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GLM 5.3 vs MiniMax M2.5

GLM 5.3 leads MiniMax M2.5 on coding, 70.6 to 37.2 on the Vibe Coding Index, and MiniMax M2.5 costs 3.7x less per output token ($1.20 vs $4.40 per million). Updated October 22, 2026; 53 benchmarks have a result for both.

Replace GLM 5.3 Replace MiniMax M2.5
GLM 5.3Z.ai
Coding
70.6
Overall
67.9
Output $/M
$4.40
53
MiniMax M2.5MiniMax
Coding
37.2
Overall
37.3
Output $/M
$1.20
0
vs boards won of 53 shared

GLM 5.3 or MiniMax M2.5: which to pick

Pick GLM 5.3 for coding: it scores 70.6 on the Vibe Coding Index against 37.2 for MiniMax M2.5.

Pick MiniMax M2.5 for output tokens 3.7x cheaper ($1.20 vs $4.40 per million).

Model AGLM 5.3Z.ai70.6/1006 independent benchmarks$0.36 per solved taskModel BMiniMax M2.5MiniMax37.2/1003 independent benchmarks$0.62 per solved task
Shared results
53
GLM 5.3 only
47
MiniMax M2.5 only
15
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 5.3 70.6 from 6 benchmarks, MiniMax M2.5 37.2 from 3 benchmarks.
Widest category
Agentic work, 51.0 points apart on average over 1 shared benchmark.
Widest single gap
Vibe Code Bench (Vals AI): GLM 5.3 78.1% against MiniMax M2.5 14.8%, 63.3 points apart on a 0 to 100 scale.
Output price
GLM 5.3 $4.40 against MiniMax M2.5 $1.20 per million output tokens, 3.7x apart.
Context window
GLM 5.3 takes 1.05M tokens, MiniMax M2.5 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 5.3BarsMiniMax M2.5Ties
Coding90
Intelligence20
Agentic work10
Reasoning60
Math30
Knowledge70
Web design70
General180
All shared boards530
CodingLike-for-likeSix of these boards feed the Vibe Coding Index90
Coding benchmarks for GLM 5.3 and MiniMax M2.5
BenchmarkGLM 5.3MiniMax M2.5Leader
SWE-bench VerifiedNot measured75.8%Only MiniMax M2.5 measured
SWE-bench MultilingualNot measured68.3%Only MiniMax M2.5 measured
LiveBench coding82.8%Not measuredOnly GLM 5.3 measured
LMArena WebDev16211386GLM 5.3
AA Coding Index74.8Not measuredOnly GLM 5.3 measured
LiveCodeBench (Vals AI)80.5%79.2%GLM 5.3
SWE-bench Verified (Vals AI)95.4%74.2%GLM 5.3
Terminal-Bench 4 (Vals AI)38.9%Not measuredOnly GLM 5.3 measured
Vibe Code Bench (Vals AI)78.1%14.8%GLM 5.3
Code migration (Vals AI)44.2%Not measuredOnly GLM 5.3 measured
ProgramBench (Vals AI)1.5%Not measuredOnly GLM 5.3 measured
IOI (Vals AI)68.4%Not measuredOnly GLM 5.3 measured
Terminal-Bench 2.1 (Vals AI)71.5%Not measuredOnly GLM 5.3 measured
Vibe Code Bench 1-100 (Vals AI)20.0%Not measuredOnly GLM 5.3 measured
SkillsBench (Vals AI)47.5%Not measuredOnly GLM 5.3 measured
CyberBench (Vals AI)72.1%Not measuredOnly GLM 5.3 measured
SciCode59.0%Not measuredOnly GLM 5.3 measured
Terminal-Bench Hard (Artificial Analysis run)Not measured34.8%Only MiniMax M2.5 measured
Terminal-BenchNot measured42.7%Only MiniMax M2.5 measured
FrontierCode40.1%Not measuredOnly GLM 5.3 measured
FrontierSWE30.2%Not measuredOnly GLM 5.3 measured
WeirdML75.4%Not measuredOnly GLM 5.3 measured
