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GLM 4.6V vs MiniMax M2.7

MiniMax M2.7 leads GLM 4.6V on the Artificial Analysis Intelligence Index, 22.8 to 11.2, and GLM 4.6V costs 1.3x less per output token ($0.90 vs $1.20 per million). Updated October 22, 2026; 27 benchmarks have a result for both.

Replace GLM 4.6V Replace MiniMax M2.7
GLM 4.6VZ.ai
Coding
No data
Overall
No data
Output $/M
$0.90
0
MiniMax M2.7MiniMax
Coding
36.2
Overall
44.5
Output $/M
$1.20
27
vs boards won of 27 shared

GLM 4.6V or MiniMax M2.7: which to pick

Only MiniMax M2.7 has a coding score, 36.2 on the Vibe Coding Index; GLM 4.6V has none, so the two cannot be ranked on coding.

Pick GLM 4.6V for output tokens 1.3x cheaper ($0.90 vs $1.20 per million).

Model AGLM 4.6VZ.aiNot benchmarkedNo coding benchmarkNo cost per solved taskModel BMiniMax M2.7MiniMax36.2/1003 independent benchmarks$0.64 per solved task
Shared results
27
GLM 4.6V only
11
MiniMax M2.7 only
47
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 4.6V not benchmarked, MiniMax M2.7 36.2 from 3 benchmarks.
Widest category
Agentic work, 53.2 points apart on average over 1 shared benchmark.
Widest single gap
τ²-Bench Telecom: GLM 4.6V 31.6% against MiniMax M2.7 84.8%, 53.2 points apart on a 0 to 100 scale.
Output price
GLM 4.6V $0.90 against MiniMax M2.7 $1.20 per million output tokens, 1.3x apart.
Context window
GLM 4.6V 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 4.6VBarsMiniMax M2.7Ties
Coding03
Intelligence01
Agentic work01
Reasoning05
Knowledge04
General013
All shared boards027
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
Coding benchmarks for GLM 4.6V and MiniMax M2.7
BenchmarkGLM 4.6VMiniMax M2.7Leader
LMArena WebDevNot measured1399Only MiniMax M2.7 measured
AA Coding IndexNot measured52.6Only MiniMax M2.7 measured
LiveCodeBench (Vals AI)Not measured79.9%Only MiniMax M2.7 measured
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)41.1%Not measuredOnly GLM 4.6V measured
SciCodeNot measured50.1%Only MiniMax M2.7 measured
Terminal-Bench Hard (Artificial Analysis run)14.4%39.4%MiniMax M2.7
Terminal-BenchNot measured45.1%Only MiniMax M2.7 measured
WeirdMLNot measured37.0%Only MiniMax M2.7 measured
ALE-BenchNot measured599.3Only MiniMax M2.7 measured
LMArena Text: coding13891454MiniMax M2.7
LMArena Text: software and IT13911446MiniMax 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 21 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for GLM 4.6V and MiniMax M2.7
BenchmarkGLM 4.6VMiniMax M2.7Leader
AA Intelligence Index11.222.8MiniMax 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 4.6V and MiniMax M2.7
BenchmarkGLM 4.6VMiniMax 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 Telecom31.6%84.8%MiniMax M2.7
Neither model has a result on the other 35 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
Reasoning benchmarks for GLM 4.6V and MiniMax M2.7
BenchmarkGLM 4.6VMiniMax M2.7Leader
GPQA DiamondNot measured86.6%Only MiniMax M2.7 measured
GPQA Diamond (Artificial Analysis run)71.9%87.4%MiniMax M2.7
Humanity's Last Exam (Artificial Analysis run)9.6%29.6%MiniMax M2.7
LMArena Text: hard prompts13671422MiniMax M2.7
LMArena Text: expert13711443MiniMax M2.7
LMArena Text: hard prompts in English13911441MiniMax M2.7
LMArena Vision: diagrams1182Not measuredOnly GLM 4.6V measured
LMArena Vision: homework1155Not measuredOnly GLM 4.6V measured
Neither model has a result on the other 14 boards here
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
Math benchmarks for GLM 4.6V and MiniMax M2.7
BenchmarkGLM 4.6VMiniMax M2.7Leader
AIMENot measured91.0%Only MiniMax M2.7 measured
AA Math Index85.3Not measuredOnly GLM 4.6V measured
AIME 2025 (Artificial Analysis run)85.3%Not measuredOnly GLM 4.6V measured
ProofBenchNot measured3.0%Only MiniMax M2.7 measured
LMArena Text: mathNot measured1421Only MiniMax M2.7 measured
LMArena Text: mathematical workNot measured1437Only MiniMax M2.7 measured
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for GLM 4.6V and MiniMax M2.7
BenchmarkGLM 4.6VMiniMax M2.7Leader
MMLU ProNot measured80.4%Only MiniMax M2.7 measured
LegalBench (Vals AI)Not measured84.0%Only MiniMax M2.7 measured
CaseLaw (Vals AI)Not measured60.9%Only MiniMax M2.7 measured
CorpFin (Vals AI)Not measured61.2%Only MiniMax M2.7 measured
TaxEval (Vals AI)Not measured66.6%Only MiniMax M2.7 measured
MedCode (Vals AI)Not measured34.4%Only MiniMax M2.7 measured
MMLU Pro (Artificial Analysis run)79.9%Not measuredOnly GLM 4.6V measured
LMArena Text: science13891416MiniMax M2.7
LMArena Text: medicine and healthcare13891409MiniMax M2.7
LMArena Text: legal and government13931403MiniMax M2.7
LMArena Text: business and finance13591407MiniMax M2.7
Neither model has a result on the other 7 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Web design benchmarks for GLM 4.6V and MiniMax M2.7
BenchmarkGLM 4.6VMiniMax M2.7Leader
Design Arena: websitesNot measured1243Only MiniMax M2.7 measured
Design Arena: UI componentsNot measured1210Only MiniMax M2.7 measured
Design Arena: game devNot measured1209Only MiniMax M2.7 measured
Design Arena: data vizNot measured1233Only MiniMax M2.7 measured
Design Arena: 3DNot measured1195Only MiniMax M2.7 measured
Design Arena: all code categoriesNot measured1233Only MiniMax M2.7 measured
Design Arena: SVGNot measured1137Only MiniMax M2.7 measured
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 index013
General benchmarks for GLM 4.6V and MiniMax M2.7
BenchmarkGLM 4.6VMiniMax M2.7Leader
MedScribe (Vals AI)Not measured79.9%Only MiniMax M2.7 measured
IFBench30.1%75.7%MiniMax M2.7
Long context reasoning (AA-LCR)48.7%78.3%MiniMax M2.7
ExploitBenchNot measured13.3%Only MiniMax M2.7 measured
LMArena Text13761405MiniMax M2.7
LMArena Text: instruction following13501405MiniMax M2.7
LMArena Text: creative writing13471354MiniMax M2.7
LMArena Vision1164Not measuredOnly GLM 4.6V measured
LMArena Text: English13951426MiniMax M2.7
LMArena Text: non-English13581382MiniMax M2.7
LMArena Text: Chinese14251442MiniMax M2.7
LMArena Text: GermanNot measured1398Only MiniMax M2.7 measured
LMArena Text: JapaneseNot measured1260Only MiniMax M2.7 measured
LMArena Text: KoreanNot measured1314Only MiniMax M2.7 measured
LMArena Text: FrenchNot measured1424Only MiniMax M2.7 measured
LMArena Text: SpanishNot measured1402Only MiniMax M2.7 measured
LMArena Text: Russian13391383MiniMax M2.7
LMArena Text: PolishNot measured1387Only MiniMax M2.7 measured
LMArena Text: multi-turn13591412MiniMax M2.7
LMArena Text: longer queries13571418MiniMax M2.7
LMArena Text: writing and language13471376MiniMax M2.7
LMArena Text: entertainment and media13431360MiniMax M2.7
LMArena Vision: English1179Not measuredOnly GLM 4.6V measured
LMArena Vision: Chinese1217Not measuredOnly GLM 4.6V measured
LMArena Vision: reading text1171Not measuredOnly GLM 4.6V measured
LMArena Vision: creative writing1113Not measuredOnly GLM 4.6V measured
Neither model has a result on the other 20 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 4.6V and MiniMax M2.7
BenchmarkGLM 4.6VMiniMax 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 4.6V$0.0008Fits in one request
MiniMax M2.7$0.0009Fits in one request

