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GLM 4.7 vs MiniMax M2.5

Updated October 21, 2026. 62 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace GLM 4.7 Replace MiniMax M2.5
GLM 4.7Z.ai
Coding
33.6
Overall
22.2
Output $/M
$1.98
51
MiniMax M2.5MiniMax
Coding
59.4
Overall
22.8
Output $/M
$0.95
11
vs boards won of 62 shared

Decision reading

Pick MiniMax M2.5 for coding: it scores 59.4 on the Vibe Coding Index against 33.6 for GLM 4.7.

Pick GLM 4.7 when a solved task costs $0.29 against $0.62, or when what matters is LMArena WebDev or LiveCodeBench (Vals AI), where it leads.

Model AGLM 4.7Z.ai33.6/1001 independent benchmark$0.29 per solved taskModel BMiniMax M2.5MiniMax59.4/1002 independent benchmarks$0.62 per solved task
Shared results
62
GLM 4.7 only
18
MiniMax M2.5 only
5
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 4.7 33.6 from 1 benchmark, MiniMax M2.5 59.4 from 2 benchmarks.
Widest category
General, 13.1 points apart on average over 21 shared benchmarks.
Widest single gap
LMArena Text: Japanese: GLM 4.7 1439 against MiniMax M2.5 1171, 44.6 points apart on a 0 to 100 scale.
Output price
GLM 4.7 $1.98 against MiniMax M2.5 $0.95 per million output tokens, 2.1x 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 4.7BarsMiniMax M2.5Ties
Coding64
Intelligence02
Agentic work20
Reasoning51
Math40
Knowledge91
Web design61
General192
All shared boards5111
CodingLike-for-likeFour of these boards feed the Vibe Coding Index64
Coding benchmarks for GLM 4.7 and MiniMax M2.5
BenchmarkGLM 4.7MiniMax M2.5Leader
SWE-bench VerifiedNot measured75.8%Only MiniMax M2.5 measured
SWE-bench MultilingualNot measured68.3%Only MiniMax M2.5 measured
LMArena WebDev14351386GLM 4.7
AA Coding Index45.3Not measuredOnly GLM 4.7 measured
LiveCodeBench (Vals AI)82.2%79.2%GLM 4.7
SWE-bench Verified (Vals AI)69.4%74.2%MiniMax M2.5
Vibe Code Bench (Vals AI)Not measured14.8%Only MiniMax M2.5 measured
LiveCodeBench (Artificial Analysis run)89.4%Not measuredOnly GLM 4.7 measured
Terminal-Bench Hard (Artificial Analysis run)31.8%34.8%MiniMax M2.5
Terminal-Bench33.4%42.7%MiniMax M2.5
ALE-Bench399.5618.2MiniMax M2.5
LMArena Text: coding14541380GLM 4.7
LMArena Text: software and IT14531383GLM 4.7
LMArena WebDev: React14201374GLM 4.7
LMArena WebDev: HTML14471414GLM 4.7
Neither model has a result on the other 23 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for GLM 4.7 and MiniMax M2.5
BenchmarkGLM 4.7MiniMax M2.5Leader
AA Intelligence Index22.222.8MiniMax M2.5
Epoch Capabilities Index143.6146.6MiniMax M2.5
Agentic workLike-for-likePublic boards, shown as published, kept out of the index20
Agentic work benchmarks for GLM 4.7 and MiniMax M2.5
BenchmarkGLM 4.7MiniMax M2.5Leader
Design Arena: agents building full-stack apps1050Not measuredOnly GLM 4.7 measured
Design Arena: agents building mobile apps1105Not measuredOnly GLM 4.7 measured
Design Arena: agents building Android apps1118Not measuredOnly GLM 4.7 measured
Design Arena: agents building Godot games1058Not measuredOnly GLM 4.7 measured
τ²-Bench Telecom95.9%95.3%GLM 4.7
Vending-Bench 2$2,377-$23GLM 4.7
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 4.7 and MiniMax M2.5
BenchmarkGLM 4.7MiniMax M2.5Leader
GPQA Diamond80.0%82.1%MiniMax M2.5
GPQA Diamond (Artificial Analysis run)85.9%84.8%GLM 4.7
Humanity's Last Exam (Artificial Analysis run)27.4%20.5%GLM 4.7
GPQA Diamond (Epoch AI run)83.3%Not measuredOnly GLM 4.7 measured
ARC-AGINot measured63.7%Only MiniMax M2.5 measured
ARC-AGI-2Not measured4.9%Only MiniMax M2.5 measured
SimpleBench47.7%Not measuredOnly GLM 4.7 measured
Chess puzzles21.0%Not measuredOnly GLM 4.7 measured
LMArena Text: hard prompts14431372GLM 4.7
LMArena Text: expert14241378GLM 4.7
LMArena Text: hard prompts in English14521384GLM 4.7
Neither model has a result on the other 10 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index40
Math benchmarks for GLM 4.7 and MiniMax M2.5
BenchmarkGLM 4.7MiniMax M2.5Leader
AIME93.3%88.8%GLM 4.7
MGSM (Vals AI)88.2%Not measuredOnly GLM 4.7 measured
AA Math Index95.0Not measuredOnly GLM 4.7 measured
AIME 2025 (Artificial Analysis run)95.0%Not measuredOnly GLM 4.7 measured
OTIS Mock AIME83.3%Not measuredOnly GLM 4.7 measured
ProofBench6.0%4.0%GLM 4.7
LMArena Text: math14241378GLM 4.7
LMArena Text: mathematical work14111378GLM 4.7
Neither model has a result on the other 5 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index91
Knowledge benchmarks for GLM 4.7 and MiniMax M2.5
BenchmarkGLM 4.7MiniMax M2.5Leader
MMLU Pro82.7%80.1%GLM 4.7
LegalBench (Vals AI)83.4%80.0%GLM 4.7
CaseLaw (Vals AI)54.9%53.5%GLM 4.7
CorpFin (Vals AI)46.4%59.6%MiniMax M2.5
TaxEval (Vals AI)68.8%68.2%GLM 4.7
MedQA (Vals AI)93.7%92.5%GLM 4.7
MedCode (Vals AI)32.8%Not measuredOnly GLM 4.7 measured
MMLU Pro (Artificial Analysis run)85.6%Not measuredOnly GLM 4.7 measured
SimpleQA Verified32.2%Not measuredOnly GLM 4.7 measured
LMArena Text: science14571369GLM 4.7
LMArena Text: medicine and healthcare14691357GLM 4.7
LMArena Text: legal and government14441366GLM 4.7
LMArena Text: business and finance14291356GLM 4.7
Neither model has a result on the other 4 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index61
Web design benchmarks for GLM 4.7 and MiniMax M2.5
BenchmarkGLM 4.7MiniMax M2.5Leader
Design Arena: websites12261222GLM 4.7
Design Arena: UI components11991172GLM 4.7
Design Arena: game dev11891176GLM 4.7
Design Arena: data viz11951171GLM 4.7
Design Arena: 3D11911170GLM 4.7
Design Arena: all code categories12171207GLM 4.7
Design Arena: SVG11421147MiniMax M2.5
Design Arena: ASCII art1165Not measuredOnly GLM 4.7 measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index192
General benchmarks for GLM 4.7 and MiniMax M2.5
BenchmarkGLM 4.7MiniMax M2.5Leader
MedScribe (Vals AI)68.6%Not measuredOnly GLM 4.7 measured
IFBench67.9%71.6%MiniMax M2.5
Long context reasoning (AA-LCR)71.0%73.3%MiniMax M2.5
CL-bench15.9%11.4%GLM 4.7
CL-bench Life10.9%6.3%GLM 4.7
LMArena Text14351359GLM 4.7
LMArena Text: instruction following14121353GLM 4.7
LMArena Text: creative writing14011331GLM 4.7
LMArena Text: English14531380GLM 4.7
LMArena Text: non-English14171338GLM 4.7
LMArena Text: Chinese14951393GLM 4.7
LMArena Text: German14261361GLM 4.7
LMArena Text: Japanese14391171GLM 4.7
LMArena Text: Korean13991233GLM 4.7
LMArena Text: French14321363GLM 4.7
LMArena Text: Spanish14341353GLM 4.7
LMArena Text: Russian14231359GLM 4.7
LMArena Text: Polish14351334GLM 4.7
LMArena Text: multi-turn14461364GLM 4.7
LMArena Text: longer queries14321366GLM 4.7
LMArena Text: writing and language14111345GLM 4.7
LMArena Text: entertainment and media14001329GLM 4.7
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 4.7 and MiniMax M2.5
BenchmarkGLM 4.7MiniMax 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 4.7$0.0015Fits in one request
MiniMax M2.5$0.0007Fits in one request

