Enter opens the highlighted result. Esc closes.

GLM 5.2 vs MiniMax M2.7

GLM 5.2 leads MiniMax M2.7 on coding, 61.1 to 36.2 on the Vibe Coding Index, and MiniMax M2.7 costs 3.7x less per output token ($1.20 vs $4.40 per million). Updated October 22, 2026; 69 benchmarks have a result for both.

Replace GLM 5.2 Replace MiniMax M2.7
GLM 5.2Z.ai
Coding
61.1
Overall
62.5
Output $/M
$4.40
65
MiniMax M2.7MiniMax
Coding
36.2
Overall
44.5
Output $/M
$1.20
3
vs boards won of 69 shared, 1 tied

GLM 5.2 or MiniMax M2.7: which to pick

Pick GLM 5.2 for coding: it scores 61.1 on the Vibe Coding Index against 36.2 for MiniMax M2.7.

Pick MiniMax M2.7 for output tokens 3.7x cheaper ($1.20 vs $4.40 per million) and a lower cost per solved task ($0.64 against $0.86).

Model AGLM 5.2Z.ai61.1/1004 independent benchmarks$0.86 per solved taskModel BMiniMax M2.7MiniMax36.2/1003 independent benchmarks$0.64 per solved task
Shared results
69
GLM 5.2 only
35
MiniMax M2.7 only
5
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 5.2 61.1 from 4 benchmarks, MiniMax M2.7 36.2 from 3 benchmarks.
Widest category
Agentic work, 22.6 points apart on average over 5 shared benchmarks.
Widest single gap
Vibe Code Bench (Vals AI): GLM 5.2 64.0% against MiniMax M2.7 11.9%, 52.0 points apart on a 0 to 100 scale.
Output price
GLM 5.2 $4.40 against MiniMax M2.7 $1.20 per million output tokens, 3.7x apart.
Context window
GLM 5.2 takes 1.05M 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 5.2BarsMiniMax M2.7Ties
Coding151
Intelligence20
Agentic work50
Reasoning51
Math30
Knowledge90
Web design80
General1811 tie
All shared boards6531 tie
CodingLike-for-likeSix of these boards feed the Vibe Coding Index151
Coding benchmarks for GLM 5.2 and MiniMax M2.7
BenchmarkGLM 5.2MiniMax M2.7Leader
LMArena WebDev16041399GLM 5.2
AA Coding Index68.852.6GLM 5.2
LiveCodeBench (Vals AI)69.5%79.9%MiniMax M2.7
SWE-bench Verified (Vals AI)82.8%73.8%GLM 5.2
Vibe Code Bench (Vals AI)64.0%11.9%GLM 5.2
Code migration (Vals AI)37.9%8.7%GLM 5.2
ProgramBench (Vals AI)0.5%0.0%GLM 5.2
Terminal-Bench 2.1 (Vals AI)67.8%48.7%GLM 5.2
SkillsBench (Vals AI)45.1%Not measuredOnly GLM 5.2 measured
SciCode51.2%50.1%GLM 5.2
Terminal-Bench Hard (Artificial Analysis run)50.8%39.4%GLM 5.2
Terminal-BenchNot measured45.1%Only MiniMax M2.7 measured
FrontierCode24.5%Not measuredOnly GLM 5.2 measured
WeirdML70.1%37.0%GLM 5.2
ALE-Bench1047.0599.3GLM 5.2
LMArena Text: coding14871454GLM 5.2
LMArena Text: software and IT14861446GLM 5.2
LMArena WebDev: React16171388GLM 5.2
LMArena WebDev: HTML15651401GLM 5.2
SWE Atlas: Codebase QnA48.1%Not measuredOnly GLM 5.2 measured
SWE Atlas: Refactoring42.4%Not measuredOnly GLM 5.2 measured
SWE Atlas: Test Writing41.5%Not measuredOnly GLM 5.2 measured
Neither model has a result on the other 21 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
Intelligence benchmarks for GLM 5.2 and MiniMax M2.7
BenchmarkGLM 5.2MiniMax M2.7Leader
AA Intelligence Index33.722.8GLM 5.2
Epoch Capabilities Index151.9145.8GLM 5.2
Agentic workLike-for-likePublic boards, shown as published, kept out of the index50
Agentic work benchmarks for GLM 5.2 and MiniMax M2.7
BenchmarkGLM 5.2MiniMax M2.7Leader
AA Agentic Index38.415.3GLM 5.2
Design Arena: agents building web apps1222Not measuredOnly GLM 5.2 measured
Design Arena: agents building full-stack apps1241Not measuredOnly GLM 5.2 measured
Design Arena: agents building mobile apps1159Not measuredOnly GLM 5.2 measured
Design Arena: agents building Android apps1177Not measuredOnly GLM 5.2 measured
Design Arena: agents building Godot games1142Not measuredOnly GLM 5.2 measured
Design Arena: agents building games1206Not measuredOnly GLM 5.2 measured
Design Arena: HTML slides1114Not measuredOnly GLM 5.2 measured
Design Arena: PowerPoint slides1167Not measuredOnly GLM 5.2 measured
SREBench (Vals AI)0.0%Not measuredOnly GLM 5.2 measured
Finance Agent (Vals AI)49.7%27.9%GLM 5.2
Tax Agent Bench (Vals AI)Not measured6.9%Only MiniMax M2.7 measured
Legal Research Bench (Vals AI)31.3%10.6%GLM 5.2
Excel Modeling Benchmark (Vals AI)61.5%28.4%GLM 5.2
τ²-Bench Telecom99.1%84.8%GLM 5.2
APEX-Agents45.2%Not measuredOnly GLM 5.2 measured
