Enter opens the highlighted result. Esc closes.

GLM 4.7 vs MiniMax M2.7

GLM 4.7 leads MiniMax M2.7 on coding, 55.1 to 36.2 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; 62 benchmarks have a result for both.

Replace GLM 4.7 Replace MiniMax M2.7
GLM 4.7Z.ai
Coding
55.1
Overall
56.9
Output $/M
$2.20
37
MiniMax M2.7MiniMax
Coding
36.2
Overall
44.5
Output $/M
$1.20
25
vs boards won of 62 shared

GLM 4.7 or MiniMax M2.7: which to pick

Pick GLM 4.7 for coding: it scores 55.1 on the Vibe Coding Index against 36.2 for MiniMax M2.7, though on 2 benchmarks against 3.

Pick MiniMax M2.7 for output tokens 1.8x cheaper ($1.20 vs $2.20 per million) and work measured by AA Coding Index or SWE-bench Verified (Vals AI), where it leads.

Model AGLM 4.7Z.ai55.1/1002 independent benchmarks$0.29 per solved taskModel BMiniMax M2.7MiniMax36.2/1003 independent benchmarks$0.64 per solved task
Shared results
62
GLM 4.7 only
19
MiniMax M2.7 only
12
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 4.7 55.1 from 2 benchmarks, MiniMax M2.7 36.2 from 3 benchmarks.
Widest category
Agentic work, 11.1 points apart on average over 1 shared benchmark.
Widest single gap
LMArena Text: Japanese: GLM 4.7 1439 against MiniMax M2.7 1260, 29.8 points apart on a 0 to 100 scale.
Output price
GLM 4.7 $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 4.7BarsMiniMax M2.7Ties
Coding65
Intelligence02
Agentic work10
Reasoning24
Math31
Knowledge64
Web design26
General173
All shared boards3725
CodingLike-for-likeSix of these boards feed the Vibe Coding Index65
Coding benchmarks for GLM 4.7 and MiniMax M2.7
BenchmarkGLM 4.7MiniMax M2.7Leader
LMArena WebDev14351399GLM 4.7
AA Coding Index45.352.6MiniMax M2.7
LiveCodeBench (Vals AI)82.2%79.9%GLM 4.7
SWE-bench Verified (Vals AI)69.4%73.8%MiniMax M2.7
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)89.4%Not measuredOnly GLM 4.7 measured
SciCodeNot measured50.1%Only MiniMax M2.7 measured
Terminal-Bench Hard (Artificial Analysis run)31.8%39.4%MiniMax M2.7
Terminal-Bench33.4%45.1%MiniMax M2.7
WeirdMLNot measured37.0%Only MiniMax M2.7 measured
ALE-Bench399.5599.3MiniMax M2.7
LMArena Text: coding14541454GLM 4.7
LMArena Text: software and IT14531446GLM 4.7
LMArena WebDev: React14201388GLM 4.7
LMArena WebDev: HTML14471401GLM 4.7
Neither model has a result on the other 21 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for GLM 4.7 and MiniMax M2.7
BenchmarkGLM 4.7MiniMax M2.7Leader
AA Intelligence Index22.222.8MiniMax M2.7
Epoch Capabilities Index143.6145.8MiniMax M2.7
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for GLM 4.7 and MiniMax M2.7
BenchmarkGLM 4.7MiniMax M2.7Leader
AA Agentic IndexNot measured15.3Only MiniMax M2.7 measured
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 apps1112Not measuredOnly GLM 4.7 measured
Design Arena: agents building Godot games1058Not measuredOnly GLM 4.7 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 Telecom95.9%84.8%GLM 4.7
Vending-Bench 2$2,377Not measuredOnly GLM 4.7 measured
BrowseComp-Plus64.7%Not measuredOnly GLM 4.7 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 4.7 and MiniMax M2.7
BenchmarkGLM 4.7MiniMax M2.7Leader
GPQA Diamond80.0%86.6%MiniMax M2.7
GPQA Diamond (Artificial Analysis run)85.9%87.4%MiniMax M2.7
Humanity's Last Exam (Artificial Analysis run)27.4%29.6%MiniMax M2.7
GPQA Diamond (Epoch AI run)83.3%Not measuredOnly GLM 4.7 measured
SimpleBench47.7%Not measuredOnly GLM 4.7 measured
Chess puzzles21.0%Not measuredOnly GLM 4.7 measured
LMArena Text: hard prompts14431422GLM 4.7
LMArena Text: expert14241443MiniMax M2.7
LMArena Text: hard prompts in English14521441GLM 4.7
Neither model has a result on the other 13 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index31
Math benchmarks for GLM 4.7 and MiniMax M2.7
BenchmarkGLM 4.7MiniMax M2.7Leader
AIME93.3%91.0%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%3.0%GLM 4.7
LMArena Text: math14241421GLM 4.7
LMArena Text: mathematical work14111437MiniMax M2.7
Neither model has a result on the other 5 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index64
