Enter opens the highlighted result. Esc closes.

GLM 4.6 vs MiniMax M2.7

MiniMax M2.7 leads GLM 4.6 on coding, 36.2 to 8.8 on the Vibe Coding Index, and MiniMax M2.7 costs 1.8x less per output token ($1.20 vs $2.20 per million). Updated October 22, 2026; 55 benchmarks have a result for both.

Replace GLM 4.6 Replace MiniMax M2.7
GLM 4.6Z.ai
Coding
8.8
Overall
33.4
Output $/M
$2.20
29
MiniMax M2.7MiniMax
Coding
36.2
Overall
44.5
Output $/M
$1.20
26
vs boards won of 55 shared

GLM 4.6 or MiniMax M2.7: which to pick

Pick MiniMax M2.7 for coding: it scores 36.2 on the Vibe Coding Index against 8.8 for GLM 4.6.

Pick GLM 4.6 for work measured by LiveCodeBench (Vals AI) or LMArena Text: software and IT, where it leads.

Model AGLM 4.6Z.ai8.8/1002 independent benchmarksNo cost per solved taskModel BMiniMax M2.7MiniMax36.2/1003 independent benchmarks$0.64 per solved task
Shared results
55
GLM 4.6 only
14
MiniMax M2.7 only
19
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 4.6 8.8 from 2 benchmarks, MiniMax M2.7 36.2 from 3 benchmarks.
Widest category
General, 9.7 points apart on average over 19 shared benchmarks.
Widest single gap
IFBench: GLM 4.6 43.4% against MiniMax M2.7 75.7%, 32.3 points apart on a 0 to 100 scale.
Output price
GLM 4.6 $2.20 against MiniMax M2.7 $1.20 per million output tokens, 1.8x 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 M2.7Ties
Coding28
Intelligence01
Agentic work01
Reasoning24
Math30
Knowledge53
Web design07
General172
All shared boards2926
CodingLike-for-likeSix of these boards feed the Vibe Coding Index28
Coding benchmarks for GLM 4.6 and MiniMax M2.7
BenchmarkGLM 4.6MiniMax M2.7Leader
SWE-bench Verified68.2%Not measuredOnly GLM 4.6 measured
LMArena WebDev13411399MiniMax M2.7
AA Coding Index45.852.6MiniMax M2.7
LiveCodeBench (Vals AI)81.0%79.9%GLM 4.6
SWE-bench Verified (Vals AI)Not measured73.8%Only MiniMax M2.7 measured
Vibe Code Bench (Vals AI)3.1%11.9%MiniMax M2.7
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)69.5%Not measuredOnly GLM 4.6 measured
SciCodeNot measured50.1%Only MiniMax M2.7 measured
Terminal-Bench Hard (Artificial Analysis run)28.8%39.4%MiniMax M2.7
Terminal-Bench24.5%45.1%MiniMax M2.7
WeirdMLNot measured37.0%Only MiniMax M2.7 measured
ALE-Bench340.8599.3MiniMax M2.7
LMArena Text: coding14491454MiniMax M2.7
LMArena Text: software and IT14551446GLM 4.6
LMArena WebDev: ReactNot measured1388Only MiniMax M2.7 measured
LMArena WebDev: HTML13501401MiniMax M2.7
Neither model has a result on the other 20 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for GLM 4.6 and MiniMax M2.7
BenchmarkGLM 4.6MiniMax M2.7Leader
AA Intelligence Index18.522.8MiniMax 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 4.6 and MiniMax M2.7
BenchmarkGLM 4.6MiniMax M2.7Leader
AA Agentic IndexNot measured15.3Only MiniMax M2.7 measured
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 apps1084Not measuredOnly GLM 4.6 measured
Design Arena: agents building Godot games1180Not measuredOnly GLM 4.6 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 Telecom76.9%84.8%MiniMax M2.7
Berkeley Function Calling Leaderboard (BFCL)72.4%Not measuredOnly GLM 4.6 measured
BrowseComp-Plus48.2%Not measuredOnly GLM 4.6 measured
Neither model has a result on the other 29 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index24
Reasoning benchmarks for GLM 4.6 and MiniMax M2.7
BenchmarkGLM 4.6MiniMax M2.7Leader
GPQA Diamond74.5%86.6%MiniMax M2.7
GPQA Diamond (Artificial Analysis run)78.0%87.4%MiniMax M2.7
Humanity's Last Exam (Artificial Analysis run)14.5%29.6%MiniMax M2.7
LMArena Text: hard prompts14401422GLM 4.6
LMArena Text: expert14321443MiniMax M2.7
LMArena Text: hard prompts in English14451441GLM 4.6
Kagi LLM Benchmark45.7%Not measuredOnly GLM 4.6 measured
Neither model has a result on the other 15 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index30
Math benchmarks for GLM 4.6 and MiniMax M2.7
BenchmarkGLM 4.6MiniMax M2.7Leader
AIME92.7%91.0%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 measured3.0%Only MiniMax M2.7 measured
LMArena Text: math14321421GLM 4.6
