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GLM 5.2 vs MiniMax M2.5

GLM 5.2 leads MiniMax M2.5 on coding, 61.1 to 37.2 on the Vibe Coding Index, and MiniMax M2.5 costs 3.7x less per output token ($1.20 vs $4.40 per million). Updated October 22, 2026; 60 benchmarks have a result for both.

Replace GLM 5.2 Replace MiniMax M2.5
GLM 5.2Z.ai
Coding
61.1
Overall
62.5
Output $/M
$4.40
59
MiniMax M2.5MiniMax
Coding
37.2
Overall
37.3
Output $/M
$1.20
1
vs boards won of 60 shared

GLM 5.2 or MiniMax M2.5: which to pick

Pick GLM 5.2 for coding: it scores 61.1 on the Vibe Coding Index against 37.2 for MiniMax M2.5.

Pick MiniMax M2.5 for output tokens 3.7x cheaper ($1.20 vs $4.40 per million) and a lower cost per solved task ($0.62 against $0.86).

Model AGLM 5.2Z.ai61.1/1004 independent benchmarks$0.86 per solved taskModel BMiniMax M2.5MiniMax37.2/1003 independent benchmarks$0.62 per solved task
Shared results
60
GLM 5.2 only
38
MiniMax M2.5 only
8
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 M2.5 37.2 from 3 benchmarks.
Widest category
Agentic work, 27.9 points apart on average over 2 shared benchmarks.
Widest single gap
Vending-Bench 2: GLM 5.2 $8,314 against MiniMax M2.5 -$23, 52.0 points apart on a 0 to 100 scale.
Output price
GLM 5.2 $4.40 against MiniMax M2.5 $1.20 per million output tokens, 3.7x apart.
Context window
GLM 5.2 takes 1.05M tokens, MiniMax M2.5 205K.

