GLM 5.3 vs MiniMax M3

GLM 5.3 leads MiniMax M3 on coding, 70.6 to 39.2 on the Vibe Coding Index, and MiniMax M3 costs 3.7x less per output token ($1.20 vs $4.40 per million). Updated October 22, 2026; 88 benchmarks have a result for both.

Replace GLM 5.3 Replace MiniMax M3

GLM 5.3Z.ai

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

MiniMax M3MiniMax

Coding: 39.2
Overall: 51.7
Output $/M: $1.20

vs boards won of 88 shared, 1 tied

GLM 5.3 or MiniMax M3: which to pick

Pick GLM 5.3 for coding: it scores 70.6 on the Vibe Coding Index against 39.2 for MiniMax M3.

Pick MiniMax M3 for output tokens 3.7x cheaper ($1.20 vs $4.40 per million) and work measured by LiveCodeBench (Vals AI) or SkillsBench (Vals AI), where it leads.

Model AGLM 5.3Z.ai70.6/1006 independent benchmarks$0.36 per solved task Model BMiniMax M3MiniMax39.2/1006 independent benchmarks$0.56 per solved task

881228

Shared results: 88
GLM 5.3 only: 12
MiniMax M3 only: 28
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 39.2.
6 benchmarks behind each score
Intelligence
AA Intelligence Index
GLM 5.3
GLM 5.3 leads, 44.8 against 29.2.
Same board, both measured
Agentic work
AA Agentic Index
GLM 5.3
GLM 5.3 leads, 53.1 against 29.5.
Same board, both measured
Reasoning
GPQA Diamond
MiniMax M3
MiniMax M3 leads, 92.7% against 88.1%.
Same board, both measured
Knowledge
MMLU Pro
GLM 5.3
GLM 5.3 leads, 86.8% against 84.2%.
Same board, both measured
Web design
Design Arena: websites
GLM 5.3
GLM 5.3 leads, 1295 against 1257.
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
No call
Both take 1.05M tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
MiniMax M3
GLM 5.3 $0.0036, MiniMax M3 $0.0009 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GLM 5.3
GLM 5.3 $0.36, MiniMax M3 $0.56.
Published attempt cost and pass rate

At a glance

Coding index: GLM 5.3 70.6 from 6 benchmarks, MiniMax M3 39.2 from 6 benchmarks.
Widest category: Coding, 18.0 points apart on average over 19 shared benchmarks.
Widest single gap: Terminal-Bench 4 (Vals AI): GLM 5.3 38.9% against MiniMax M3 1.0%, 50.5 points apart on a 0 to 100 scale.
Output price: GLM 5.3 $4.40 against MiniMax M3 $1.20 per million output tokens, 3.7x 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
Category	GLM 5.3	MiniMax M3	Ties
Coding	17	2
Intelligence	2	0
Agentic work	12	1
Reasoning	7	2	1 tie
Math	4	0
Knowledge	5	3
Web design	9	0
General	21	2
All shared boards	77	10	1 tie

