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GLM 5.2 vs MiniMax M3

GLM 5.2 leads MiniMax M3 on coding, 61.1 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; 87 benchmarks have a result for both.

Replace GLM 5.2 Replace MiniMax M3
GLM 5.2Z.ai
Coding
61.1
Overall
62.5
Output $/M
$4.40
74
MiniMax M3MiniMax
Coding
39.2
Overall
51.7
Output $/M
$1.20
13
vs boards won of 87 shared

GLM 5.2 or MiniMax M3: which to pick

Pick GLM 5.2 for coding: it scores 61.1 on the Vibe Coding Index against 39.2 for MiniMax M3, though on 4 benchmarks against 6.

Pick MiniMax M3 for output tokens 3.7x cheaper ($1.20 vs $4.40 per million) and a lower cost per solved task ($0.56 against $0.86).

Model AGLM 5.2Z.ai61.1/1004 independent benchmarks$0.86 per solved taskModel BMiniMax M3MiniMax39.2/1006 independent benchmarks$0.56 per solved task
Shared results
87
GLM 5.2 only
17
MiniMax M3 only
30
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 5.2 61.1 from 4 benchmarks, MiniMax M3 39.2 from 6 benchmarks.
Widest category
Math, 12.0 points apart on average over 4 shared benchmarks.
Widest single gap
Vending-Bench 2: GLM 5.2 $8,314 against MiniMax M3 $2,158, 38.5 points apart on a 0 to 100 scale.
Output price
GLM 5.2 $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
CategoryGLM 5.2BarsMiniMax M3Ties
Coding142
Intelligence20
Agentic work113
Reasoning82
Math40
Knowledge63
Web design80
General213
All shared boards7413
CodingLike-for-likeSix of these boards feed the Vibe Coding Index142
Coding benchmarks for GLM 5.2 and MiniMax M3
BenchmarkGLM 5.2MiniMax M3Leader
LiveBench codingNot measured70.9%Only MiniMax M3 measured
LMArena WebDev16041482GLM 5.2
AA Coding Index68.858.6GLM 5.2
LiveCodeBench (Vals AI)69.5%82.2%MiniMax M3
SWE-bench Verified (Vals AI)82.8%75.0%GLM 5.2
Terminal-Bench 4 (Vals AI)Not measured1.0%Only MiniMax M3 measured
Vibe Code Bench (Vals AI)64.0%47.6%GLM 5.2
Code migration (Vals AI)37.9%19.9%GLM 5.2
ProgramBench (Vals AI)0.5%Not measuredOnly GLM 5.2 measured
Terminal-Bench 2.1 (Vals AI)67.8%53.6%GLM 5.2
SWE-bench Multimodal (Vals AI)Not measured33.3%Only MiniMax M3 measured
Vibe Code Bench 1-100 (Vals AI)Not measured9.2%Only MiniMax M3 measured
SkillsBench (Vals AI)45.1%51.5%MiniMax M3
CyberBench (Vals AI)Not measured65.0%Only MiniMax M3 measured
SciCode51.2%47.1%GLM 5.2
Terminal-Bench Hard (Artificial Analysis run)50.8%42.4%GLM 5.2
FrontierCode24.5%14.7%GLM 5.2
WeirdML70.1%Not measuredOnly GLM 5.2 measured
ALE-Bench1047.0640.0GLM 5.2
LMArena Text: coding14871470GLM 5.2
LMArena Text: software and IT14861466GLM 5.2
LMArena WebDev: React16171478GLM 5.2
LMArena WebDev: HTML15651455GLM 5.2
LMArena WebDev: from an imageNot measured1470Only MiniMax M3 measured
SWE Atlas: Codebase QnA48.1%Not measuredOnly GLM 5.2 measured
SWE Atlas: Refactoring42.4%Not measuredOnly GLM 5.2 measured
SWE Atlas: Test Writing41.5%Not measuredOnly GLM 5.2 measured
Neither model has a result on the other 16 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
Intelligence benchmarks for GLM 5.2 and MiniMax M3
BenchmarkGLM 5.2MiniMax M3Leader
AA Intelligence Index33.729.2GLM 5.2
Epoch Capabilities Index151.9146.9GLM 5.2
Agentic workLike-for-likePublic boards, shown as published, kept out of the index113
Agentic work benchmarks for GLM 5.2 and MiniMax M3
BenchmarkGLM 5.2MiniMax M3Leader
AA Agentic Index38.429.5GLM 5.2
Design Arena: agents building web apps12221200GLM 5.2
Design Arena: agents building full-stack apps12411197GLM 5.2
Design Arena: agents building mobile apps11591169MiniMax M3
Design Arena: agents building Android apps11771158GLM 5.2
Design Arena: agents building Godot games1142Not measuredOnly GLM 5.2 measured
Design Arena: agents building games12061159GLM 5.2
Design Arena: HTML slides11141120MiniMax M3
Design Arena: PowerPoint slides11671178MiniMax M3
Design Arena: spreadsheetsNot measured1042Only MiniMax M3 measured
LiveBench researchNot measured68.0%Only MiniMax M3 measured
LiveBench browserNot measured8.0%Only MiniMax M3 measured
LiveBench long agenticNot measured12.4%Only MiniMax M3 measured
SREBench (Vals AI)0.0%Not measuredOnly GLM 5.2 measured
Finance Agent (Vals AI)49.7%48.3%GLM 5.2
Tax Agent Bench (Vals AI)Not measured20.6%Only MiniMax M3 measured
Legal Research Bench (Vals AI)31.3%29.8%GLM 5.2
Excel Modeling Benchmark (Vals AI)61.5%47.8%GLM 5.2
τ²-Bench Telecom99.1%88.9%GLM 5.2
APEX-Agents45.2%37.7%GLM 5.2
OSWorld 2.0Not measured4.6%Only MiniMax M3 measured
PostTrainBench31.7%Not measuredOnly GLM 5.2 measured
Vending-Bench 2$8,314$2,158GLM 5.2
MCP Atlas77.8%Not measuredOnly GLM 5.2 measured
Neither model has a result on the other 18 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index82