PostTrainBench31.7%Not measuredOnly GLM 5.2 measured
Vending-Bench 2$8,314Not measuredOnly GLM 5.2 measured
MCP Atlas77.8%Not measuredOnly GLM 5.2 measured
Neither model has a result on the other 23 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index51
Reasoning benchmarks for GLM 5.2 and MiniMax M2.7
BenchmarkGLM 5.2MiniMax M2.7Leader
GPQA Diamond85.6%86.6%MiniMax M2.7
GPQA Diamond (Artificial Analysis run)89.5%87.4%GLM 5.2
Humanity's Last Exam (Artificial Analysis run)41.1%29.6%GLM 5.2
GPQA Diamond (Epoch AI run)91.9%Not measuredOnly GLM 5.2 measured
ARC-AGI77.0%Not measuredOnly GLM 5.2 measured
ARC-AGI-222.8%Not measuredOnly GLM 5.2 measured
SimpleBench58.8%Not measuredOnly GLM 5.2 measured
Chess puzzles22.0%Not measuredOnly GLM 5.2 measured
Mystery game puzzles19.0%Not measuredOnly GLM 5.2 measured
EBR-bench9.5%Not measuredOnly GLM 5.2 measured
LMArena Text: hard prompts14811422GLM 5.2
LMArena Text: expert14871443GLM 5.2
LMArena Text: hard prompts in English14811441GLM 5.2
Kagi LLM Benchmark62.6%Not measuredOnly GLM 5.2 measured
Neither model has a result on the other 9 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index30
Math benchmarks for GLM 5.2 and MiniMax M2.7
BenchmarkGLM 5.2MiniMax M2.7Leader
AIMENot measured91.0%Only MiniMax M2.7 measured
OTIS Mock AIME86.4%Not measuredOnly GLM 5.2 measured
FrontierMath, tiers 1 to 359.2%Not measuredOnly GLM 5.2 measured
FrontierMath, tier 429.3%Not measuredOnly GLM 5.2 measured
ProofBench35.0%3.0%GLM 5.2
LMArena Text: math14831421GLM 5.2
LMArena Text: mathematical work14891437GLM 5.2
MathArena22.7%Not measuredOnly GLM 5.2 measured
Neither model has a result on the other 6 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index90
Knowledge benchmarks for GLM 5.2 and MiniMax M2.7
BenchmarkGLM 5.2MiniMax M2.7Leader
MMLU Pro86.7%80.4%GLM 5.2
LegalBench (Vals AI)84.1%84.0%GLM 5.2
CaseLaw (Vals AI)Not measured60.9%Only MiniMax M2.7 measured
CorpFin (Vals AI)66.1%61.2%GLM 5.2
TaxEval (Vals AI)73.3%66.6%GLM 5.2
MedCode (Vals AI)40.8%34.4%GLM 5.2
SimpleQA Verified34.2%Not measuredOnly GLM 5.2 measured
LMArena Text: science14891416GLM 5.2
LMArena Text: medicine and healthcare14751409GLM 5.2
LMArena Text: legal and government14861403GLM 5.2
LMArena Text: business and finance14511407GLM 5.2
Neither model has a result on the other 9 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index80
Web design benchmarks for GLM 5.2 and MiniMax M2.7
BenchmarkGLM 5.2MiniMax M2.7Leader
Design Arena: websites12851242GLM 5.2
Design Arena: UI components12941207GLM 5.2
Design Arena: game dev12671209GLM 5.2
Design Arena: data viz12861233GLM 5.2
Design Arena: 3D12871194GLM 5.2
Design Arena: all code categories12871233GLM 5.2
Design Arena: SVG12131137GLM 5.2
Design Arena: ASCII art11911136GLM 5.2
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index181
General benchmarks for GLM 5.2 and MiniMax M2.7
BenchmarkGLM 5.2MiniMax M2.7Leader
MedScribe (Vals AI)83.5%79.9%GLM 5.2
IFBench73.3%75.7%MiniMax M2.7
Long context reasoning (AA-LCR)78.3%78.3%Tie
DTBench93.6%Not measuredOnly GLM 5.2 measured
LMCA45.8%Not measuredOnly GLM 5.2 measured
Surface Evolver Bench55.6%Not measuredOnly GLM 5.2 measured
ExploitBenchNot measured13.3%Only MiniMax M2.7 measured
LMArena Text14701405GLM 5.2
LMArena Text: instruction following14661405GLM 5.2
LMArena Text: creative writing14631354GLM 5.2
LMArena Text: English14751426GLM 5.2
LMArena Text: non-English14591382GLM 5.2
LMArena Text: Chinese15221442GLM 5.2
LMArena Text: German14701398GLM 5.2
LMArena Text: Japanese14501260GLM 5.2
LMArena Text: Korean14401314GLM 5.2
LMArena Text: French14791424GLM 5.2
LMArena Text: Spanish14781402GLM 5.2
LMArena Text: Russian14681383GLM 5.2
LMArena Text: Polish14561387GLM 5.2
LMArena Text: multi-turn14691412GLM 5.2
LMArena Text: longer queries14801418GLM 5.2
LMArena Text: writing and language14631376GLM 5.2
LMArena Text: entertainment and media14501360GLM 5.2
EQ-Bench 41222Not measuredOnly GLM 5.2 measured
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 5.2 and MiniMax M2.7
BenchmarkGLM 5.2MiniMax 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 5.2$0.0036Fits in one request
MiniMax M2.7$0.0009Fits in one request