CodingLike-for-likeSix of these boards feed the Vibe Coding Index172

Coding benchmarks for GLM 5.3 and MiniMax M3
Benchmark	GLM 5.3	MiniMax M3	Leader
LiveBench coding	82.8%	70.9%	GLM 5.3
LMArena WebDev	1621	1482	GLM 5.3
AA Coding Index	74.8	58.6	GLM 5.3
LiveCodeBench (Vals AI)	80.5%	82.2%	MiniMax M3
SWE-bench Verified (Vals AI)	95.4%	75.0%	GLM 5.3
Terminal-Bench 4 (Vals AI)	38.9%	1.0%	GLM 5.3
Vibe Code Bench (Vals AI)	78.1%	47.6%	GLM 5.3
Code migration (Vals AI)	44.2%	19.9%	GLM 5.3
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%	53.6%	GLM 5.3
SWE-bench Multimodal (Vals AI)	Not measured	33.3%	Only MiniMax M3 measured
Vibe Code Bench 1-100 (Vals AI)	20.0%	9.2%	GLM 5.3
SkillsBench (Vals AI)	47.5%	51.5%	MiniMax M3
CyberBench (Vals AI)	72.1%	65.0%	GLM 5.3
SciCode	59.0%	47.1%	GLM 5.3
Terminal-Bench Hard (Artificial Analysis run)	Not measured	42.4%	Only MiniMax M3 measured
FrontierCode	40.1%	14.7%	GLM 5.3
FrontierSWE	30.2%	Not measured	Only GLM 5.3 measured
WeirdML	75.4%	Not measured	Only GLM 5.3 measured
ALE-Bench	1317.4	640.0	GLM 5.3
LMArena Text: coding	1497	1470	GLM 5.3
LMArena Text: software and IT	1493	1466	GLM 5.3
LMArena WebDev: React	1641	1478	GLM 5.3
LMArena WebDev: HTML	1595	1455	GLM 5.3
LMArena WebDev: from an image	Not measured	1470	Only MiniMax M3 measured
Neither model has a result on the other 13 boards here

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

Intelligence benchmarks for GLM 5.3 and MiniMax M3
Benchmark	GLM 5.3	MiniMax M3	Leader
AA Intelligence Index	44.8	29.2	GLM 5.3
Epoch Capabilities Index	156.0	146.9	GLM 5.3

Agentic workLike-for-likePublic boards, shown as published, kept out of the index121

Agentic work benchmarks for GLM 5.3 and MiniMax M3
Benchmark	GLM 5.3	MiniMax M3	Leader
AA Agentic Index	53.1	29.5	GLM 5.3
Design Arena: agents building web apps	Not measured	1200	Only MiniMax M3 measured
Design Arena: agents building full-stack apps	Not measured	1197	Only MiniMax M3 measured
Design Arena: agents building mobile apps	1194	1169	GLM 5.3
Design Arena: agents building Android apps	Not measured	1159	Only MiniMax M3 measured
Design Arena: agents building games	Not measured	1158	Only MiniMax M3 measured
Design Arena: HTML slides	1189	1130	GLM 5.3
Design Arena: PowerPoint slides	1230	1181	GLM 5.3
Design Arena: spreadsheets	Not measured	1028	Only MiniMax M3 measured
LiveBench research	73.2%	68.0%	GLM 5.3
LiveBench browser	0.0%	8.0%	MiniMax M3
LiveBench long agentic	35.9%	12.4%	GLM 5.3
SREBench (Vals AI)	9.2%	Not measured	Only GLM 5.3 measured
Finance Agent (Vals AI)	55.8%	48.3%	GLM 5.3
Tax Agent Bench (Vals AI)	39.7%	20.6%	GLM 5.3
Legal Research Bench (Vals AI)	49.0%	29.8%	GLM 5.3
Harvey's Legal Agent Benchmark (Vals AI)	14.6%	Not measured	Only GLM 5.3 measured
Excel Modeling Benchmark (Vals AI)	56.3%	47.8%	GLM 5.3
Terminal-Bench-Science (Vals AI)	5.7%	Not measured	Only GLM 5.3 measured
τ²-Bench Telecom	Not measured	88.9%	Only MiniMax M3 measured
APEX-Agents	56.6%	37.7%	GLM 5.3
OSWorld 2.0	Not measured	4.6%	Only MiniMax M3 measured
Vending-Bench 2	$8,164	$2,158	GLM 5.3
Neither model has a result on the other 18 boards here

