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GLM 4.5 vs MiniMax M2.7

Updated October 21, 2026. 51 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace GLM 4.5 Replace MiniMax M2.7
GLM 4.5Z.ai
Coding
64.2
Overall
12.8
Output $/M
$2.20
23
MiniMax M2.7MiniMax
Coding
28.4
Overall
22.8
Output $/M
$1.20
28
vs boards won of 51 shared

Decision reading

Pick GLM 4.5 for coding: it scores 64.2 on the Vibe Coding Index against 28.4 for MiniMax M2.7.

Pick MiniMax M2.7 when output tokens cost 1.8x less, or when the context window is larger, 205K against 131K tokens.

Model AGLM 4.5Z.ai64.2/1001 independent benchmarkNo cost per solved taskModel BMiniMax M2.7MiniMax28.4/1001 independent benchmark$0.64 per solved task
Shared results
51
GLM 4.5 only
9
MiniMax M2.7 only
23
Like-for-like categories
8 of 9

Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.

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 64.2 from 1 benchmark, MiniMax M2.7 28.4 from 1 benchmark.
Widest category
Agentic work, 41.8 points apart on average over 1 shared benchmark.
Widest single gap
τ²-Bench Telecom: GLM 4.5 43.0% against MiniMax M2.7 84.8%, 41.8 points apart on a 0 to 100 scale.
Output price
GLM 4.5 $2.20 against MiniMax M2.7 $1.20 per million output tokens, 1.8x apart.
Context window
GLM 4.5 takes 131K tokens, MiniMax M2.7 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 M2.7Ties
Coding15
Intelligence01
Agentic work01
Reasoning15
Math12
Knowledge62
Web design07
General145
All shared boards2328
CodingLike-for-likeFour of these boards feed the Vibe Coding Index15
Coding benchmarks for GLM 4.5 and MiniMax M2.7
BenchmarkGLM 4.5MiniMax M2.7Leader
SWE-bench Verified64.2%Not measuredOnly GLM 4.5 measured
LMArena WebDevNot measured1399Only MiniMax M2.7 measured
AA Coding IndexNot measured52.6Only MiniMax M2.7 measured
LiveCodeBench (Vals AI)67.5%79.9%MiniMax M2.7
SWE-bench Verified (Vals AI)Not measured73.8%Only MiniMax M2.7 measured
Vibe Code Bench (Vals AI)Not measured11.9%Only MiniMax M2.7 measured
Code migration (Vals AI)Not measured8.7%Only MiniMax M2.7 measured
ProgramBench (Vals AI)Not measured0.0%Only MiniMax M2.7 measured
Terminal-Bench 2.1 (Vals AI)Not measured48.7%Only MiniMax M2.7 measured
LiveCodeBench (Artificial Analysis run)73.8%Not measuredOnly GLM 4.5 measured
SciCodeNot measured50.1%Only MiniMax M2.7 measured
Terminal-Bench Hard (Artificial Analysis run)22.0%39.4%MiniMax M2.7
Terminal-BenchNot measured45.1%Only MiniMax M2.7 measured
WeirdML40.6%37.0%GLM 4.5
ALE-Bench344.8599.3MiniMax M2.7
LMArena Text: coding14341454MiniMax M2.7
LMArena Text: software and IT14401446MiniMax M2.7
LMArena WebDev: ReactNot measured1388Only MiniMax M2.7 measured
LMArena WebDev: HTMLNot measured1401Only MiniMax M2.7 measured
Neither model has a result on the other 19 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for GLM 4.5 and MiniMax M2.7
BenchmarkGLM 4.5MiniMax M2.7Leader
AA Intelligence Index12.822.8MiniMax M2.7
Epoch Capabilities IndexNot measured145.8Only MiniMax M2.7 measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for GLM 4.5 and MiniMax M2.7
BenchmarkGLM 4.5MiniMax M2.7Leader
AA Agentic IndexNot measured15.3Only MiniMax M2.7 measured
Finance Agent (Vals AI)Not measured27.9%Only MiniMax M2.7 measured
Tax Agent Bench (Vals AI)Not measured6.9%Only MiniMax M2.7 measured
Legal Research Bench (Vals AI)Not measured10.6%Only MiniMax M2.7 measured
Excel Modeling Benchmark (Vals AI)Not measured28.4%Only MiniMax M2.7 measured
τ²-Bench Telecom43.0%84.8%MiniMax M2.7
Neither model has a result on the other 33 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index15
Reasoning benchmarks for GLM 4.5 and MiniMax M2.7
BenchmarkGLM 4.5MiniMax M2.7Leader
GPQA Diamond72.2%86.6%MiniMax M2.7
GPQA Diamond (Artificial Analysis run)78.2%87.4%MiniMax M2.7
Humanity's Last Exam (Artificial Analysis run)13.0%29.6%MiniMax M2.7
LMArena Text: hard prompts14291422GLM 4.5
LMArena Text: expert14331443MiniMax M2.7
LMArena Text: hard prompts in English14291441MiniMax M2.7
Neither model has a result on the other 15 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index12
Math benchmarks for GLM 4.5 and MiniMax M2.7
BenchmarkGLM 4.5MiniMax M2.7Leader
AIME86.7%91.0%MiniMax M2.7
MATH 500 (Vals AI)94.0%Not measuredOnly GLM 4.5 measured
MGSM (Vals AI)90.8%Not measuredOnly GLM 4.5 measured
AA Math Index73.7Not measuredOnly GLM 4.5 measured
AIME 2025 (Artificial Analysis run)73.7%Not measuredOnly GLM 4.5 measured
ProofBenchNot measured3.0%Only MiniMax M2.7 measured
LMArena Text: math14271421GLM 4.5
LMArena Text: mathematical work14301437MiniMax M2.7
Neither model has a result on the other 5 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index62
Knowledge benchmarks for GLM 4.5 and MiniMax M2.7
BenchmarkGLM 4.5MiniMax M2.7Leader
MMLU Pro81.2%80.4%GLM 4.5
LegalBench (Vals AI)75.6%84.0%MiniMax M2.7
CaseLaw (Vals AI)Not measured60.9%Only MiniMax M2.7 measured
CorpFin (Vals AI)61.0%61.2%MiniMax M2.7
TaxEval (Vals AI)72.4%66.6%GLM 4.5
MedQA (Vals AI)90.0%Not measuredOnly GLM 4.5 measured
MedCode (Vals AI)Not measured34.4%Only MiniMax M2.7 measured
MMLU Pro (Artificial Analysis run)83.5%Not measuredOnly GLM 4.5 measured
LMArena Text: science14441416GLM 4.5
LMArena Text: medicine and healthcare14551409GLM 4.5
LMArena Text: legal and government14381403GLM 4.5
LMArena Text: business and finance14201407GLM 4.5
Neither model has a result on the other 5 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index07
Web design benchmarks for GLM 4.5 and MiniMax M2.7
BenchmarkGLM 4.5MiniMax M2.7Leader
Design Arena: websites11701243MiniMax M2.7
Design Arena: UI components11441210MiniMax M2.7
Design Arena: game dev11451210MiniMax M2.7
Design Arena: data viz11591233MiniMax M2.7
Design Arena: 3D11701195MiniMax M2.7
Design Arena: all code categories11651233MiniMax M2.7
Design Arena: SVG10961136MiniMax M2.7
Design Arena: ASCII artNot measured1136Only MiniMax M2.7 measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index145
General benchmarks for GLM 4.5 and MiniMax M2.7
BenchmarkGLM 4.5MiniMax M2.7Leader
MedScribe (Vals AI)Not measured79.9%Only MiniMax M2.7 measured
IFBench44.1%75.7%MiniMax M2.7
Long context reasoning (AA-LCR)52.7%78.3%MiniMax M2.7
Creative writing (Lech Mazur)73.4%Not measuredOnly GLM 4.5 measured
ExploitBenchNot measured13.3%Only MiniMax M2.7 measured
LMArena Text14301405GLM 4.5
LMArena Text: instruction following14041405MiniMax M2.7
LMArena Text: creative writing13951354GLM 4.5
LMArena Text: English14371426GLM 4.5
LMArena Text: non-English14171382GLM 4.5
LMArena Text: Chinese14651442GLM 4.5
LMArena Text: German14071398GLM 4.5
LMArena Text: Japanese14151260GLM 4.5
LMArena Text: Korean13801314GLM 4.5
LMArena Text: French14181424MiniMax M2.7
LMArena Text: Spanish14541402GLM 4.5
LMArena Text: Russian14131383GLM 4.5
LMArena Text: Polish14221387GLM 4.5
LMArena Text: multi-turn14151412GLM 4.5
LMArena Text: longer queries14121418MiniMax M2.7
LMArena Text: writing and language14031376GLM 4.5
LMArena Text: entertainment and media14041360GLM 4.5
Neither model has a result on the other 24 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 M2.7
BenchmarkGLM 4.5MiniMax M2.7Leader
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 M2.7$0.0009Fits in one request