Knowledge benchmarks for GLM 4.7 and MiniMax M2.7
BenchmarkGLM 4.7MiniMax M2.7Leader
MMLU Pro82.7%80.4%GLM 4.7
LegalBench (Vals AI)83.4%84.0%MiniMax M2.7
CaseLaw (Vals AI)54.9%60.9%MiniMax M2.7
CorpFin (Vals AI)46.4%61.2%MiniMax M2.7
TaxEval (Vals AI)68.8%66.6%GLM 4.7
MedQA (Vals AI)93.7%Not measuredOnly GLM 4.7 measured
MedCode (Vals AI)32.8%34.4%MiniMax M2.7
MMLU Pro (Artificial Analysis run)85.6%Not measuredOnly GLM 4.7 measured
SimpleQA Verified32.2%Not measuredOnly GLM 4.7 measured
LMArena Text: science14571416GLM 4.7
LMArena Text: medicine and healthcare14691409GLM 4.7
LMArena Text: legal and government14441403GLM 4.7
LMArena Text: business and finance14291407GLM 4.7
Neither model has a result on the other 5 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index26
Web design benchmarks for GLM 4.7 and MiniMax M2.7
BenchmarkGLM 4.7MiniMax M2.7Leader
Design Arena: websites12251243MiniMax M2.7
Design Arena: UI components11961210MiniMax M2.7
Design Arena: game dev11881209MiniMax M2.7
Design Arena: data viz11951233MiniMax M2.7
Design Arena: 3D11901195MiniMax M2.7
Design Arena: all code categories12161233MiniMax M2.7
Design Arena: SVG11421137GLM 4.7
Design Arena: ASCII art11651136GLM 4.7
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index173
General benchmarks for GLM 4.7 and MiniMax M2.7
BenchmarkGLM 4.7MiniMax M2.7Leader
MedScribe (Vals AI)68.6%79.9%MiniMax M2.7
IFBench67.9%75.7%MiniMax M2.7
Long context reasoning (AA-LCR)71.0%78.3%MiniMax M2.7
CL-bench15.9%Not measuredOnly GLM 4.7 measured
ExploitBenchNot measured13.3%Only MiniMax M2.7 measured
CL-bench Life10.9%Not measuredOnly GLM 4.7 measured
LMArena Text14351405GLM 4.7
LMArena Text: instruction following14121405GLM 4.7
LMArena Text: creative writing14011354GLM 4.7
LMArena Text: English14531426GLM 4.7
LMArena Text: non-English14171382GLM 4.7
LMArena Text: Chinese14951442GLM 4.7
LMArena Text: German14261398GLM 4.7
LMArena Text: Japanese14391260GLM 4.7
LMArena Text: Korean13991314GLM 4.7
LMArena Text: French14321424GLM 4.7
LMArena Text: Spanish14341402GLM 4.7
LMArena Text: Russian14231383GLM 4.7
LMArena Text: Polish14351387GLM 4.7
LMArena Text: multi-turn14461412GLM 4.7
LMArena Text: longer queries14321418GLM 4.7
LMArena Text: writing and language14111376GLM 4.7
LMArena Text: entertainment and media14001360GLM 4.7
Neither model has a result on the other 23 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 M2.7
BenchmarkGLM 4.7MiniMax 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 4.7$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 4.7$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 4.7$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 4.7 and MiniMax M2.7
SpecificationZ.ai GLM 4.7MiniMax M2.7
MakerZ.aiMiniMax
WeightsOpen weightsOpen weights
ReleasedDecember 22, 2025March 18, 2026
Context window205K205K
Input $/Mtok$0.60$0.30
Output $/Mtok$2.20$1.20
Coding index55.136.2
Independent measurements behind it23
Cost per solved task$0.29$0.64
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.7 better than MiniMax M2.7 for coding?

Pick GLM 4.7 for coding: it scores 55.1 on the Vibe Coding Index against 36.2 for MiniMax M2.7, though on 2 benchmarks against 3. Pick MiniMax M2.7 for output tokens 1.8x cheaper ($1.20 vs $2.20 per million) and work measured by AA Coding Index or SWE-bench Verified (Vals AI), where it leads.

Which is cheaper, GLM 4.7 or MiniMax M2.7?

GLM 4.7 costs $0.60 per million input tokens and $2.20 per million output tokens; MiniMax M2.7 costs $0.30 and $1.20. Per solved SWE-bench Verified task, GLM 4.7 costs $0.29 and MiniMax M2.7 $0.64.

Which has the bigger context window, GLM 4.7 or MiniMax M2.7?

Neither: both take 205K tokens.

How many benchmarks is the GLM 4.7 vs MiniMax M2.7 comparison based on?

62 benchmarks have a result for both models as of October 22, 2026. GLM 4.7'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-4-7-vs-minimax-minimax-m2-7" title="GLM 4.7 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-4-7-vs-minimax-minimax-m2-7">LLMBenchmark.ai, GLM 4.7 vs MiniMax M2.7</a></p>