Reasoning benchmarks for GLM 5.2 and MiniMax M3
BenchmarkGLM 5.2MiniMax M3Leader
GPQA Diamond85.6%92.7%MiniMax M3
LiveBench reasoningNot measured74.5%Only MiniMax M3 measured
MMMU Pro (Vals AI)Not measured81.2%Only MiniMax M3 measured
GPQA Diamond (Artificial Analysis run)89.5%92.9%MiniMax M3
Humanity's Last Exam (Artificial Analysis run)41.1%39.0%GLM 5.2
GPQA Diamond (Epoch AI run)91.9%90.9%GLM 5.2
ARC-AGI77.0%Not measuredOnly GLM 5.2 measured
ARC-AGI-222.8%Not measuredOnly GLM 5.2 measured
SimpleBench58.8%45.8%GLM 5.2
Chess puzzles22.0%13.0%GLM 5.2
Mystery game puzzles19.0%8.0%GLM 5.2
EBR-bench9.5%Not measuredOnly GLM 5.2 measured
LMArena Text: hard prompts14811447GLM 5.2
LMArena Text: expert14871461GLM 5.2
LMArena Text: hard prompts in English14811455GLM 5.2
LMArena Vision: diagramsNot measured1266Only MiniMax M3 measured
LMArena Vision: homeworkNot measured1268Only MiniMax M3 measured
Kagi LLM Benchmark62.6%Not measuredOnly GLM 5.2 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 5.2 and MiniMax M3
BenchmarkGLM 5.2MiniMax M3Leader
OTIS Mock AIME86.4%71.1%GLM 5.2
FrontierMath, tiers 1 to 359.2%Not measuredOnly GLM 5.2 measured
FrontierMath, tier 429.3%Not measuredOnly GLM 5.2 measured
ProofBench35.0%18.0%GLM 5.2
LMArena Text: math14831429GLM 5.2
LMArena Text: mathematical work14891448GLM 5.2
MathArena22.7%Not measuredOnly GLM 5.2 measured
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index63
Knowledge benchmarks for GLM 5.2 and MiniMax M3
BenchmarkGLM 5.2MiniMax M3Leader
MMLU Pro86.7%84.2%GLM 5.2
LegalBench (Vals AI)84.1%85.4%MiniMax M3
CorpFin (Vals AI)66.1%68.1%MiniMax M3
TaxEval (Vals AI)73.3%72.7%GLM 5.2
MortgageTax (Vals AI)Not measured68.4%Only MiniMax M3 measured
MedCode (Vals AI)40.8%46.3%MiniMax M3
SimpleQA Verified34.2%Not measuredOnly GLM 5.2 measured
LMArena Text: science14891452GLM 5.2
LMArena Text: medicine and healthcare14751441GLM 5.2
LMArena Text: legal and government14861437GLM 5.2
LMArena Text: business and finance14511434GLM 5.2
Neither model has a result on the other 9 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index80
Web design benchmarks for GLM 5.2 and MiniMax M3
BenchmarkGLM 5.2MiniMax M3Leader
Design Arena: websites12851257GLM 5.2
Design Arena: UI components12941243GLM 5.2
Design Arena: game dev12671210GLM 5.2
Design Arena: data viz12861236GLM 5.2
Design Arena: 3D12871208GLM 5.2
Design Arena: all code categories12871246GLM 5.2
Design Arena: SVG12131160GLM 5.2
Design Arena: ASCII art11911161GLM 5.2
LiveBench designNot measured66.2%Only MiniMax M3 measured
GeneralLike-for-likePublic boards, shown as published, kept out of the index213
General benchmarks for GLM 5.2 and MiniMax M3
BenchmarkGLM 5.2MiniMax M3Leader
LiveBench data analysisNot measured76.2%Only MiniMax M3 measured
Public Benefits Bench (Vals AI)Not measured64.1%Only MiniMax M3 measured
SAGE (Vals AI)Not measured50.6%Only MiniMax M3 measured
MedScribe (Vals AI)83.5%87.3%MiniMax M3
Vals Multimodal IndexNot measured60.0%Only MiniMax M3 measured
IFBench73.3%82.9%MiniMax M3
Long context reasoning (AA-LCR)78.3%83.0%MiniMax M3
DTBench93.6%78.9%GLM 5.2
LMCA45.8%33.7%GLM 5.2
Surface Evolver Bench55.6%55.0%GLM 5.2
LMArena Text14701433GLM 5.2
LMArena Text: instruction following14661433GLM 5.2
LMArena Text: creative writing14631404GLM 5.2
LMArena VisionNot measured1251Only MiniMax M3 measured
LMArena Text: English14751443GLM 5.2
LMArena Text: non-English14591420GLM 5.2
LMArena Text: Chinese15221466GLM 5.2
LMArena Text: German14701428GLM 5.2
LMArena Text: Japanese14501380GLM 5.2
LMArena Text: Korean14401373GLM 5.2
LMArena Text: French14791444GLM 5.2
LMArena Text: Spanish14781433GLM 5.2
LMArena Text: Russian14681426GLM 5.2
LMArena Text: Polish14561423GLM 5.2
LMArena Text: multi-turn14691443GLM 5.2
LMArena Text: longer queries14801446GLM 5.2
LMArena Text: writing and language14631419GLM 5.2
LMArena Text: entertainment and media14501403GLM 5.2
LMArena Vision: EnglishNot measured1263Only MiniMax M3 measured
LMArena Vision: ChineseNot measured1311Only MiniMax M3 measured
LMArena Vision: reading textNot measured1259Only MiniMax M3 measured
LMArena Vision: humorNot measured1235Only MiniMax M3 measured
LMArena Vision: recognizing thingsNot measured1197Only MiniMax M3 measured
LMArena Vision: creative writingNot measured1234Only MiniMax M3 measured
LMArena DocumentNot measured1435Only MiniMax M3 measured
EQ-Bench 412221150GLM 5.2
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 5.2 and MiniMax M3
BenchmarkGLM 5.2MiniMax M3Leader
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 5.2$0.0036Fits in one request
MiniMax M3$0.0009Fits in one request

