GLM 5.3 vs GPT-4.1 Mini

Updated October 21, 2026. 54 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace GLM 5.3 Replace GPT-4.1 Mini

GLM 5.3Z.ai

Coding: 73.1
Overall: 44.8
Output $/M: $1.40

GPT-4.1 MiniOpenAI

Coding: 28.5
Overall: 10.2
Output $/M: $1.60

vs boards won of 54 shared

Decision reading

Pick GLM 5.3 for coding: it scores 73.1 on the Vibe Coding Index against 28.5 for GPT-4.1 Mini.

GPT-4.1 Mini is neither cheaper nor larger in context, and leads no benchmark both models were measured on.

Model AGLM 5.3Z.ai73.1/1002 independent benchmarks$0.36 per solved task Model BGPT-4.1 MiniOpenAI28.5/1002 independent benchmarks$1.818 per solved task

544630

Shared results: 54
GLM 5.3 only: 46
GPT-4.1 Mini only: 30
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.

Coding
Vibe Coding Index, four public coding benchmarks
GLM 5.3
GLM 5.3 scores 73.1 against 28.5.
2 benchmarks behind each score
Intelligence
AA Intelligence Index
GLM 5.3
GLM 5.3 leads, 44.8 against 10.2.
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 67.9%.
Same board, both measured
Math
AIME
No call
Only GPT-4.1 Mini has a AIME result, 49.4%.
Directional only
Knowledge
MMLU Pro
GLM 5.3
GLM 5.3 leads, 86.8% against 77.2%.
Same board, both measured
Web design
Design Arena: websites
GLM 5.3
GLM 5.3 leads, 1295 against 997.
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 1.05M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 5.3
GLM 5.3 $0.0008, GPT-4.1 Mini $0.0012 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GLM 5.3
GLM 5.3 $0.36, GPT-4.1 Mini $1.818.
Published attempt cost and pass rate

At a glance

Coding index: GLM 5.3 73.1 from 2 benchmarks, GPT-4.1 Mini 28.5 from 2 benchmarks.
Widest category: Web design, 40.0 points apart on average over 6 shared benchmarks.
Widest single gap: FrontierMath, tiers 1 to 3: GLM 5.3 68.8% against GPT-4.1 Mini 6.7%, 62.1 points apart on a 0 to 100 scale.
Output price: GLM 5.3 $1.40 against GPT-4.1 Mini $1.60 per million output tokens, 1.1x apart.
Context window: GLM 5.3 takes 1.05M tokens, GPT-4.1 Mini 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
Category	GLM 5.3	Bars	GPT-4.1 Mini	Ties
Coding	5		0
Intelligence	2		0
Reasoning	9		0
Math	4		0
Knowledge	8		0
Web design	6		0
General	20		0
All shared boards	54		0

CodingLike-for-likeFour of these boards feed the Vibe Coding Index50

Coding benchmarks for GLM 5.3 and GPT-4.1 Mini
Benchmark	GLM 5.3	GPT-4.1 Mini	Leader
Aider polyglot	Not measured	32.4%	Only GPT-4.1 Mini measured
SWE-bench Verified	Not measured	23.9%	Only GPT-4.1 Mini measured
LiveBench coding	82.8%	Not measured	Only GLM 5.3 measured
LMArena WebDev	1621	Not measured	Only GLM 5.3 measured
AA Coding Index	74.8	20.2	GLM 5.3
LiveCodeBench (Vals AI)	80.5%	58.2%	GLM 5.3
SWE-bench Verified (Vals AI)	95.4%	Not measured	Only GLM 5.3 measured
Terminal-Bench 4 (Vals AI)	38.9%	Not measured	Only GLM 5.3 measured
Vibe Code Bench (Vals AI)	78.1%	Not measured	Only GLM 5.3 measured
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	48.3%	Only GPT-4.1 Mini measured
SciCode	59.0%	Not measured	Only GLM 5.3 measured
Terminal-Bench Hard (Artificial Analysis run)	Not measured	7.6%	Only GPT-4.1 Mini measured
FrontierCode	40.1%	Not measured	Only GLM 5.3 measured
FrontierSWE	30.2%	Not measured	Only GLM 5.3 measured
WeirdML	75.4%	37.6%	GLM 5.3
ALE-Bench	1317.4	Not measured	Only GLM 5.3 measured
LMArena Text: coding	1497	1367	GLM 5.3
LMArena Text: software and IT	1493	1360	GLM 5.3
LMArena WebDev: React	1641	Not measured	Only GLM 5.3 measured
LMArena WebDev: HTML	1595	Not measured	Only GLM 5.3 measured
Neither model has a result on the other 11 boards here

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

Intelligence benchmarks for GLM 5.3 and GPT-4.1 Mini
Benchmark	GLM 5.3	GPT-4.1 Mini	Leader
AA Intelligence Index	44.8	10.2	GLM 5.3
Epoch Capabilities Index	156.0	134.9	GLM 5.3

