GLM 5.3 vs GPT-5.1

GLM 5.3 leads GPT-5.1 on coding, 70.6 to 40.4 on the Vibe Coding Index, and GLM 5.3 costs 2.3x less per output token ($4.40 vs $10.00 per million). Updated October 21, 2026; 63 benchmarks have a result for both.

Replace GLM 5.3 Replace GPT-5.1

GLM 5.3Z.ai

Coding: 70.6
Overall: 67.9
Output $/M: $4.40

GPT-5.1OpenAI

Coding: 40.4
Overall: 55.0
Output $/M: $10.00

vs boards won of 63 shared

GLM 5.3 or GPT-5.1: which to pick

Pick GLM 5.3 for coding: it scores 70.6 on the Vibe Coding Index against 40.4 for GPT-5.1.

Pick GPT-5.1 for work measured by LiveCodeBench (Vals AI) or Chess puzzles, where it leads.

Model AGLM 5.3Z.ai70.6/1006 independent benchmarks$0.36 per solved task Model BGPT-5.1OpenAI40.4/1003 independent benchmarks$1.02 per solved task

633738

Shared results: 63
GLM 5.3 only: 37
GPT-5.1 only: 38
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.

Coding
Vibe Coding Index, six public coding benchmarks
GLM 5.3
GLM 5.3 scores 70.6 against 40.4.
6 and 3 benchmarks behind the scores
Intelligence
AA Intelligence Index
GLM 5.3
GLM 5.3 leads, 44.8 against 24.7.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only GLM 5.3 has a AA Agentic Index result, 53.1.
Directional only
Reasoning
GPQA Diamond
GLM 5.3
GLM 5.3 leads, 88.1% against 86.6%.
Same board, both measured
Math
AIME
No call
Only GPT-5.1 has a AIME result, 93.3%.
Directional only
Knowledge
MMLU Pro
GLM 5.3
GLM 5.3 leads, 86.8% against 86.4%.
Same board, both measured
Web design
Design Arena: websites
GLM 5.3
GLM 5.3 leads, 1295 against 1187.
Same board, both measured
General
Vals Index
No call
Only GLM 5.3 has a Vals Index result, 59.7%.
Directional only
Long documents
Listed context window
GLM 5.3
GLM 5.3 takes 1.05M tokens against 400K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 5.3
GLM 5.3 $0.0036, GPT-5.1 $0.0063 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GLM 5.3
GLM 5.3 $0.36, GPT-5.1 $1.02.
Published attempt cost and pass rate

At a glance

Coding index: GLM 5.3 70.6 from 6 benchmarks, GPT-5.1 40.4 from 3 benchmarks.
Widest category: Agentic work, 41.8 points apart on average over 1 shared benchmark.
Widest single gap: Vibe Code Bench (Vals AI): GLM 5.3 78.1% against GPT-5.1 24.6%, 53.5 points apart on a 0 to 100 scale.
Output price: GLM 5.3 $4.40 against GPT-5.1 $10.00 per million output tokens, 2.3x apart.
Context window: GLM 5.3 takes 1.05M tokens, GPT-5.1 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	GPT-5.1
Coding	9	1
Intelligence	2	0
Agentic work	1	0
Reasoning	8	1
Math	3	0
Knowledge	5	4
Web design	8	0
General	19	2
All shared boards	55	8