GLM 4.6V costs less

Repository review

50K input + 3K output tokens

GLM 4.6V$0.018Fits in one request
MiniMax M2.7$0.019Fits in one request

GLM 4.6V costs less

Agent loop

200K input + 10K output tokens

GLM 4.6V$0.069Exceeds the context window
MiniMax M2.7$0.072Exceeds the context window

GLM 4.6V 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 4.6V and MiniMax M2.7
SpecificationZ.ai GLM 4.6VMiniMax M2.7
MakerZ.aiMiniMax
WeightsOpen weightsOpen weights
ReleasedDecember 8, 2025March 18, 2026
Context window131K205K
Input $/Mtok$0.30$0.30
Output $/Mtok$0.90$1.20
Coding indexNot benchmarked36.2
Independent measurements behind it03
Cost per solved taskNot computable$0.64
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.6V better than MiniMax M2.7 for coding?

Only MiniMax M2.7 has a coding score, 36.2 on the Vibe Coding Index; GLM 4.6V has none, so the two cannot be ranked on coding. Pick GLM 4.6V for output tokens 1.3x cheaper ($0.90 vs $1.20 per million).

Which is cheaper, GLM 4.6V or MiniMax M2.7?

GLM 4.6V costs $0.30 per million input tokens and $0.90 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 4.6V or MiniMax M2.7?

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

How many benchmarks is the GLM 4.6V vs MiniMax M2.7 comparison based on?

27 benchmarks have a result for both models as of October 22, 2026. GLM 4.6V's coding index rests on 0 independent benchmarks and MiniMax M2.7's on 3. Where only one model has a result, the page says so instead of naming a winner.

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