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GLM 4.6 vs MiniMax M1

GLM 4.6 leads MiniMax M1 on the Artificial Analysis Intelligence Index, 18.5 to 11.7, at about the same output price ($2.20 vs $2.20 per million). Updated October 22, 2026; 29 benchmarks have a result for both.

Replace GLM 4.6 Replace MiniMax M1
GLM 4.6Z.ai
Coding
8.8
Overall
33.4
Output $/M
$2.20
29
MiniMax M1MiniMax
Coding
No data
Overall
No data
Output $/M
$2.20
0
vs boards won of 29 shared

GLM 4.6 or MiniMax M1: which to pick

Only GLM 4.6 has a coding score, 8.8 on the Vibe Coding Index; MiniMax M1 has none, so the two cannot be ranked on coding.

Pick MiniMax M1 for a larger context window (1M against 205K tokens).

Model AGLM 4.6Z.ai8.8/1002 independent benchmarksNo cost per solved taskModel BMiniMax M1MiniMaxNot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
29
GLM 4.6 only
40
MiniMax M1 only
0
Like-for-like categories
6 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 M1 not benchmarked.
Widest category
General, 17.9 points apart on average over 17 shared benchmarks.
Widest single gap
LMArena Text: Japanese: GLM 4.6 1393 against MiniMax M1 1217, 29.3 points apart on a 0 to 100 scale.
Output price
Both cost $2.20 per million output tokens.
Context window
GLM 4.6 takes 205K tokens, MiniMax M1 1M.

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 M1Ties
Coding20
Intelligence10
Reasoning30
Math20
Knowledge40
General170
All shared boards290
CodingLike-for-likeSix of these boards feed the Vibe Coding Index20
Coding benchmarks for GLM 4.6 and MiniMax M1
BenchmarkGLM 4.6MiniMax M1Leader
SWE-bench Verified68.2%Not measuredOnly GLM 4.6 measured
LMArena WebDev1341Not measuredOnly GLM 4.6 measured
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%Not measuredOnly GLM 4.6 measured
Terminal-Bench Hard (Artificial Analysis run)28.8%Not measuredOnly GLM 4.6 measured
Terminal-Bench24.5%Not measuredOnly GLM 4.6 measured
ALE-Bench340.8Not measuredOnly GLM 4.6 measured
LMArena Text: coding14491359GLM 4.6
LMArena Text: software and IT14551359GLM 4.6
LMArena WebDev: HTML1350Not measuredOnly GLM 4.6 measured
Neither model has a result on the other 27 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for GLM 4.6 and MiniMax M1
BenchmarkGLM 4.6MiniMax M1Leader
AA Intelligence Index18.511.7GLM 4.6
Neither model has a result on the other 1 board here
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
Agentic work benchmarks for GLM 4.6 and MiniMax M1
BenchmarkGLM 4.6MiniMax M1Leader
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%Not measuredOnly GLM 4.6 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 34 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index30
Reasoning benchmarks for GLM 4.6 and MiniMax M1
BenchmarkGLM 4.6MiniMax M1Leader
GPQA Diamond74.5%Not measuredOnly GLM 4.6 measured
GPQA Diamond (Artificial Analysis run)78.0%Not measuredOnly GLM 4.6 measured
Humanity's Last Exam (Artificial Analysis run)14.5%Not measuredOnly GLM 4.6 measured
LMArena Text: hard prompts14401339GLM 4.6
LMArena Text: expert14321317GLM 4.6
LMArena Text: hard prompts in English14451353GLM 4.6
Kagi LLM Benchmark45.7%Not measuredOnly GLM 4.6 measured
Neither model has a result on the other 15 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index20
Math benchmarks for GLM 4.6 and MiniMax M1
BenchmarkGLM 4.6MiniMax M1Leader
AIME92.7%Not measuredOnly GLM 4.6 measured
MGSM (Vals AI)89.8%Not measuredOnly GLM 4.6 measured
AA Math Index86.0Not measuredOnly GLM 4.6 measured
AIME 2025 (Artificial Analysis run)86.0%Not measuredOnly GLM 4.6 measured
LMArena Text: math14321361GLM 4.6
LMArena Text: mathematical work14471363GLM 4.6
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
Knowledge benchmarks for GLM 4.6 and MiniMax M1
BenchmarkGLM 4.6MiniMax M1Leader
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%Not measuredOnly GLM 4.6 measured
LMArena Text: science14561354GLM 4.6
LMArena Text: medicine and healthcare14551355GLM 4.6
LMArena Text: legal and government14481337GLM 4.6
LMArena Text: business and finance14161319GLM 4.6
Neither model has a result on the other 8 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
Web design benchmarks for GLM 4.6 and MiniMax M1
BenchmarkGLM 4.6MiniMax M1Leader
Design Arena: websites1174Not measuredOnly GLM 4.6 measured
Design Arena: UI components1145Not measuredOnly GLM 4.6 measured
Design Arena: game dev1150Not measuredOnly GLM 4.6 measured
Design Arena: data viz1161Not measuredOnly GLM 4.6 measured
Design Arena: 3D1128Not measuredOnly GLM 4.6 measured
Design Arena: all code categories1165Not measuredOnly GLM 4.6 measured
Design Arena: SVG1108Not measuredOnly GLM 4.6 measured
Neither model has a result on the other 2 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index170
General benchmarks for GLM 4.6 and MiniMax M1
BenchmarkGLM 4.6MiniMax M1Leader
IFBench43.4%Not measuredOnly GLM 4.6 measured
Long context reasoning (AA-LCR)54.0%Not measuredOnly GLM 4.6 measured
LMArena Text14401343GLM 4.6
LMArena Text: instruction following14101312GLM 4.6
LMArena Text: creative writing14121297GLM 4.6
LMArena Text: English14491363GLM 4.6
LMArena Text: non-English14261319GLM 4.6
LMArena Text: Chinese14991360GLM 4.6
LMArena Text: German14471350GLM 4.6
LMArena Text: Japanese13931217GLM 4.6
LMArena Text: Korean14001266GLM 4.6
LMArena Text: French14591370GLM 4.6
LMArena Text: Spanish14361353GLM 4.6
LMArena Text: Russian14181329GLM 4.6
LMArena Text: Polish14471329GLM 4.6
LMArena Text: multi-turn14271335GLM 4.6
LMArena Text: longer queries14221326GLM 4.6
LMArena Text: writing and language14151311GLM 4.6
LMArena Text: entertainment and media14091303GLM 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 M1
BenchmarkGLM 4.6MiniMax M1Leader
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 M1$0.0015Fits in one request