CodingLike-for-likeSix of these boards feed the Vibe Coding Index91

Coding benchmarks for GLM 5.3 and GPT-5.1
Benchmark	GLM 5.3	GPT-5.1	Leader
SWE-bench Verified	Not measured	66.0%	Only GPT-5.1 measured
LiveBench coding	82.8%	Not measured	Only GLM 5.3 measured
LMArena WebDev	1621	1396	GLM 5.3
AA Coding Index	74.8	49.4	GLM 5.3
LiveCodeBench (Vals AI)	80.5%	86.5%	GPT-5.1
SWE-bench Verified (Vals AI)	95.4%	69.8%	GLM 5.3
Terminal-Bench 4 (Vals AI)	38.9%	Not measured	Only GLM 5.3 measured
Vibe Code Bench (Vals AI)	78.1%	24.6%	GLM 5.3
Code migration (Vals AI)	44.2%	Not measured	Only GLM 5.3 measured
ProgramBench (Vals AI)	1.5%	Not measured	Only GLM 5.3 measured
IOI (Vals AI)	68.4%	Not measured	Only GLM 5.3 measured
Terminal-Bench 2.1 (Vals AI)	71.5%	Not measured	Only GLM 5.3 measured
Vibe Code Bench 1-100 (Vals AI)	20.0%	Not measured	Only GLM 5.3 measured
SkillsBench (Vals AI)	47.5%	Not measured	Only GLM 5.3 measured
CyberBench (Vals AI)	72.1%	Not measured	Only GLM 5.3 measured
LiveCodeBench (Artificial Analysis run)	Not measured	86.8%	Only GPT-5.1 measured
SciCode	59.0%	Not measured	Only GLM 5.3 measured
Terminal-Bench Hard (Artificial Analysis run)	Not measured	45.5%	Only GPT-5.1 measured
Terminal-Bench	Not measured	47.6%	Only GPT-5.1 measured
FrontierCode	40.1%	Not measured	Only GLM 5.3 measured
FrontierSWE	30.2%	Not measured	Only GLM 5.3 measured
GSO-Bench	Not measured	13.7%	Only GPT-5.1 measured
WeirdML	75.4%	60.8%	GLM 5.3
ALE-Bench	1317.4	Not measured	Only GLM 5.3 measured
LMArena Text: coding	1497	1453	GLM 5.3
LMArena Text: software and IT	1493	1454	GLM 5.3
LMArena WebDev: React	1641	1307	GLM 5.3
LMArena WebDev: HTML	1595	1405	GLM 5.3
LMArena WebDev: from an image	Not measured	1421	Only GPT-5.1 measured
Neither model has a result on the other 10 boards here

IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20

Intelligence benchmarks for GLM 5.3 and GPT-5.1
Benchmark	GLM 5.3	GPT-5.1	Leader
AA Intelligence Index	44.8	24.7	GLM 5.3
Epoch Capabilities Index	156.0	149.8	GLM 5.3

Agentic workLike-for-likePublic boards, shown as published, kept out of the index10

Agentic work benchmarks for GLM 5.3 and GPT-5.1
Benchmark	GLM 5.3	GPT-5.1	Leader
AA Agentic Index	53.1	Not measured	Only GLM 5.3 measured
Design Arena: agents building mobile apps	1194	Not measured	Only GLM 5.3 measured
Design Arena: HTML slides	1189	Not measured	Only GLM 5.3 measured
Design Arena: PowerPoint slides	1254	Not measured	Only GLM 5.3 measured
LiveBench research	73.2%	Not measured	Only GLM 5.3 measured
LiveBench browser	0.0%	Not measured	Only GLM 5.3 measured
LiveBench long agentic	35.9%	Not measured	Only GLM 5.3 measured
SREBench (Vals AI)	9.2%	Not measured	Only GLM 5.3 measured
Finance Agent (Vals AI)	55.8%	Not measured	Only GLM 5.3 measured
Tax Agent Bench (Vals AI)	39.7%	Not measured	Only GLM 5.3 measured
Legal Research Bench (Vals AI)	49.0%	Not measured	Only GLM 5.3 measured
Harvey's Legal Agent Benchmark (Vals AI)	14.6%	Not measured	Only GLM 5.3 measured
Excel Modeling Benchmark (Vals AI)	56.3%	Not measured	Only GLM 5.3 measured
Terminal-Bench-Science (Vals AI)	5.7%	Not measured	Only GLM 5.3 measured
τ²-Bench Telecom	Not measured	81.9%	Only GPT-5.1 measured
APEX-Agents	56.6%	Not measured	Only GLM 5.3 measured
DeepResearch Bench	Not measured	42.8%	Only GPT-5.1 measured
Vending-Bench 2	$8,164	$1,473	GLM 5.3
Neither model has a result on the other 23 boards here

