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GLM 4.6 vs MiniMax M2.5

Updated October 21, 2026. 56 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace GLM 4.6 Replace MiniMax M2.5
GLM 4.6Z.ai
Coding
52.2
Overall
18.5
Output $/M
$2.20
31
MiniMax M2.5MiniMax
Coding
59.4
Overall
22.8
Output $/M
$0.95
25
vs boards won of 56 shared

Decision reading

Pick MiniMax M2.5 for coding: it scores 59.4 on the Vibe Coding Index against 52.2 for GLM 4.6.

Pick GLM 4.6 when what matters is LiveCodeBench (Vals AI) or LMArena Text: coding, where it leads.

Model AGLM 4.6Z.ai52.2/1002 independent benchmarksNo cost per solved taskModel BMiniMax M2.5MiniMax59.4/1002 independent benchmarks$0.62 per solved task
Shared results
56
GLM 4.6 only
10
MiniMax M2.5 only
11
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 4.6 52.2 from 2 benchmarks, MiniMax M2.5 59.4 from 2 benchmarks.
Widest category
Agentic work, 18.4 points apart on average over 1 shared benchmark.
Widest single gap
LMArena Text: Japanese: GLM 4.6 1393 against MiniMax M2.5 1171, 37.0 points apart on a 0 to 100 scale.
Output price
GLM 4.6 $2.20 against MiniMax M2.5 $0.95 per million output tokens, 2.3x 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 M2.5Ties
Coding37
Intelligence01
Agentic work01
Reasoning33
Math30
Knowledge54
Web design07
General172
All shared boards3125
CodingLike-for-likeFour of these boards feed the Vibe Coding Index37
Coding benchmarks for GLM 4.6 and MiniMax M2.5
BenchmarkGLM 4.6MiniMax M2.5Leader
SWE-bench Verified68.2%75.8%MiniMax M2.5
SWE-bench MultilingualNot measured68.3%Only MiniMax M2.5 measured
LMArena WebDev13411386MiniMax M2.5
AA Coding Index45.8Not measuredOnly GLM 4.6 measured
LiveCodeBench (Vals AI)81.0%79.2%GLM 4.6
SWE-bench Verified (Vals AI)Not measured74.2%Only MiniMax M2.5 measured
Vibe Code Bench (Vals AI)3.1%14.8%MiniMax M2.5
LiveCodeBench (Artificial Analysis run)69.5%Not measuredOnly GLM 4.6 measured
Terminal-Bench Hard (Artificial Analysis run)28.8%34.8%MiniMax M2.5
Terminal-Bench24.5%42.7%MiniMax M2.5
ALE-Bench340.8618.2MiniMax M2.5
LMArena Text: coding14491380GLM 4.6
LMArena Text: software and IT14551383GLM 4.6
LMArena WebDev: ReactNot measured1374Only MiniMax M2.5 measured
LMArena WebDev: HTML13501414MiniMax M2.5
Neither model has a result on the other 23 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for GLM 4.6 and MiniMax M2.5
BenchmarkGLM 4.6MiniMax M2.5Leader
AA Intelligence Index18.522.8MiniMax M2.5
Epoch Capabilities IndexNot measured146.6Only MiniMax M2.5 measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for GLM 4.6 and MiniMax M2.5
BenchmarkGLM 4.6MiniMax M2.5Leader
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 apps1090Not measuredOnly GLM 4.6 measured
Design Arena: agents building Godot games1180Not measuredOnly GLM 4.6 measured
τ²-Bench Telecom76.9%95.3%MiniMax M2.5
Vending-Bench 2Not measured-$23Only MiniMax M2.5 measured
Neither model has a result on the other 33 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index33
Reasoning benchmarks for GLM 4.6 and MiniMax M2.5
BenchmarkGLM 4.6MiniMax M2.5Leader
GPQA Diamond74.5%82.1%MiniMax M2.5
GPQA Diamond (Artificial Analysis run)78.0%84.8%MiniMax M2.5
Humanity's Last Exam (Artificial Analysis run)14.5%20.5%MiniMax M2.5
ARC-AGINot measured63.7%Only MiniMax M2.5 measured
ARC-AGI-2Not measured4.9%Only MiniMax M2.5 measured
LMArena Text: hard prompts14401372GLM 4.6
LMArena Text: expert14321378GLM 4.6
LMArena Text: hard prompts in English14451384GLM 4.6
Neither model has a result on the other 13 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index30
Math benchmarks for GLM 4.6 and MiniMax M2.5
BenchmarkGLM 4.6MiniMax M2.5Leader
AIME92.7%88.8%GLM 4.6
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
ProofBenchNot measured4.0%Only MiniMax M2.5 measured
LMArena Text: math14321378GLM 4.6
LMArena Text: mathematical work14471378GLM 4.6
Neither model has a result on the other 6 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index54
Knowledge benchmarks for GLM 4.6 and MiniMax M2.5
BenchmarkGLM 4.6MiniMax M2.5Leader
MMLU Pro82.2%80.1%GLM 4.6
LegalBench (Vals AI)79.6%80.0%MiniMax M2.5
CaseLaw (Vals AI)Not measured53.5%Only MiniMax M2.5 measured
CorpFin (Vals AI)56.8%59.6%MiniMax M2.5
TaxEval (Vals AI)66.2%68.2%MiniMax M2.5
MedQA (Vals AI)92.2%92.5%MiniMax M2.5
MMLU Pro (Artificial Analysis run)82.9%Not measuredOnly GLM 4.6 measured
LMArena Text: science14561369GLM 4.6
LMArena Text: medicine and healthcare14551357GLM 4.6
LMArena Text: legal and government14481366GLM 4.6
LMArena Text: business and finance14161356GLM 4.6
Neither model has a result on the other 6 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index07
Web design benchmarks for GLM 4.6 and MiniMax M2.5
BenchmarkGLM 4.6MiniMax M2.5Leader
Design Arena: websites11741222MiniMax M2.5
Design Arena: UI components11471172MiniMax M2.5
Design Arena: game dev11511176MiniMax M2.5
Design Arena: data viz11611171MiniMax M2.5
Design Arena: 3D11291170MiniMax M2.5
Design Arena: all code categories11661207MiniMax M2.5
Design Arena: SVG11081147MiniMax M2.5
Neither model has a result on the other 2 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index172
General benchmarks for GLM 4.6 and MiniMax M2.5
BenchmarkGLM 4.6MiniMax M2.5Leader
IFBench43.4%71.6%MiniMax M2.5
Long context reasoning (AA-LCR)54.0%73.3%MiniMax M2.5
CL-benchNot measured11.4%Only MiniMax M2.5 measured
CL-bench LifeNot measured6.3%Only MiniMax M2.5 measured
LMArena Text14401359GLM 4.6
LMArena Text: instruction following14101353GLM 4.6
LMArena Text: creative writing14121331GLM 4.6
LMArena Text: English14491380GLM 4.6
LMArena Text: non-English14261338GLM 4.6
LMArena Text: Chinese14991393GLM 4.6
LMArena Text: German14471361GLM 4.6
LMArena Text: Japanese13931171GLM 4.6
LMArena Text: Korean14001233GLM 4.6
LMArena Text: French14591363GLM 4.6
LMArena Text: Spanish14361353GLM 4.6
LMArena Text: Russian14181359GLM 4.6
LMArena Text: Polish14471334GLM 4.6
LMArena Text: multi-turn14271364GLM 4.6
LMArena Text: longer queries14221366GLM 4.6
LMArena Text: writing and language14151345GLM 4.6
LMArena Text: entertainment and media14091329GLM 4.6
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 4.6 and MiniMax M2.5
BenchmarkGLM 4.6MiniMax 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 4.6$0.0016Fits in one request
MiniMax M2.5$0.0007Fits in one request