MiniMax M3 costs less

Repository review

50K input + 3K output tokens

GLM 5.2$0.083Fits in one request
MiniMax M3$0.019Fits in one request

MiniMax M3 costs less

Agent loop

200K input + 10K output tokens

GLM 5.2$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 5.2 and MiniMax M3
SpecificationZ.ai GLM 5.2MiniMax M3
MakerZ.aiMiniMax
WeightsOpen weightsOpen weights
ReleasedJune 16, 2026May 31, 2026
Context window1.05M1.05M
Input $/Mtok$1.40$0.30
Output $/Mtok$4.40$1.20
Coding index61.139.2
Independent measurements behind it46
Cost per solved task$0.86$0.56
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 5.2 better than MiniMax M3 for coding?

Pick GLM 5.2 for coding: it scores 61.1 on the Vibe Coding Index against 39.2 for MiniMax M3, though on 4 benchmarks against 6. Pick MiniMax M3 for output tokens 3.7x cheaper ($1.20 vs $4.40 per million) and a lower cost per solved task ($0.56 against $0.86).

Which is cheaper, GLM 5.2 or MiniMax M3?

GLM 5.2 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 5.2 costs $0.86 and MiniMax M3 $0.56.

Which has the bigger context window, GLM 5.2 or MiniMax M3?

Neither: both take 1.05M tokens.

How many benchmarks is the GLM 5.2 vs MiniMax M3 comparison based on?

87 benchmarks have a result for both models as of October 22, 2026. GLM 5.2's coding index rests on 4 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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