ReasoningLike-for-likePublic boards, shown as published, kept out of the index81

Reasoning benchmarks for GLM 5.3 and GPT-5.1
Benchmark	GLM 5.3	GPT-5.1	Leader
GPQA Diamond	88.1%	86.6%	GLM 5.3
LiveBench reasoning	85.8%	Not measured	Only GLM 5.3 measured
MysteryMechanism (Vals AI)	23.0%	Not measured	Only GLM 5.3 measured
MMMU Pro (Vals AI)	Not measured	83.2%	Only GPT-5.1 measured
GPQA Diamond (Artificial Analysis run)	91.7%	87.3%	GLM 5.3
Humanity's Last Exam (Artificial Analysis run)	42.3%	28.5%	GLM 5.3
GPQA Diamond (Epoch AI run)	90.9%	87.6%	GLM 5.3
ARC-AGI	Not measured	72.8%	Only GPT-5.1 measured
ARC-AGI-2	Not measured	17.6%	Only GPT-5.1 measured
SimpleBench	Not measured	53.2%	Only GPT-5.1 measured
Chess puzzles	37.0%	42.0%	GPT-5.1
Mystery game puzzles	33.0%	19.0%	GLM 5.3
VPCT	Not measured	58.7%	Only GPT-5.1 measured
LMArena Text: hard prompts	1490	1457	GLM 5.3
LMArena Text: expert	1514	1470	GLM 5.3
LMArena Text: hard prompts in English	1496	1462	GLM 5.3
LMArena Vision: diagrams	Not measured	1264	Only GPT-5.1 measured
LMArena Vision: homework	Not measured	1272	Only GPT-5.1 measured
Neither model has a result on the other 4 boards here

MathLike-for-likePublic boards, shown as published, kept out of the index30

Math benchmarks for GLM 5.3 and GPT-5.1
Benchmark	GLM 5.3	GPT-5.1	Leader
AIME	Not measured	93.3%	Only GPT-5.1 measured
MGSM (Vals AI)	Not measured	93.0%	Only GPT-5.1 measured
AA Math Index	Not measured	94.0	Only GPT-5.1 measured
AIME 2025 (Artificial Analysis run)	Not measured	94.0%	Only GPT-5.1 measured
OTIS Mock AIME	91.1%	88.6%	GLM 5.3
FrontierMath, tiers 1 to 3	68.8%	Not measured	Only GLM 5.3 measured
FrontierMath, tier 4	29.3%	Not measured	Only GLM 5.3 measured
ProofBench	49.0%	Not measured	Only GLM 5.3 measured
LMArena Text: math	1495	1446	GLM 5.3
LMArena Text: mathematical work	1497	1449	GLM 5.3
Neither model has a result on the other 3 boards here

KnowledgeLike-for-likePublic boards, shown as published, kept out of the index54

Knowledge benchmarks for GLM 5.3 and GPT-5.1
Benchmark	GLM 5.3	GPT-5.1	Leader
MMLU Pro	86.8%	86.4%	GLM 5.3
LegalBench (Vals AI)	84.8%	85.7%	GPT-5.1
CaseLaw (Vals AI)	Not measured	73.4%	Only GPT-5.1 measured
CorpFin (Vals AI)	Not measured	63.8%	Only GPT-5.1 measured
TaxEval (Vals AI)	72.4%	74.9%	GPT-5.1
MortgageTax (Vals AI)	Not measured	61.4%	Only GPT-5.1 measured
MedQA (Vals AI)	Not measured	96.4%	Only GPT-5.1 measured
MedCode (Vals AI)	42.9%	52.7%	GPT-5.1
MMLU Pro (Artificial Analysis run)	Not measured	87.0%	Only GPT-5.1 measured
SimpleQA Verified	41.0%	48.0%	GPT-5.1
LMArena Text: science	1498	1459	GLM 5.3
LMArena Text: medicine and healthcare	1481	1456	GLM 5.3
LMArena Text: legal and government	1473	1459	GLM 5.3
LMArena Text: business and finance	1459	1431	GLM 5.3
FACTS Benchmark Suite	Not measured	45.0%	Only GPT-5.1 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 and GPT-5.1
Benchmark	GLM 5.3	GPT-5.1	Leader
Design Arena: websites	1295	1187	GLM 5.3
Design Arena: UI components	1314	1166	GLM 5.3
Design Arena: game dev	1311	1179	GLM 5.3
Design Arena: data viz	1259	1204	GLM 5.3
Design Arena: 3D	1332	1061	GLM 5.3
Design Arena: all code categories	1304	1171	GLM 5.3
Design Arena: SVG	1293	1149	GLM 5.3
Design Arena: ASCII art	1213	1119	GLM 5.3
LiveBench design	77.3%	Not measured	Only GLM 5.3 measured