MiniMax M2.7 costs less

Repository review

50K input + 3K output tokens

GLM 5.2$0.083Fits in one request
MiniMax M2.7$0.019Fits in one request

MiniMax M2.7 costs less

Agent loop

200K input + 10K output tokens

GLM 5.2$0.324Fits in one request
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 5.2 and MiniMax M2.7
SpecificationZ.ai GLM 5.2MiniMax M2.7
MakerZ.aiMiniMax
WeightsOpen weightsOpen weights
ReleasedJune 16, 2026March 18, 2026
Context window1.05M205K
Input $/Mtok$1.40$0.30
Output $/Mtok$4.40$1.20
Coding index61.136.2
Independent measurements behind it43
Cost per solved task$0.86$0.64
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 5.2 better than MiniMax M2.7 for coding?

Pick GLM 5.2 for coding: it scores 61.1 on the Vibe Coding Index against 36.2 for MiniMax M2.7. Pick MiniMax M2.7 for output tokens 3.7x cheaper ($1.20 vs $4.40 per million) and a lower cost per solved task ($0.64 against $0.86).

Which is cheaper, GLM 5.2 or MiniMax M2.7?

GLM 5.2 costs $1.40 per million input tokens and $4.40 per million output tokens; MiniMax M2.7 costs $0.30 and $1.20. Per solved SWE-bench Verified task, GLM 5.2 costs $0.86 and MiniMax M2.7 $0.64.

Which has the bigger context window, GLM 5.2 or MiniMax M2.7?

GLM 5.2, with 1.05M tokens against 205K.

How many benchmarks is the GLM 5.2 vs MiniMax M2.7 comparison based on?

69 benchmarks have a result for both models as of October 22, 2026. GLM 5.2's coding index rests on 4 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-5-2-vs-minimax-minimax-m2-7" title="GLM 5.2 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-5-2-vs-minimax-minimax-m2-7">LLMBenchmark.ai, GLM 5.2 vs MiniMax M2.7</a></p>