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 M2.5Ties
Coding91
Intelligence20
Agentic work20
Reasoning90
Math30
Knowledge80
Web design70
General190
All shared boards591
CodingLike-for-likeSix of these boards feed the Vibe Coding Index91
Coding benchmarks for GLM 5.2 and MiniMax M2.5
BenchmarkGLM 5.2MiniMax M2.5Leader
SWE-bench VerifiedNot measured75.8%Only MiniMax M2.5 measured
SWE-bench MultilingualNot measured68.3%Only MiniMax M2.5 measured
LMArena WebDev16041386GLM 5.2
AA Coding Index68.8Not measuredOnly GLM 5.2 measured
LiveCodeBench (Vals AI)69.5%79.2%MiniMax M2.5
SWE-bench Verified (Vals AI)82.8%74.2%GLM 5.2
Vibe Code Bench (Vals AI)64.0%14.8%GLM 5.2
Code migration (Vals AI)37.9%Not measuredOnly GLM 5.2 measured
ProgramBench (Vals AI)0.5%Not measuredOnly GLM 5.2 measured
Terminal-Bench 2.1 (Vals AI)67.8%Not measuredOnly GLM 5.2 measured
SkillsBench (Vals AI)45.1%Not measuredOnly GLM 5.2 measured
SciCode51.2%Not measuredOnly GLM 5.2 measured
Terminal-Bench Hard (Artificial Analysis run)50.8%34.8%GLM 5.2
Terminal-BenchNot measured42.7%Only MiniMax M2.5 measured
FrontierCode24.5%Not measuredOnly GLM 5.2 measured
WeirdML70.1%Not measuredOnly GLM 5.2 measured
ALE-Bench1047.0618.2GLM 5.2
LMArena Text: coding14871380GLM 5.2
LMArena Text: software and IT14861383GLM 5.2
LMArena WebDev: React16171374GLM 5.2
LMArena WebDev: HTML15651414GLM 5.2
Neither model has a result on the other 18 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
Intelligence benchmarks for GLM 5.2 and MiniMax M2.5
BenchmarkGLM 5.2MiniMax M2.5Leader
AA Intelligence Index33.722.8GLM 5.2
Epoch Capabilities Index151.9146.6GLM 5.2
Agentic workLike-for-likePublic boards, shown as published, kept out of the index20
Agentic work benchmarks for GLM 5.2 and MiniMax M2.5
BenchmarkGLM 5.2MiniMax M2.5Leader
AA Agentic Index38.4Not measuredOnly GLM 5.2 measured
Design Arena: agents building web apps1222Not measuredOnly GLM 5.2 measured
Design Arena: agents building full-stack apps1241Not measuredOnly GLM 5.2 measured
Design Arena: agents building mobile apps1159Not measuredOnly GLM 5.2 measured
Design Arena: agents building Android apps1178Not measuredOnly GLM 5.2 measured
Design Arena: agents building Godot games1142Not measuredOnly GLM 5.2 measured
Design Arena: agents building games1206Not measuredOnly GLM 5.2 measured
Design Arena: HTML slides1125Not measuredOnly GLM 5.2 measured
Design Arena: PowerPoint slides1170Not measuredOnly GLM 5.2 measured
SREBench (Vals AI)0.0%Not measuredOnly GLM 5.2 measured
Finance Agent (Vals AI)49.7%Not measuredOnly GLM 5.2 measured
Legal Research Bench (Vals AI)31.3%Not measuredOnly GLM 5.2 measured
Excel Modeling Benchmark (Vals AI)61.5%Not measuredOnly GLM 5.2 measured
τ²-Bench Telecom99.1%95.3%GLM 5.2
APEX-Agents45.2%Not measuredOnly GLM 5.2 measured
PostTrainBench31.7%Not measuredOnly GLM 5.2 measured
Vending-Bench 2$8,314-$23GLM 5.2
Neither model has a result on the other 24 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index90
Reasoning benchmarks for GLM 5.2 and MiniMax M2.5
BenchmarkGLM 5.2MiniMax M2.5Leader
GPQA Diamond85.6%82.1%GLM 5.2
GPQA Diamond (Artificial Analysis run)89.5%84.8%GLM 5.2
Humanity's Last Exam (Artificial Analysis run)41.1%20.5%GLM 5.2
GPQA Diamond (Epoch AI run)91.9%Not measuredOnly GLM 5.2 measured
ARC-AGI77.0%63.7%GLM 5.2
ARC-AGI-222.8%4.9%GLM 5.2
SimpleBench58.8%Not measuredOnly GLM 5.2 measured
Chess puzzles22.0%Not measuredOnly GLM 5.2 measured
Mystery game puzzles19.0%Not measuredOnly GLM 5.2 measured
EBR-bench9.5%Not measuredOnly GLM 5.2 measured
LMArena Text: hard prompts14811372GLM 5.2
LMArena Text: expert14871378GLM 5.2
LMArena Text: hard prompts in English14811384GLM 5.2
Kagi LLM Benchmark62.6%55.2%GLM 5.2
Neither model has a result on the other 8 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index30
Math benchmarks for GLM 5.2 and MiniMax M2.5
BenchmarkGLM 5.2MiniMax M2.5Leader
AIMENot measured88.8%Only MiniMax M2.5 measured
OTIS Mock AIME86.4%Not measuredOnly GLM 5.2 measured
FrontierMath, tiers 1 to 359.2%Not measuredOnly GLM 5.2 measured
FrontierMath, tier 429.3%Not measuredOnly GLM 5.2 measured
ProofBench35.0%4.0%GLM 5.2
LMArena Text: math14831378GLM 5.2
LMArena Text: mathematical work14891378GLM 5.2
Neither model has a result on the other 6 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index80
Knowledge benchmarks for GLM 5.2 and MiniMax M2.5
BenchmarkGLM 5.2MiniMax M2.5Leader
MMLU Pro86.7%80.1%GLM 5.2
LegalBench (Vals AI)84.1%80.0%GLM 5.2
CaseLaw (Vals AI)Not measured53.5%Only MiniMax M2.5 measured
CorpFin (Vals AI)66.1%59.6%GLM 5.2
TaxEval (Vals AI)73.3%68.2%GLM 5.2
MedQA (Vals AI)Not measured92.5%Only MiniMax M2.5 measured
MedCode (Vals AI)40.8%Not measuredOnly GLM 5.2 measured
SimpleQA Verified34.2%Not measuredOnly GLM 5.2 measured
LMArena Text: science14891369GLM 5.2
LMArena Text: medicine and healthcare14751357GLM 5.2
LMArena Text: legal and government14861366GLM 5.2
LMArena Text: business and finance14511356GLM 5.2
Neither model has a result on the other 6 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index70
Web design benchmarks for GLM 5.2 and MiniMax M2.5
BenchmarkGLM 5.2MiniMax M2.5Leader
Design Arena: websites12851222GLM 5.2
Design Arena: UI components12941173GLM 5.2
Design Arena: game dev12681176GLM 5.2
Design Arena: data viz12851172GLM 5.2
Design Arena: 3D12871170GLM 5.2
Design Arena: all code categories12871207GLM 5.2
Design Arena: SVG12131150GLM 5.2
Design Arena: ASCII art1191Not measuredOnly GLM 5.2 measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index190
General benchmarks for GLM 5.2 and MiniMax M2.5
BenchmarkGLM 5.2MiniMax M2.5Leader
MedScribe (Vals AI)83.5%Not measuredOnly GLM 5.2 measured
IFBench73.3%71.6%GLM 5.2
Long context reasoning (AA-LCR)78.3%73.3%GLM 5.2
DTBench93.6%Not measuredOnly GLM 5.2 measured
LMCA45.8%Not measuredOnly GLM 5.2 measured
CL-benchNot measured11.4%Only MiniMax M2.5 measured
Surface Evolver Bench55.6%Not measuredOnly GLM 5.2 measured
CL-bench LifeNot measured6.3%Only MiniMax M2.5 measured
LMArena Text14701359GLM 5.2
LMArena Text: instruction following14661353GLM 5.2
LMArena Text: creative writing14631331GLM 5.2
LMArena Text: English14751380GLM 5.2
LMArena Text: non-English14591338GLM 5.2
LMArena Text: Chinese15221393GLM 5.2
LMArena Text: German14701361GLM 5.2
LMArena Text: Japanese14501171GLM 5.2
LMArena Text: Korean14401233GLM 5.2
LMArena Text: French14791363GLM 5.2
LMArena Text: Spanish14781353GLM 5.2
LMArena Text: Russian14681359GLM 5.2
LMArena Text: Polish14561334GLM 5.2
LMArena Text: multi-turn14691364GLM 5.2
LMArena Text: longer queries14801366GLM 5.2
LMArena Text: writing and language14631345GLM 5.2
LMArena Text: entertainment and media14501329GLM 5.2
Neither model has a result on the other 21 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 M2.5
BenchmarkGLM 5.2MiniMax M2.5Leader
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 M2.5$0.0009Fits in one request

