flash
Design Arena: data viz	1262	1202	GLM 5.3 Flash
Design Arena: 3D	1316	1079	GLM 5.3 Flash
Design Arena: all code categories	1282	1170	GLM 5.3 Flash
Design Arena: SVG	1285	1139	GLM 5.3 Flash
Design Arena: ASCII art	1267	1152	GLM 5.3 Flash
LiveBench design	77.2%	Not measured	Only GLM 5.3 Flash measured

GeneralLike-for-likePublic boards, shown as published, kept out of the index241

General benchmarks for GLM 5.3 Flash and GPT-5.2
Benchmark	GLM 5.3 Flash	GPT-5.2	Leader
Vals Index	50.9%	Not measured	Only GLM 5.3 Flash measured
LiveBench data analysis	76.4%	Not measured	Only GLM 5.3 Flash measured
SAGE (Vals AI)	Not measured	49.3%	Only GPT-5.2 measured
MedScribe (Vals AI)	88.9%	84.4%	GLM 5.3 Flash
IFBench	Not measured	75.4%	Only GPT-5.2 measured
Long context reasoning (AA-LCR)	80.0%	82.7%	GPT-5.2
DTBench	Not measured	90.9%	Only GPT-5.2 measured
LMCA	Not measured	43.9%	Only GPT-5.2 measured
CL-bench	Not measured	18.2%	Only GPT-5.2 measured
Surface Evolver Bench	52.5%	Not measured	Only GLM 5.3 Flash measured
Furniture assembly	Not measured	38.3%	Only GPT-5.2 measured
LMArena Text	1471	1416	GLM 5.3 Flash
LMArena Text: instruction following	1478	1409	GLM 5.3 Flash
LMArena Text: creative writing	1441	1376	GLM 5.3 Flash
LMArena Vision	1294	1246	GLM 5.3 Flash
LMArena Text: English	1474	1422	GLM 5.3 Flash
LMArena Text: non-English	1462	1405	GLM 5.3 Flash
LMArena Text: Chinese	1531	1447	GLM 5.3 Flash
LMArena Text: German	1462	1444	GLM 5.3 Flash
LMArena Text: Japanese	1444	1420	GLM 5.3 Flash
LMArena Text: Korean	1436	1374	GLM 5.3 Flash
LMArena Text: French	1491	1454	GLM 5.3 Flash
LMArena Text: Spanish	1475	1413	GLM 5.3 Flash
LMArena Text: Russian	1465	1415	GLM 5.3 Flash
LMArena Text: Polish	1450	1426	GLM 5.3 Flash
LMArena Text: multi-turn	1469	1419	GLM 5.3 Flash
LMArena Text: longer queries	1481	1413	GLM 5.3 Flash
LMArena Text: writing and language	1455	1392	GLM 5.3 Flash
LMArena Text: entertainment and media	1440	1378	GLM 5.3 Flash
LMArena Vision: English	1295	1243	GLM 5.3 Flash
LMArena Vision: Chinese	1363	1305	GLM 5.3 Flash
LMArena Vision: reading text	1299	1257	GLM 5.3 Flash
LMArena Vision: humor	1279	1227	GLM 5.3 Flash
LMArena Vision: captioning	Not measured	1267	Only GPT-5.2 measured
LMArena Vision: recognizing things	Not measured	1232	Only GPT-5.2 measured
LMArena Vision: creative writing	1270	1225	GLM 5.3 Flash
LMArena Document	Not measured	1405	Only GPT-5.2 measured
Neither model has a result on the other 9 boards here

Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared

Image generation benchmarks for GLM 5.3 Flash and GPT-5.2
Benchmark	GLM 5.3 Flash	GPT-5.2	Leader
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 Flash$0.0004Fits in one request

GPT-5.2$0.0088Fits in one request

GLM 5.3 Flash costs less

Repository review

50K input + 3K output tokens

GLM 5.3 Flash$0.0090Fits in one request

GPT-5.2$0.130Fits in one request

GLM 5.3 Flash costs less

Agent loop

200K input + 10K output tokens

GLM 5.3 Flash$0.035Fits in one request

GPT-5.2$0.490Fits 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 Flash and GPT-5.2
Specification	Z.ai GLM 5.3 Flash	OpenAI GPT-5.2
Maker	Z.ai	OpenAI
Weights	Open weights	No public weights listed
Released	August 26, 2026	December 10, 2025
Context window	1.05M	400K
Input $/Mtok	$0.15	$1.75
Output $/Mtok	$0.50	$14.00
Coding index	57.7	55.9
Independent measurements behind it	6	3
Cost per solved task	$0.02	$1.91
Source	OpenRouter listing	OpenRouter listing

Questions

Is GLM 5.3 Flash better than GPT-5.2 for coding?

GLM 5.3 Flash and GPT-5.2 are level for coding, 57.7 against 55.9 on the Vibe Coding Index, so price and context decide. Pick GPT-5.2 for work measured by LiveCodeBench (Vals AI) or Vibe Code Bench (Vals AI), where it leads. Pick GLM 5.3 Flash for output tokens 28.0x cheaper ($0.50 vs $14.00 per million) and a lower cost per solved task ($0.02 against $1.91).

Which is cheaper, GLM 5.3 Flash or GPT-5.2?

GLM 5.3 Flash costs $0.15 per million input tokens and $0.50 per million output tokens; GPT-5.2 costs $1.75 and $14.00. Per solved SWE-bench Verified task, GLM 5.3 Flash costs $0.02 and GPT-5.2 $1.91.

Which has the bigger context window, GLM 5.3 Flash or GPT-5.2?

GLM 5.3 Flash, with 1.05M tokens against 400K.

How many benchmarks is the GLM 5.3 Flash vs GPT-5.2 comparison based on?

70 benchmarks have a result for both models as of October 22, 2026. GLM 5.3 Flash's coding index rests on 6 independent benchmarks and GPT-5.2's on 3. Where only one model has a result, the page says so instead of naming a winner.

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GLM 5.3 Flash or GPT-5.2: which to pick

Which one for your work

Coding

Intelligence

Agentic work

Reasoning

Math

Knowledge

Web design

General

Long documents

Everyday cost

Cost per solved task

At a glance

Benchmark by benchmark

What each workload costs

Chat turn

Repository review

Agent loop

Specification differences

Questions

Compare next