Agentic workDirectional onlyPublic boards, shown as published, kept out of the index17 boards, none shared

Agentic work benchmarks for GLM 5.3 and GPT-4.1 Mini
Benchmark	GLM 5.3	GPT-4.1 Mini	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	1255	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	52.9%	Only GPT-4.1 Mini measured
APEX-Agents	56.6%	Not measured	Only GLM 5.3 measured
Vending-Bench 2	$8,164	Not measured	Only 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 index90

Reasoning benchmarks for GLM 5.3 and GPT-4.1 Mini
Benchmark	GLM 5.3	GPT-4.1 Mini	Leader
GPQA Diamond	88.1%	67.9%	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	70.5%	Only GPT-4.1 Mini measured
GPQA Diamond (Artificial Analysis run)	91.7%	66.4%	GLM 5.3
Humanity's Last Exam (Artificial Analysis run)	42.3%	5.0%	GLM 5.3
GPQA Diamond (Epoch AI run)	90.9%	65.8%	GLM 5.3
ARC-AGI	Not measured	3.5%	Only GPT-4.1 Mini measured
ARC-AGI-2	Not measured	0.0%	Only GPT-4.1 Mini measured
Chess puzzles	37.0%	1.0%	GLM 5.3
Mystery game puzzles	33.0%	7.0%	GLM 5.3
LMArena Text: hard prompts	1490	1349	GLM 5.3
LMArena Text: expert	1514	1338	GLM 5.3
LMArena Text: hard prompts in English	1496	1362	GLM 5.3
LMArena Vision: diagrams	Not measured	1182	Only GPT-4.1 Mini measured
LMArena Vision: homework	Not measured	1214	Only GPT-4.1 Mini measured
Neither model has a result on the other 5 boards here

MathLike-for-likePublic boards, shown as published, kept out of the index40

Math benchmarks for GLM 5.3 and GPT-4.1 Mini
Benchmark	GLM 5.3	GPT-4.1 Mini	Leader
AIME	Not measured	49.4%	Only GPT-4.1 Mini measured
MATH 500 (Vals AI)	Not measured	88.0%	Only GPT-4.1 Mini measured
MGSM (Vals AI)	Not measured	87.8%	Only GPT-4.1 Mini measured
AA Math Index	Not measured	46.3	Only GPT-4.1 Mini measured
AIME 2025 (Artificial Analysis run)	Not measured	46.3%	Only GPT-4.1 Mini measured
OTIS Mock AIME	91.1%	44.7%	GLM 5.3
FrontierMath, tiers 1 to 3	68.8%	6.7%	GLM 5.3
FrontierMath, tier 4	29.3%	Not measured	Only GLM 5.3 measured
ProofBench	49.0%	Not measured	Only GLM 5.3 measured
MATH level 5	Not measured	87.3%	Only GPT-4.1 Mini measured
LMArena Text: math	1495	1343	GLM 5.3
LMArena Text: mathematical work	1497	1347	GLM 5.3
Neither model has a result on the other 1 board here

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

Knowledge benchmarks for GLM 5.3 and GPT-4.1 Mini
Benchmark	GLM 5.3	GPT-4.1 Mini	Leader
MMLU Pro	86.8%	77.2%	GLM 5.3
LegalBench (Vals AI)	84.8%	78.0%	GLM 5.3
CorpFin (Vals AI)	Not measured	57.9%	Only GPT-4.1 Mini measured
TaxEval (Vals AI)	72.4%	71.9%	GLM 5.3
MortgageTax (Vals AI)	Not measured	65.5%	Only GPT-4.1 Mini measured
MedQA (Vals AI)	Not measured	84.6%	Only GPT-4.1 Mini measured
MedCode (Vals AI)	42.9%	Not measured	Only GLM 5.3 measured
MMLU Pro (Artificial Analysis run)	Not measured	78.1%	Only GPT-4.1 Mini measured
SimpleQA Verified	41.0%	12.7%	GLM 5.3
LMArena Text: science	1498	1328	GLM 5.3
LMArena Text: medicine and healthcare	1481	1324	GLM 5.3
LMArena Text: legal and government	1473	1343	GLM 5.3
LMArena Text: business and finance	1459	1322	GLM 5.3
Neither model has a result on the other 4 boards here

Web designLike-for-likePublic boards, shown as published, kept out of the index60

Web design benchmarks for GLM 5.3 and GPT-4.1 Mini
Benchmark	GLM 5.3	GPT-4.1 Mini	Leader
Design Arena: websites	1295	997	GLM 5.3
Design Arena: UI components	1314	961	GLM 5.3
Design Arena: game dev	1311	1071	GLM 5.3
Design Arena: data viz	1260	1034	GLM 5.3
Design Arena: 3D	1332	837	GLM 5.3
Design Arena: all code categories	1304	994	GLM 5.3
Design Arena: SVG	1292	Not measured	Only GLM 5.3 measured
Design Arena: ASCII art	1213	Not measured	Only GLM 5.3 measured
LiveBench design	77.3%	Not measured	Only GLM 5.3 measured