ALE-Bench1317.4618.2GLM 5.3
LMArena Text: coding14971380GLM 5.3
LMArena Text: software and IT14931383GLM 5.3
LMArena WebDev: React16411374GLM 5.3
LMArena WebDev: HTML15951414GLM 5.3
Neither model has a result on the other 12 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
Intelligence benchmarks for GLM 5.3 and MiniMax M2.5
BenchmarkGLM 5.3MiniMax M2.5Leader
AA Intelligence Index44.822.8GLM 5.3
Epoch Capabilities Index156.0146.6GLM 5.3
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for GLM 5.3 and MiniMax M2.5
BenchmarkGLM 5.3MiniMax M2.5Leader
AA Agentic Index53.1Not measuredOnly GLM 5.3 measured
Design Arena: agents building mobile apps1194Not measuredOnly GLM 5.3 measured
Design Arena: HTML slides1189Not measuredOnly GLM 5.3 measured
Design Arena: PowerPoint slides1230Not measuredOnly GLM 5.3 measured
LiveBench research73.2%Not measuredOnly GLM 5.3 measured
LiveBench browser0.0%Not measuredOnly GLM 5.3 measured
LiveBench long agentic35.9%Not measuredOnly GLM 5.3 measured
SREBench (Vals AI)9.2%Not measuredOnly GLM 5.3 measured
Finance Agent (Vals AI)55.8%Not measuredOnly GLM 5.3 measured
Tax Agent Bench (Vals AI)39.7%Not measuredOnly GLM 5.3 measured
Legal Research Bench (Vals AI)49.0%Not measuredOnly GLM 5.3 measured
Harvey's Legal Agent Benchmark (Vals AI)14.6%Not measuredOnly GLM 5.3 measured
Excel Modeling Benchmark (Vals AI)56.3%Not measuredOnly GLM 5.3 measured
Terminal-Bench-Science (Vals AI)5.7%Not measuredOnly GLM 5.3 measured
τ²-Bench TelecomNot measured95.3%Only MiniMax M2.5 measured
APEX-Agents56.6%Not measuredOnly GLM 5.3 measured
Vending-Bench 2$8,164-$23GLM 5.3
Neither model has a result on the other 24 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index60
Reasoning benchmarks for GLM 5.3 and MiniMax M2.5
BenchmarkGLM 5.3MiniMax M2.5Leader
GPQA Diamond88.1%82.1%GLM 5.3
LiveBench reasoning85.8%Not measuredOnly GLM 5.3 measured
MysteryMechanism (Vals AI)23.0%Not measuredOnly GLM 5.3 measured
GPQA Diamond (Artificial Analysis run)91.7%84.8%GLM 5.3
Humanity's Last Exam (Artificial Analysis run)42.3%20.5%GLM 5.3
GPQA Diamond (Epoch AI run)90.9%Not measuredOnly GLM 5.3 measured
ARC-AGINot measured63.7%Only MiniMax M2.5 measured
ARC-AGI-2Not measured4.9%Only MiniMax M2.5 measured
Chess puzzles37.0%Not measuredOnly GLM 5.3 measured
Mystery game puzzles33.0%Not measuredOnly GLM 5.3 measured
LMArena Text: hard prompts14901372GLM 5.3
LMArena Text: expert15141378GLM 5.3
LMArena Text: hard prompts in English14961384GLM 5.3
Kagi LLM BenchmarkNot measured55.2%Only MiniMax M2.5 measured
Neither model has a result on the other 8 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index30
Math benchmarks for GLM 5.3 and MiniMax M2.5
BenchmarkGLM 5.3MiniMax M2.5Leader
AIMENot measured88.8%Only MiniMax M2.5 measured
OTIS Mock AIME91.1%Not measuredOnly GLM 5.3 measured
FrontierMath, tiers 1 to 368.8%Not measuredOnly GLM 5.3 measured
FrontierMath, tier 429.3%Not measuredOnly GLM 5.3 measured