MiniMax M2.5 costs less

Repository review

50K input + 3K output tokens

GLM 4.7$0.033Fits in one request
MiniMax M2.5$0.016Fits in one request

MiniMax M2.5 costs less

Agent loop

200K input + 10K output tokens

GLM 4.7$0.128Exceeds the context window
MiniMax M2.5$0.064Exceeds 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 4.7 and MiniMax M2.5
SpecificationZ.ai GLM 4.7MiniMax M2.5
MakerZ.aiMiniMax
WeightsOpen weightsOpen weights
ReleasedDecember 22, 2025February 12, 2026
Context window205K205K
Input $/Mtok$0.54$0.27
Output $/Mtok$1.98$0.95
Coding index33.659.4
Independent measurements behind it12
Cost per solved task$0.29$0.62
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.7 better than MiniMax M2.5 for coding?

Pick MiniMax M2.5 for coding: it scores 59.4 on the Vibe Coding Index against 33.6 for GLM 4.7. Pick GLM 4.7 when a solved task costs $0.29 against $0.62, or when what matters is LMArena WebDev or LiveCodeBench (Vals AI), where it leads.

Which is cheaper, GLM 4.7 or MiniMax M2.5?

GLM 4.7 costs $0.54 per million input tokens and $1.98 per million output tokens; MiniMax M2.5 costs $0.27 and $0.95. Per solved SWE-bench Verified task, GLM 4.7 costs $0.29 and MiniMax M2.5 $0.62.

Which has the bigger context window, GLM 4.7 or MiniMax M2.5?

Neither: both take 205K tokens.

How many benchmarks is the GLM 4.7 vs MiniMax M2.5 comparison based on?

62 benchmarks have a result for both models as of October 21, 2026. GLM 4.7's coding index rests on 1 independent benchmark and MiniMax M2.5's on 2. Where only one model has a result, the page says so instead of naming a winner.

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