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GLM 4.5 vs MiniMax M2.1

MiniMax M2.1 leads GLM 4.5 on the Artificial Analysis Intelligence Index, 20.9 to 12.8, and MiniMax M2.1 costs 1.8x less per output token ($1.20 vs $2.20 per million). Updated October 22, 2026; 29 benchmarks have a result for both.

Replace GLM 4.5 Replace MiniMax M2.1
GLM 4.5Z.ai
Coding
No data
Overall
No data
Output $/M
$2.20
8
MiniMax M2.1MiniMax
Coding
No data
Overall
No data
Output $/M
$1.20
21
vs boards won of 29 shared

GLM 4.5 or MiniMax M2.1: which to pick

Neither model has a coding score yet, so the two cannot be ranked on coding.

Pick GLM 4.5 for work measured by LiveCodeBench (Vals AI) or LiveCodeBench (Artificial Analysis run), where it leads.

Model AGLM 4.5Z.aiNot benchmarkedNo coding benchmarkNo cost per solved taskModel BMiniMax M2.1MiniMaxNot benchmarked1 independent benchmark$0.59 per solved task
Shared results
29
GLM 4.5 only
32
MiniMax M2.1 only
5
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 4.5 not benchmarked, MiniMax M2.1 not benchmarked.
Widest category
Agentic work, 42.4 points apart on average over 1 shared benchmark.
Widest single gap
τ²-Bench Telecom: GLM 4.5 43.0% against MiniMax M2.1 85.4%, 42.4 points apart on a 0 to 100 scale.
Output price
GLM 4.5 $2.20 against MiniMax M2.1 $1.20 per million output tokens, 1.8x apart.
Context window
GLM 4.5 takes 131K tokens, MiniMax M2.1 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 4.5BarsMiniMax M2.1Ties
Coding04
Intelligence01
Agentic work01
Reasoning03
Math32
Knowledge33
Web design25
General02
All shared boards821
CodingLike-for-likeSix of these boards feed the Vibe Coding Index04
Coding benchmarks for GLM 4.5 and MiniMax M2.1
BenchmarkGLM 4.5MiniMax M2.1Leader
SWE-bench Verified64.2%Not measuredOnly GLM 4.5 measured
LiveCodeBench (Vals AI)67.5%81.8%MiniMax M2.1
SWE-bench Verified (Vals AI)Not measured74.8%Only MiniMax M2.1 measured
LiveCodeBench (Artificial Analysis run)73.8%81.0%MiniMax M2.1
Terminal-Bench Hard (Artificial Analysis run)22.0%28.8%MiniMax M2.1
Terminal-BenchNot measured36.6%Only MiniMax M2.1 measured
WeirdML40.6%Not measuredOnly GLM 4.5 measured
ALE-Bench344.8623.8MiniMax M2.1
LMArena Text: coding1434Not measuredOnly GLM 4.5 measured
LMArena Text: software and IT1440Not measuredOnly GLM 4.5 measured
Neither model has a result on the other 29 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for GLM 4.5 and MiniMax M2.1
BenchmarkGLM 4.5MiniMax M2.1Leader
AA Intelligence Index12.820.9MiniMax M2.1
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 4.5 and MiniMax M2.1
BenchmarkGLM 4.5MiniMax M2.1Leader
τ²-Bench Telecom43.0%85.4%MiniMax M2.1
Neither model has a result on the other 40 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index03
Reasoning benchmarks for GLM 4.5 and MiniMax M2.1
BenchmarkGLM 4.5MiniMax M2.1Leader
GPQA Diamond72.2%78.5%MiniMax M2.1
GPQA Diamond (Artificial Analysis run)78.2%83.0%MiniMax M2.1
Humanity's Last Exam (Artificial Analysis run)13.0%23.2%MiniMax M2.1
LMArena Text: hard prompts1429Not measuredOnly GLM 4.5 measured
LMArena Text: expert1433Not measuredOnly GLM 4.5 measured
LMArena Text: hard prompts in English1429Not measuredOnly GLM 4.5 measured
Kagi LLM Benchmark57.9%Not measuredOnly GLM 4.5 measured
Neither model has a result on the other 15 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index32
Math benchmarks for GLM 4.5 and MiniMax M2.1
BenchmarkGLM 4.5MiniMax M2.1Leader
AIME86.7%77.9%GLM 4.5
MATH 500 (Vals AI)94.0%89.0%GLM 4.5
MGSM (Vals AI)90.8%87.8%GLM 4.5
AA Math Index73.782.7MiniMax M2.1
AIME 2025 (Artificial Analysis run)73.7%82.7%MiniMax M2.1
LMArena Text: math1427Not measuredOnly GLM 4.5 measured
LMArena Text: mathematical work1430Not measuredOnly GLM 4.5 measured
Neither model has a result on the other 6 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index33
Knowledge benchmarks for GLM 4.5 and MiniMax M2.1
BenchmarkGLM 4.5MiniMax M2.1Leader
MMLU Pro81.2%87.0%MiniMax M2.1
LegalBench (Vals AI)75.6%75.5%GLM 4.5
CaseLaw (Vals AI)Not measured55.8%Only MiniMax M2.1 measured
CorpFin (Vals AI)61.0%58.9%GLM 4.5
TaxEval (Vals AI)72.4%66.3%GLM 4.5
MedQA (Vals AI)90.0%91.2%MiniMax M2.1
MedCode (Vals AI)Not measured34.1%Only MiniMax M2.1 measured
MMLU Pro (Artificial Analysis run)83.5%87.5%MiniMax M2.1
LMArena Text: science1444Not measuredOnly GLM 4.5 measured
LMArena Text: medicine and healthcare1455Not measuredOnly GLM 4.5 measured
LMArena Text: legal and government1438Not measuredOnly GLM 4.5 measured
LMArena Text: business and finance1420Not measuredOnly GLM 4.5 measured
Neither model has a result on the other 6 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index25
Web design benchmarks for GLM 4.5 and MiniMax M2.1
BenchmarkGLM 4.5MiniMax M2.1Leader
Design Arena: websites11691201MiniMax M2.1
Design Arena: UI components11411220MiniMax M2.1
Design Arena: game dev11441134GLM 4.5
Design Arena: data viz11591206MiniMax M2.1
Design Arena: 3D11701164GLM 4.5
Design Arena: all code categories11651190MiniMax M2.1
Design Arena: SVG10961131MiniMax M2.1
Neither model has a result on the other 2 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index02
General benchmarks for GLM 4.5 and MiniMax M2.1
BenchmarkGLM 4.5MiniMax M2.1Leader
MedScribe (Vals AI)Not measured80.8%Only MiniMax M2.1 measured
IFBench44.1%69.9%MiniMax M2.1
Long context reasoning (AA-LCR)52.7%67.7%MiniMax M2.1
Creative writing (Lech Mazur)73.4%Not measuredOnly GLM 4.5 measured
LMArena Text1430Not measuredOnly GLM 4.5 measured
LMArena Text: instruction following1404Not measuredOnly GLM 4.5 measured
LMArena Text: creative writing1395Not measuredOnly GLM 4.5 measured
LMArena Text: English1437Not measuredOnly GLM 4.5 measured
LMArena Text: non-English1417Not measuredOnly GLM 4.5 measured
LMArena Text: Chinese1465Not measuredOnly GLM 4.5 measured
LMArena Text: German1407Not measuredOnly GLM 4.5 measured
LMArena Text: Japanese1415Not measuredOnly GLM 4.5 measured
LMArena Text: Korean1380Not measuredOnly GLM 4.5 measured
LMArena Text: French1418Not measuredOnly GLM 4.5 measured
LMArena Text: Spanish1454Not measuredOnly GLM 4.5 measured
LMArena Text: Russian1413Not measuredOnly GLM 4.5 measured
LMArena Text: Polish1422Not measuredOnly GLM 4.5 measured
LMArena Text: multi-turn1415Not measuredOnly GLM 4.5 measured
LMArena Text: longer queries1412Not measuredOnly GLM 4.5 measured
LMArena Text: writing and language1403Not measuredOnly GLM 4.5 measured
LMArena Text: entertainment and media1404Not measuredOnly GLM 4.5 measured
Neither model has a result on the other 25 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 4.5 and MiniMax M2.1
BenchmarkGLM 4.5MiniMax M2.1Leader
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 4.5$0.0017Fits in one request
MiniMax M2.1$0.0009Fits in one request