ReasoningLike-for-likePublic boards, shown as published, kept out of the index72

Reasoning benchmarks for GLM 5.3 and MiniMax M3
Benchmark	GLM 5.3	MiniMax M3	Leader
GPQA Diamond	88.1%	92.7%	MiniMax M3
LiveBench reasoning	85.8%	74.5%	GLM 5.3
MysteryMechanism (Vals AI)	23.0%	Not measured	Only GLM 5.3 measured
MMMU Pro (Vals AI)	Not measured	81.2%	Only MiniMax M3 measured
GPQA Diamond (Artificial Analysis run)	91.7%	92.9%	MiniMax M3
Humanity's Last Exam (Artificial Analysis run)	42.3%	39.0%	GLM 5.3
GPQA Diamond (Epoch AI run)	90.9%	90.9%	Tie
SimpleBench	Not measured	45.8%	Only MiniMax M3 measured
Chess puzzles	37.0%	13.0%	GLM 5.3
Mystery game puzzles	33.0%	8.0%	GLM 5.3
LMArena Text: hard prompts	1490	1447	GLM 5.3
LMArena Text: expert	1514	1461	GLM 5.3
LMArena Text: hard prompts in English	1496	1455	GLM 5.3
LMArena Vision: diagrams	Not measured	1266	Only MiniMax M3 measured
LMArena Vision: homework	Not measured	1268	Only MiniMax M3 measured
Neither model has a result on the other 7 boards here

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

Math benchmarks for GLM 5.3 and MiniMax M3
Benchmark	GLM 5.3	MiniMax M3	Leader
OTIS Mock AIME	91.1%	71.1%	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%	18.0%	GLM 5.3
LMArena Text: math	1495	1429	GLM 5.3
LMArena Text: mathematical work	1497	1448	GLM 5.3
Neither model has a result on the other 7 boards here

KnowledgeLike-for-likePublic boards, shown as published, kept out of the index53

Knowledge benchmarks for GLM 5.3 and MiniMax M3
Benchmark	GLM 5.3	MiniMax M3	Leader
MMLU Pro	86.8%	84.2%	GLM 5.3
LegalBench (Vals AI)	84.8%	85.4%	MiniMax M3
CorpFin (Vals AI)	Not measured	68.1%	Only MiniMax M3 measured
TaxEval (Vals AI)	72.4%	72.7%	MiniMax M3
MortgageTax (Vals AI)	Not measured	68.4%	Only MiniMax M3 measured
MedCode (Vals AI)	42.9%	46.3%	MiniMax M3
SimpleQA Verified	41.0%	Not measured	Only GLM 5.3 measured
LMArena Text: science	1498	1452	GLM 5.3
LMArena Text: medicine and healthcare	1481	1441	GLM 5.3
LMArena Text: legal and government	1473	1437	GLM 5.3
LMArena Text: business and finance	1459	1434	GLM 5.3
Neither model has a result on the other 7 boards here

Web designLike-for-likePublic boards, shown as published, kept out of the index90

Web design benchmarks for GLM 5.3 and MiniMax M3
Benchmark	GLM 5.3	MiniMax M3	Leader
Design Arena: websites	1295	1257	GLM 5.3
Design Arena: UI components	1314	1242	GLM 5.3
Design Arena: game dev	1311	1210	GLM 5.3
Design Arena: data viz	1260	1236	GLM 5.3
Design Arena: 3D	1332	1208	GLM 5.3
Design Arena: all code categories	1304	1247	GLM 5.3
Design Arena: SVG	1294	1161	GLM 5.3
Design Arena: ASCII art	1208	1161	GLM 5.3
LiveBench design	77.3%	66.2%	GLM 5.3