MiniMax M1 costs less

Repository review

50K input + 3K output tokens

GLM 4.6$0.037Fits in one request
MiniMax M1$0.027Fits in one request

MiniMax M1 costs less

Agent loop

200K input + 10K output tokens

GLM 4.6$0.142Exceeds the context window
MiniMax M1$0.102Fits in one request

MiniMax M1 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 M1
SpecificationZ.ai GLM 4.6MiniMax M1
MakerZ.aiMiniMax
WeightsOpen weightsNo public weights listed
ReleasedSeptember 30, 2025June 17, 2025
Context window205K1M
Input $/Mtok$0.60$0.40
Output $/Mtok$2.20$2.20
Coding index8.8Not benchmarked
Independent measurements behind it20
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.6 better than MiniMax M1 for coding?

Only GLM 4.6 has a coding score, 8.8 on the Vibe Coding Index; MiniMax M1 has none, so the two cannot be ranked on coding. Pick MiniMax M1 for a larger context window (1M against 205K tokens).

Which is cheaper, GLM 4.6 or MiniMax M1?

GLM 4.6 costs $0.60 per million input tokens and $2.20 per million output tokens; MiniMax M1 costs $0.40 and $2.20. Cost per solved task is known for neither of them.

Which has the bigger context window, GLM 4.6 or MiniMax M1?

MiniMax M1, with 1M tokens against 205K.

How many benchmarks is the GLM 4.6 vs MiniMax M1 comparison based on?

29 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 M1's on 0. Where only one model has a result, the page says so instead of naming a winner.

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