MiniMax M2.7 costs less

Repository review

50K input + 3K output tokens

GLM 4.5$0.037Fits in one request
MiniMax M2.7$0.019Fits in one request

MiniMax M2.7 costs less

Agent loop

200K input + 10K output tokens

GLM 4.5$0.142Exceeds the context window
MiniMax M2.7$0.072Exceeds the context window

MiniMax M2.7 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 M2.7
SpecificationZ.ai GLM 4.5MiniMax M2.7
MakerZ.aiMiniMax
WeightsOpen weightsOpen weights
ReleasedJuly 25, 2025March 18, 2026
Context window131K205K
Input $/Mtok$0.60$0.30
Output $/Mtok$2.20$1.20
Coding index64.228.4
Independent measurements behind it11
Cost per solved taskNot computable$0.64
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.5 better than MiniMax M2.7 for coding?

Pick GLM 4.5 for coding: it scores 64.2 on the Vibe Coding Index against 28.4 for MiniMax M2.7. Pick MiniMax M2.7 when output tokens cost 1.8x less, or when the context window is larger, 205K against 131K tokens.

Which is cheaper, GLM 4.5 or MiniMax M2.7?

GLM 4.5 costs $0.60 per million input tokens and $2.20 per million output tokens; MiniMax M2.7 costs $0.30 and $1.20. Cost per solved task is known for MiniMax M2.7 only.

Which has the bigger context window, GLM 4.5 or MiniMax M2.7?

MiniMax M2.7, with 205K tokens against 131K.

How many benchmarks is the GLM 4.5 vs MiniMax M2.7 comparison based on?

51 benchmarks have a result for both models as of October 21, 2026. GLM 4.5's coding index rests on 1 independent benchmark and MiniMax M2.7's on 1. Where only one model has a result, the page says so instead of naming a winner.

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