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GLM 4.6 vs MiniMax M2

MiniMax M2 leads GLM 4.6 on the Artificial Analysis Intelligence Index, 18.6 to 18.5, and MiniMax M2 costs 2.2x less per output token ($1.02 vs $2.20 per million). Updated October 22, 2026; 49 benchmarks have a result for both.

Replace GLM 4.6 Replace MiniMax M2
GLM 4.6Z.ai
Coding
8.8
Overall
33.4
Output $/M
$2.20
42
MiniMax M2MiniMax
Coding
No data
Overall
No data
Output $/M
$1.02
7
vs boards won of 49 shared

GLM 4.6 or MiniMax M2: which to pick

Only GLM 4.6 has a coding score, 8.8 on the Vibe Coding Index; MiniMax M2 has none, so the two cannot be ranked on coding.

Pick MiniMax M2 for output tokens 2.2x cheaper ($1.02 vs $2.20 per million) and work measured by LiveCodeBench (Artificial Analysis run) or Terminal-Bench, where it leads.

Model AGLM 4.6Z.ai8.8/1002 independent benchmarksNo cost per solved taskModel BMiniMax M2MiniMaxNot benchmarked1 independent benchmark$0.702 per solved task
Shared results
49
GLM 4.6 only
20
MiniMax M2 only
1
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 4.6 8.8 from 2 benchmarks, MiniMax M2 not benchmarked.
Widest category
General, 17.7 points apart on average over 17 shared benchmarks.
Widest single gap
IFBench: GLM 4.6 43.4% against MiniMax M2 72.3%, 28.9 points apart on a 0 to 100 scale.
Output price
GLM 4.6 $2.20 against MiniMax M2 $1.02 per million output tokens, 2.2x apart.
Context window
Both take 205K 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 4.6BarsMiniMax M2Ties
Coding62
Intelligence01
Agentic work01
Reasoning51
Math40
Knowledge50
Web design70
General152
All shared boards427
CodingLike-for-likeSix of these boards feed the Vibe Coding Index62
Coding benchmarks for GLM 4.6 and MiniMax M2
BenchmarkGLM 4.6MiniMax M2Leader
SWE-bench Verified68.2%61.0%GLM 4.6
LMArena WebDev13411297GLM 4.6
AA Coding Index45.8Not measuredOnly GLM 4.6 measured
LiveCodeBench (Vals AI)81.0%Not measuredOnly GLM 4.6 measured
Vibe Code Bench (Vals AI)3.1%Not measuredOnly GLM 4.6 measured
LiveCodeBench (Artificial Analysis run)69.5%82.6%MiniMax M2
Terminal-Bench Hard (Artificial Analysis run)28.8%25.8%GLM 4.6
Terminal-Bench24.5%30.0%MiniMax M2
ALE-Bench340.8Not measuredOnly GLM 4.6 measured
LMArena Text: coding14491371GLM 4.6
LMArena Text: software and IT14551373GLM 4.6
LMArena WebDev: HTML13501306GLM 4.6
Neither model has a result on the other 27 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for GLM 4.6 and MiniMax M2
BenchmarkGLM 4.6MiniMax M2Leader
AA Intelligence Index18.518.6MiniMax M2
Neither model has a result on the other 1 board here
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for GLM 4.6 and MiniMax M2
BenchmarkGLM 4.6MiniMax M2Leader
Design Arena: agents building full-stack apps1029Not measuredOnly GLM 4.6 measured
Design Arena: agents building mobile apps1096Not measuredOnly GLM 4.6 measured
Design Arena: agents building Android apps1084Not measuredOnly GLM 4.6 measured
Design Arena: agents building Godot games1180Not measuredOnly GLM 4.6 measured
τ²-Bench Telecom76.9%86.8%MiniMax M2
Vending-Bench 2Not measured$161Only MiniMax M2 measured
Berkeley Function Calling Leaderboard (BFCL)72.4%Not measuredOnly GLM 4.6 measured
BrowseComp-Plus48.2%Not measuredOnly GLM 4.6 measured
Neither model has a result on the other 33 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index51
Reasoning benchmarks for GLM 4.6 and MiniMax M2
BenchmarkGLM 4.6MiniMax M2Leader
GPQA Diamond74.5%Not measuredOnly GLM 4.6 measured
GPQA Diamond (Artificial Analysis run)78.0%77.7%GLM 4.6
Humanity's Last Exam (Artificial Analysis run)14.5%13.7%GLM 4.6
LMArena Text: hard prompts14401357GLM 4.6
LMArena Text: expert14321338GLM 4.6
LMArena Text: hard prompts in English14451376GLM 4.6
Kagi LLM Benchmark45.7%57.8%MiniMax M2
Neither model has a result on the other 15 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index40
Math benchmarks for GLM 4.6 and MiniMax M2
BenchmarkGLM 4.6MiniMax M2Leader
AIME92.7%Not measuredOnly GLM 4.6 measured
MGSM (Vals AI)89.8%Not measuredOnly GLM 4.6 measured
AA Math Index86.078.3GLM 4.6
AIME 2025 (Artificial Analysis run)86.0%78.3%GLM 4.6
LMArena Text: math14321351GLM 4.6
LMArena Text: mathematical work14471368GLM 4.6
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index50
Knowledge benchmarks for GLM 4.6 and MiniMax M2
BenchmarkGLM 4.6MiniMax M2Leader
MMLU Pro82.2%Not measuredOnly GLM 4.6 measured
LegalBench (Vals AI)79.6%Not measuredOnly GLM 4.6 measured
CorpFin (Vals AI)56.8%Not measuredOnly GLM 4.6 measured
TaxEval (Vals AI)66.2%Not measuredOnly GLM 4.6 measured
MedQA (Vals AI)92.2%Not measuredOnly GLM 4.6 measured
MMLU Pro (Artificial Analysis run)82.9%82.0%GLM 4.6
LMArena Text: science14561348GLM 4.6
LMArena Text: medicine and healthcare14551367GLM 4.6
LMArena Text: legal and government14481349GLM 4.6
LMArena Text: business and finance14161336GLM 4.6
Neither model has a result on the other 8 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index70
Web design benchmarks for GLM 4.6 and MiniMax M2
BenchmarkGLM 4.6MiniMax M2Leader
Design Arena: websites11741143GLM 4.6
Design Arena: UI components11451137GLM 4.6
Design Arena: game dev11501118GLM 4.6
Design Arena: data viz11611140GLM 4.6
Design Arena: 3D11281098GLM 4.6
Design Arena: all code categories11651135GLM 4.6
Design Arena: SVG11081100GLM 4.6
Neither model has a result on the other 2 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index152
General benchmarks for GLM 4.6 and MiniMax M2
BenchmarkGLM 4.6MiniMax M2Leader
IFBench43.4%72.3%MiniMax M2
Long context reasoning (AA-LCR)54.0%64.3%MiniMax M2
LMArena Text14401340GLM 4.6
LMArena Text: instruction following14101329GLM 4.6
LMArena Text: creative writing14121286GLM 4.6
LMArena Text: English14491364GLM 4.6
LMArena Text: non-English14261313GLM 4.6
LMArena Text: Chinese14991366GLM 4.6
LMArena Text: German14471357GLM 4.6
LMArena Text: Japanese1393Not measuredOnly GLM 4.6 measured
LMArena Text: Korean1400Not measuredOnly GLM 4.6 measured
LMArena Text: French14591336GLM 4.6
LMArena Text: Spanish14361326GLM 4.6
LMArena Text: Russian14181330GLM 4.6
LMArena Text: Polish14471312GLM 4.6
LMArena Text: multi-turn14271361GLM 4.6
LMArena Text: longer queries14221331GLM 4.6
LMArena Text: writing and language14151295GLM 4.6
LMArena Text: entertainment and media14091298GLM 4.6
Neither model has a result on the other 27 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 4.6 and MiniMax M2
BenchmarkGLM 4.6MiniMax M2Leader
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 4.6$0.0017Fits in one request
MiniMax M2$0.0008Fits in one request