GeneralLike-for-likePublic boards, shown as published, kept out of the index200

General benchmarks for GLM 5.3 and GPT-4.1 Mini
Benchmark	GLM 5.3	GPT-4.1 Mini	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
MedScribe (Vals AI)	88.8%	Not measured	Only GLM 5.3 measured
IFBench	Not measured	38.3%	Only GPT-4.1 Mini measured
Long context reasoning (AA-LCR)	79.7%	44.0%	GLM 5.3
DTBench	87.7%	68.8%	GLM 5.3
LMCA	55.5%	21.1%	GLM 5.3
Fiction.LiveBench	Not measured	44.4%	Only GPT-4.1 Mini measured
LMArena Text	1474	1341	GLM 5.3
LMArena Text: instruction following	1479	1333	GLM 5.3
LMArena Text: creative writing	1458	1299	GLM 5.3
LMArena Vision	Not measured	1181	Only GPT-4.1 Mini measured
LMArena Text: English	1482	1356	GLM 5.3
LMArena Text: non-English	1459	1318	GLM 5.3
LMArena Text: Chinese	1532	1329	GLM 5.3
LMArena Text: German	1488	1350	GLM 5.3
LMArena Text: Japanese	1460	1290	GLM 5.3
LMArena Text: Korean	1460	1298	GLM 5.3
LMArena Text: French	1503	1358	GLM 5.3
LMArena Text: Spanish	1462	1319	GLM 5.3
LMArena Text: Russian	1469	1324	GLM 5.3
LMArena Text: Polish	1466	1326	GLM 5.3
LMArena Text: multi-turn	1472	1354	GLM 5.3
LMArena Text: longer queries	1483	1344	GLM 5.3
LMArena Text: writing and language	1469	1319	GLM 5.3
LMArena Text: entertainment and media	1447	1300	GLM 5.3
LMArena Vision: English	Not measured	1191	Only GPT-4.1 Mini measured
LMArena Vision: Chinese	Not measured	1186	Only GPT-4.1 Mini measured
LMArena Vision: reading text	Not measured	1188	Only GPT-4.1 Mini measured
LMArena Vision: humor	Not measured	1182	Only GPT-4.1 Mini measured
LMArena Vision: captioning	Not measured	1174	Only GPT-4.1 Mini measured
LMArena Vision: recognizing things	Not measured	1187	Only GPT-4.1 Mini measured
LMArena Vision: creative writing	Not measured	1182	Only GPT-4.1 Mini measured
Neither model has a result on the other 12 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-4.1 Mini
Benchmark	GLM 5.3	GPT-4.1 Mini	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$0.0008Fits in one request

GPT-4.1 Mini$0.0012Fits in one request

GLM 5.3 costs less

Repository review

50K input + 3K output tokens

GLM 5.3$0.0081Fits in one request

GPT-4.1 Mini$0.025Fits in one request

GLM 5.3 costs less

Agent loop

200K input + 10K output tokens

GLM 5.3$0.030Fits in one request

GPT-4.1 Mini$0.096Fits 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-4.1 Mini
Specification	Z.ai GLM 5.3	OpenAI GPT-4.1 Mini
Maker	Z.ai	OpenAI
Weights	Open weights	No public weights listed
Released	August 18, 2026	April 14, 2025
Context window	1.05M	1.05M
Input $/Mtok	$0.079	$0.40
Output $/Mtok	$1.40	$1.60
Coding index	73.1	28.5
Independent measurements behind it	2	2
Cost per solved task	$0.36	$1.818
Source	OpenRouter listing	OpenRouter listing

Questions

Is GLM 5.3 better than GPT-4.1 Mini for coding?

Pick GLM 5.3 for coding: it scores 73.1 on the Vibe Coding Index against 28.5 for GPT-4.1 Mini. GPT-4.1 Mini is neither cheaper nor larger in context, and leads no benchmark both models were measured on.

Which is cheaper, GLM 5.3 or GPT-4.1 Mini?

GLM 5.3 costs $0.079 per million input tokens and $1.40 per million output tokens; GPT-4.1 Mini costs $0.40 and $1.60. Per solved SWE-bench Verified task, GLM 5.3 costs $0.36 and GPT-4.1 Mini $1.818.

Which has the bigger context window, GLM 5.3 or GPT-4.1 Mini?

GLM 5.3, with 1.05M tokens against 1.05M.

How many benchmarks is the GLM 5.3 vs GPT-4.1 Mini comparison based on?

54 benchmarks have a result for both models as of October 21, 2026. GLM 5.3's coding index rests on 2 independent benchmarks and GPT-4.1 Mini's on 2. Where only one model has a result, the page says so instead of naming a winner.

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Decision reading

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