MiniMax M2.5 costs less

Repository review

50K input + 3K output tokens

GLM 4.6$0.034Fits in one request
MiniMax M2.5$0.016Fits in one request

MiniMax M2.5 costs less

Agent loop

200K input + 10K output tokens

GLM 4.6$0.132Exceeds 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 4.6 and MiniMax M2.5
SpecificationZ.ai GLM 4.6MiniMax M2.5
MakerZ.aiMiniMax
WeightsOpen weightsOpen weights
ReleasedSeptember 30, 2025February 12, 2026
Context window205K205K
Input $/Mtok$0.55$0.27
Output $/Mtok$2.20$0.95
Coding index52.259.4
Independent measurements behind it22
Cost per solved taskNot computable$0.62
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.6 better than MiniMax M2.5 for coding?

Pick MiniMax M2.5 for coding: it scores 59.4 on the Vibe Coding Index against 52.2 for GLM 4.6. Pick GLM 4.6 when what matters is LiveCodeBench (Vals AI) or LMArena Text: coding, where it leads.

Which is cheaper, GLM 4.6 or MiniMax M2.5?

GLM 4.6 costs $0.55 per million input tokens and $2.20 per million output tokens; MiniMax M2.5 costs $0.27 and $0.95. Cost per solved task is known for MiniMax M2.5 only.

Which has the bigger context window, GLM 4.6 or MiniMax M2.5?

Neither: both take 205K tokens.

How many benchmarks is the GLM 4.6 vs MiniMax M2.5 comparison based on?

56 benchmarks have a result for both models as of October 21, 2026. GLM 4.6's coding index rests on 2 independent benchmarks 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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