GeneralLike-for-likePublic boards, shown as published, kept out of the index212

General benchmarks for GLM 5.3 and MiniMax M3
Benchmark	GLM 5.3	MiniMax M3	Leader
Vals Index	59.7%	Not measured	Only GLM 5.3 measured
LiveBench data analysis	70.2%	76.2%	MiniMax M3
Public Benefits Bench (Vals AI)	68.5%	64.1%	GLM 5.3
SAGE (Vals AI)	Not measured	50.6%	Only MiniMax M3 measured
MedScribe (Vals AI)	88.8%	87.3%	GLM 5.3
Vals Multimodal Index	Not measured	60.0%	Only MiniMax M3 measured
IFBench	Not measured	82.9%	Only MiniMax M3 measured
Long context reasoning (AA-LCR)	79.7%	83.0%	MiniMax M3
DTBench	87.7%	78.9%	GLM 5.3
LMCA	55.5%	33.7%	GLM 5.3
Surface Evolver Bench	Not measured	55.0%	Only MiniMax M3 measured
LMArena Text	1474	1433	GLM 5.3
LMArena Text: instruction following	1479	1433	GLM 5.3
LMArena Text: creative writing	1458	1404	GLM 5.3
LMArena Vision	Not measured	1251	Only MiniMax M3 measured
LMArena Text: English	1482	1443	GLM 5.3
LMArena Text: non-English	1459	1420	GLM 5.3
LMArena Text: Chinese	1532	1466	GLM 5.3
LMArena Text: German	1488	1428	GLM 5.3
LMArena Text: Japanese	1460	1380	GLM 5.3
LMArena Text: Korean	1460	1373	GLM 5.3
LMArena Text: French	1503	1444	GLM 5.3
LMArena Text: Spanish	1462	1433	GLM 5.3
LMArena Text: Russian	1469	1426	GLM 5.3
LMArena Text: Polish	1466	1423	GLM 5.3
LMArena Text: multi-turn	1472	1443	GLM 5.3
LMArena Text: longer queries	1483	1446	GLM 5.3
LMArena Text: writing and language	1469	1419	GLM 5.3
LMArena Text: entertainment and media	1447	1403	GLM 5.3
LMArena Vision: English	Not measured	1263	Only MiniMax M3 measured
LMArena Vision: Chinese	Not measured	1311	Only MiniMax M3 measured
LMArena Vision: reading text	Not measured	1259	Only MiniMax M3 measured
LMArena Vision: humor	Not measured	1235	Only MiniMax M3 measured
LMArena Vision: recognizing things	Not measured	1197	Only MiniMax M3 measured
LMArena Vision: creative writing	Not measured	1234	Only MiniMax M3 measured
LMArena Document	Not measured	1435	Only MiniMax M3 measured
Neither model has a result on the other 10 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 MiniMax M3
Benchmark	GLM 5.3	MiniMax M3	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

MiniMax M3$0.0009Fits in one request

MiniMax M3 costs less

Repository review

50K input + 3K output tokens

GLM 5.3$0.083Fits in one request

MiniMax M3$0.019Fits in one request

MiniMax M3 costs less

Agent loop

200K input + 10K output tokens

GLM 5.3$0.324Fits in one request

MiniMax M3$0.072Fits in one request

MiniMax M3 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 MiniMax M3
Specification	Z.ai GLM 5.3	MiniMax M3
Maker	Z.ai	MiniMax
Weights	Open weights	Open weights
Released	August 18, 2026	May 31, 2026
Context window	1.05M	1.05M
Input $/Mtok	$1.40	$0.30
Output $/Mtok	$4.40	$1.20
Coding index	70.6	39.2
Independent measurements behind it	6	6
Cost per solved task	$0.36	$0.56
Source	OpenRouter listing	OpenRouter listing

Questions

Is GLM 5.3 better than MiniMax M3 for coding?

Pick GLM 5.3 for coding: it scores 70.6 on the Vibe Coding Index against 39.2 for MiniMax M3. Pick MiniMax M3 for output tokens 3.7x cheaper ($1.20 vs $4.40 per million) and work measured by LiveCodeBench (Vals AI) or SkillsBench (Vals AI), where it leads.

Which is cheaper, GLM 5.3 or MiniMax M3?

GLM 5.3 costs $1.40 per million input tokens and $4.40 per million output tokens; MiniMax M3 costs $0.30 and $1.20. Per solved SWE-bench Verified task, GLM 5.3 costs $0.36 and MiniMax M3 $0.56.

Which has the bigger context window, GLM 5.3 or MiniMax M3?

Neither: both take 1.05M tokens.

How many benchmarks is the GLM 5.3 vs MiniMax M3 comparison based on?

88 benchmarks have a result for both models as of October 22, 2026. GLM 5.3's coding index rests on 6 independent benchmarks and MiniMax M3's on 6. Where only one model has a result, the page says so instead of naming a winner.

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GLM 5.3 or MiniMax M3: which to pick

Which one for your work

Coding

Intelligence

Agentic work

Reasoning

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