LMArena Text: mathematical work14471437GLM 4.6
Neither model has a result on the other 6 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index53
Knowledge benchmarks for GLM 4.6 and MiniMax M2.7
BenchmarkGLM 4.6MiniMax M2.7Leader
MMLU Pro82.2%80.4%GLM 4.6
LegalBench (Vals AI)79.6%84.0%MiniMax M2.7
CaseLaw (Vals AI)Not measured60.9%Only MiniMax M2.7 measured
CorpFin (Vals AI)56.8%61.2%MiniMax M2.7
TaxEval (Vals AI)66.2%66.6%MiniMax M2.7
MedQA (Vals AI)92.2%Not measuredOnly GLM 4.6 measured
MedCode (Vals AI)Not measured34.4%Only MiniMax M2.7 measured
MMLU Pro (Artificial Analysis run)82.9%Not measuredOnly GLM 4.6 measured
LMArena Text: science14561416GLM 4.6
LMArena Text: medicine and healthcare14551409GLM 4.6
LMArena Text: legal and government14481403GLM 4.6
LMArena Text: business and finance14161407GLM 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 M2.7
BenchmarkGLM 4.6MiniMax M2.7Leader
Design Arena: websites11741243MiniMax M2.7
Design Arena: UI components11451210MiniMax M2.7
Design Arena: game dev11501209MiniMax M2.7
Design Arena: data viz11611233MiniMax M2.7
Design Arena: 3D11281195MiniMax M2.7
Design Arena: all code categories11651233MiniMax M2.7
Design Arena: SVG11081137MiniMax 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 index172
General benchmarks for GLM 4.6 and MiniMax M2.7
BenchmarkGLM 4.6MiniMax M2.7Leader
MedScribe (Vals AI)Not measured79.9%Only MiniMax M2.7 measured
IFBench43.4%75.7%MiniMax M2.7
Long context reasoning (AA-LCR)54.0%78.3%MiniMax M2.7
ExploitBenchNot measured13.3%Only MiniMax M2.7 measured
LMArena Text14401405GLM 4.6
LMArena Text: instruction following14101405GLM 4.6
LMArena Text: creative writing14121354GLM 4.6
LMArena Text: English14491426GLM 4.6
LMArena Text: non-English14261382GLM 4.6
LMArena Text: Chinese14991442GLM 4.6
LMArena Text: German14471398GLM 4.6
LMArena Text: Japanese13931260GLM 4.6
LMArena Text: Korean14001314GLM 4.6
LMArena Text: French14591424GLM 4.6
LMArena Text: Spanish14361402GLM 4.6
LMArena Text: Russian14181383GLM 4.6
LMArena Text: Polish14471387GLM 4.6
LMArena Text: multi-turn14271412GLM 4.6
LMArena Text: longer queries14221418GLM 4.6
LMArena Text: writing and language14151376GLM 4.6
LMArena Text: entertainment and media14091360GLM 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 M2.7
BenchmarkGLM 4.6MiniMax 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 4.6$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 4.6$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 4.6$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 4.6 and MiniMax M2.7
SpecificationZ.ai GLM 4.6MiniMax M2.7
MakerZ.aiMiniMax
WeightsOpen weightsOpen weights
ReleasedSeptember 30, 2025March 18, 2026
Context window205K205K
Input $/Mtok$0.60$0.30
Output $/Mtok$2.20$1.20
Coding index8.836.2
Independent measurements behind it23
Cost per solved taskNot computable$0.64
SourceOpenRouter listingOpenRouter listing

Questions

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

Pick MiniMax M2.7 for coding: it scores 36.2 on the Vibe Coding Index against 8.8 for GLM 4.6. Pick GLM 4.6 for work measured by LiveCodeBench (Vals AI) or LMArena Text: software and IT, where it leads.

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

GLM 4.6 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 4.6 or MiniMax M2.7?

Neither: both take 205K tokens.

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

55 benchmarks have a result for both models as of October 22, 2026. GLM 4.6's coding index rests on 2 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.

Compare next

Embed this comparison on your site

Free under CC BY 4.0, while the source link stays visible. Preview the frame · Terms and other embeds

<iframe src="https://llmbenchmark.ai/embed/compare/glm-4-6-vs-minimax-minimax-m2-7" title="GLM 4.6 vs MiniMax M2.7, LLMBenchmark.ai" width="100%" height="360" style="border:0;max-width:100%" loading="lazy"></iframe>
<p style="font:13px/1.4 sans-serif;margin:6px 0 0">Source: <a href="https://llmbenchmark.ai/compare/glm-4-6-vs-minimax-minimax-m2-7">LLMBenchmark.ai, GLM 4.6 vs MiniMax M2.7</a></p>