MiniMax M2.5 costs less

Repository review

50K input + 3K output tokens

GLM 5.2$0.083Fits in one request
MiniMax M2.5$0.019Fits in one request

MiniMax M2.5 costs less

Agent loop

200K input + 10K output tokens

GLM 5.2$0.324Fits in one request
MiniMax M2.5$0.072Exceeds the context window

MiniMax M2.5 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 M2.5
SpecificationZ.ai GLM 5.2MiniMax M2.5
MakerZ.aiMiniMax
WeightsOpen weightsOpen weights
ReleasedJune 16, 2026February 12, 2026
Context window1.05M205K
Input $/Mtok$1.40$0.30
Output $/Mtok$4.40$1.20
Coding index61.137.2
Independent measurements behind it43
Cost per solved task$0.86$0.62
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 5.2 better than MiniMax M2.5 for coding?

Pick GLM 5.2 for coding: it scores 61.1 on the Vibe Coding Index against 37.2 for MiniMax M2.5. Pick MiniMax M2.5 for output tokens 3.7x cheaper ($1.20 vs $4.40 per million) and a lower cost per solved task ($0.62 against $0.86).

Which is cheaper, GLM 5.2 or MiniMax M2.5?

GLM 5.2 costs $1.40 per million input tokens and $4.40 per million output tokens; MiniMax M2.5 costs $0.30 and $1.20. Per solved SWE-bench Verified task, GLM 5.2 costs $0.86 and MiniMax M2.5 $0.62.

Which has the bigger context window, GLM 5.2 or MiniMax M2.5?

GLM 5.2, with 1.05M tokens against 205K.

How many benchmarks is the GLM 5.2 vs MiniMax M2.5 comparison based on?

60 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 M2.5's on 3. Where only one model has a result, the page says so instead of naming a winner.

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