MiniMax M2 costs less

Repository review

50K input + 3K output tokens

GLM 4.6$0.037Fits in one request
MiniMax M2$0.016Fits in one request

MiniMax M2 costs less

Agent loop

200K input + 10K output tokens

GLM 4.6$0.142Exceeds the context window
MiniMax M2$0.061Exceeds the context window

MiniMax M2 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 4.6 and MiniMax M2
SpecificationZ.ai GLM 4.6MiniMax M2
MakerZ.aiMiniMax
WeightsOpen weightsOpen weights
ReleasedSeptember 30, 2025October 23, 2025
Context window205K205K
Input $/Mtok$0.60$0.255
Output $/Mtok$2.20$1.02
Coding index8.8Not benchmarked
Independent measurements behind it21
Cost per solved taskNot computable$0.702
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.6 better than MiniMax M2 for coding?

Only GLM 4.6 has a coding score, 8.8 on the Vibe Coding Index; MiniMax M2 has none, so the two cannot be ranked on coding. Pick MiniMax M2 for output tokens 2.2x cheaper ($1.02 vs $2.20 per million) and work measured by LiveCodeBench (Artificial Analysis run) or Terminal-Bench, where it leads.

Which is cheaper, GLM 4.6 or MiniMax M2?

GLM 4.6 costs $0.60 per million input tokens and $2.20 per million output tokens; MiniMax M2 costs $0.255 and $1.02. Cost per solved task is known for MiniMax M2 only.

Which has the bigger context window, GLM 4.6 or MiniMax M2?

Neither: both take 205K tokens.

How many benchmarks is the GLM 4.6 vs MiniMax M2 comparison based on?

49 benchmarks have a result for both models as of October 22, 2026. GLM 4.6's coding index rests on 2 independent benchmarks and MiniMax M2's on 1. Where only one model has a result, the page says so instead of naming a winner.

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