GeneralLike-for-likePublic boards, shown as published, kept out of the index192

General benchmarks for GLM 5.3 and GPT-5.1
Benchmark	GLM 5.3	GPT-5.1	Leader
Vals Index	59.7%	Not measured	Only GLM 5.3 measured
LiveBench data analysis	70.2%	Not measured	Only GLM 5.3 measured
Public Benefits Bench (Vals AI)	68.5%	Not measured	Only GLM 5.3 measured
SAGE (Vals AI)	Not measured	43.2%	Only GPT-5.1 measured
MedScribe (Vals AI)	88.8%	88.1%	GLM 5.3
IFBench	Not measured	72.9%	Only GPT-5.1 measured
Long context reasoning (AA-LCR)	79.7%	80.0%	GPT-5.1
DTBench	87.7%	90.1%	GPT-5.1
LMCA	55.5%	43.9%	GLM 5.3
CL-bench	Not measured	23.7%	Only GPT-5.1 measured
CL-bench Life	Not measured	17.3%	Only GPT-5.1 measured
LMArena Text	1474	1443	GLM 5.3
LMArena Text: instruction following	1479	1443	GLM 5.3
LMArena Text: creative writing	1458	1427	GLM 5.3
LMArena Vision	Not measured	1250	Only GPT-5.1 measured
LMArena Text: English	1482	1451	GLM 5.3
LMArena Text: non-English	1459	1431	GLM 5.3
LMArena Text: Chinese	1532	1495	GLM 5.3
LMArena Text: German	1488	1438	GLM 5.3
LMArena Text: Japanese	1460	1453	GLM 5.3
LMArena Text: Korean	1460	1401	GLM 5.3
LMArena Text: French	1503	1450	GLM 5.3
LMArena Text: Spanish	1462	1433	GLM 5.3
LMArena Text: Russian	1469	1435	GLM 5.3
LMArena Text: Polish	1466	1440	GLM 5.3
LMArena Text: multi-turn	1472	1450	GLM 5.3
LMArena Text: longer queries	1483	1448	GLM 5.3
LMArena Text: writing and language	1469	1428	GLM 5.3
LMArena Text: entertainment and media	1447	1415	GLM 5.3
LMArena Vision: English	Not measured	1252	Only GPT-5.1 measured
LMArena Vision: Chinese	Not measured	1270	Only GPT-5.1 measured
LMArena Vision: reading text	Not measured	1255	Only GPT-5.1 measured
LMArena Vision: humor	Not measured	1263	Only GPT-5.1 measured
LMArena Vision: captioning	Not measured	1252	Only GPT-5.1 measured
LMArena Vision: recognizing things	Not measured	1232	Only GPT-5.1 measured
LMArena Vision: creative writing	Not measured	1266	Only GPT-5.1 measured
LMArena Document	Not measured	1403	Only GPT-5.1 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 and GPT-5.1
Benchmark	GLM 5.3	GPT-5.1	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$0.0036Fits in one request

GPT-5.1$0.0063Fits in one request

GLM 5.3 costs less

Repository review

50K input + 3K output tokens

GLM 5.3$0.083Fits in one request

GPT-5.1$0.092Fits in one request

GLM 5.3 costs less

Agent loop

200K input + 10K output tokens

GLM 5.3$0.324Fits in one request

GPT-5.1$0.350Fits in one request

GLM 5.3 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 GPT-5.1
Specification	Z.ai GLM 5.3	OpenAI GPT-5.1
Maker	Z.ai	OpenAI
Weights	Open weights	No public weights listed
Released	August 18, 2026	November 13, 2025
Context window	1.05M	400K
Input $/Mtok	$1.40	$1.25
Output $/Mtok	$4.40	$10.00
Coding index	70.6	40.4
Independent measurements behind it	6	3
Cost per solved task	$0.36	$1.02
Source	OpenRouter listing	OpenRouter listing

Questions

Is GLM 5.3 better than GPT-5.1 for coding?

Pick GLM 5.3 for coding: it scores 70.6 on the Vibe Coding Index against 40.4 for GPT-5.1. Pick GPT-5.1 for work measured by LiveCodeBench (Vals AI) or Chess puzzles, where it leads.

Which is cheaper, GLM 5.3 or GPT-5.1?

GLM 5.3 costs $1.40 per million input tokens and $4.40 per million output tokens; GPT-5.1 costs $1.25 and $10.00. Per solved SWE-bench Verified task, GLM 5.3 costs $0.36 and GPT-5.1 $1.02.

Which has the bigger context window, GLM 5.3 or GPT-5.1?

GLM 5.3, with 1.05M tokens against 400K.

How many benchmarks is the GLM 5.3 vs GPT-5.1 comparison based on?

63 benchmarks have a result for both models as of October 21, 2026. GLM 5.3's coding index rests on 6 independent benchmarks and GPT-5.1'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 or GPT-5.1: 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