MiniMax M2.1 costs less

Repository review

50K input + 3K output tokens

GLM 4.5$0.037Fits in one request
MiniMax M2.1$0.019Fits in one request

MiniMax M2.1 costs less

Agent loop

200K input + 10K output tokens

GLM 4.5$0.142Exceeds the context window
MiniMax M2.1$0.072Exceeds the context window

MiniMax M2.1 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 4.5 and MiniMax M2.1
SpecificationZ.ai GLM 4.5MiniMax M2.1
MakerZ.aiMiniMax
WeightsOpen weightsOpen weights
ReleasedJuly 25, 2025December 23, 2025
Context window131K205K
Input $/Mtok$0.60$0.30
Output $/Mtok$2.20$1.20
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it01
Cost per solved taskNot computable$0.59
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.5 better than MiniMax M2.1 for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GLM 4.5 for work measured by LiveCodeBench (Vals AI) or LiveCodeBench (Artificial Analysis run), where it leads.

Which is cheaper, GLM 4.5 or MiniMax M2.1?

GLM 4.5 costs $0.60 per million input tokens and $2.20 per million output tokens; MiniMax M2.1 costs $0.30 and $1.20. Cost per solved task is known for MiniMax M2.1 only.

Which has the bigger context window, GLM 4.5 or MiniMax M2.1?

MiniMax M2.1, with 205K tokens against 131K.

How many benchmarks is the GLM 4.5 vs MiniMax M2.1 comparison based on?

29 benchmarks have a result for both models as of October 22, 2026. GLM 4.5's coding index rests on 0 independent benchmarks and MiniMax M2.1's on 1. Where only one model has a result, the page says so instead of naming a winner.

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