ProofBench49.0%4.0%GLM 5.3
LMArena Text: math14951378GLM 5.3
LMArena Text: mathematical work14971378GLM 5.3
Neither model has a result on the other 6 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index70
Knowledge benchmarks for GLM 5.3 and MiniMax M2.5
BenchmarkGLM 5.3MiniMax M2.5Leader
MMLU Pro86.8%80.1%GLM 5.3
LegalBench (Vals AI)84.8%80.0%GLM 5.3
CaseLaw (Vals AI)Not measured53.5%Only MiniMax M2.5 measured
CorpFin (Vals AI)Not measured59.6%Only MiniMax M2.5 measured
TaxEval (Vals AI)72.4%68.2%GLM 5.3
MedQA (Vals AI)Not measured92.5%Only MiniMax M2.5 measured
MedCode (Vals AI)42.9%Not measuredOnly GLM 5.3 measured
SimpleQA Verified41.0%Not measuredOnly GLM 5.3 measured
LMArena Text: science14981369GLM 5.3
LMArena Text: medicine and healthcare14811357GLM 5.3
LMArena Text: legal and government14731366GLM 5.3
LMArena Text: business and finance14591356GLM 5.3
Neither model has a result on the other 6 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index70
Web design benchmarks for GLM 5.3 and MiniMax M2.5
BenchmarkGLM 5.3MiniMax M2.5Leader
Design Arena: websites12951222GLM 5.3
Design Arena: UI components13141173GLM 5.3
Design Arena: game dev13111176GLM 5.3
Design Arena: data viz12601172GLM 5.3
Design Arena: 3D13321170GLM 5.3
Design Arena: all code categories13041207GLM 5.3
Design Arena: SVG12941150GLM 5.3
Design Arena: ASCII art1208Not measuredOnly GLM 5.3 measured
LiveBench design77.3%Not measuredOnly GLM 5.3 measured
GeneralLike-for-likePublic boards, shown as published, kept out of the index180
General benchmarks for GLM 5.3 and MiniMax M2.5
BenchmarkGLM 5.3MiniMax M2.5Leader
Vals Index59.7%Not measuredOnly GLM 5.3 measured
LiveBench data analysis70.2%Not measuredOnly GLM 5.3 measured
Public Benefits Bench (Vals AI)68.5%Not measuredOnly GLM 5.3 measured
MedScribe (Vals AI)88.8%Not measuredOnly GLM 5.3 measured
IFBenchNot measured71.6%Only MiniMax M2.5 measured
Long context reasoning (AA-LCR)79.7%73.3%GLM 5.3
DTBench87.7%Not measuredOnly GLM 5.3 measured
LMCA55.5%Not measuredOnly GLM 5.3 measured
CL-benchNot measured11.4%Only MiniMax M2.5 measured
CL-bench LifeNot measured6.3%Only MiniMax M2.5 measured
LMArena Text14741359GLM 5.3
LMArena Text: instruction following14791353GLM 5.3
LMArena Text: creative writing14581331GLM 5.3
LMArena Text: English14821380GLM 5.3
LMArena Text: non-English14591338GLM 5.3
LMArena Text: Chinese15321393GLM 5.3
LMArena Text: German14881361GLM 5.3
LMArena Text: Japanese14601171GLM 5.3
LMArena Text: Korean14601233GLM 5.3
LMArena Text: French15031363GLM 5.3
LMArena Text: Spanish14621353GLM 5.3
LMArena Text: Russian14691359GLM 5.3
LMArena Text: Polish14661334GLM 5.3
LMArena Text: multi-turn14721364GLM 5.3
LMArena Text: longer queries14831366GLM 5.3
LMArena Text: writing and language14691345GLM 5.3
LMArena Text: entertainment and media14471329GLM 5.3
Neither model has a result on the other 19 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 5.3 and MiniMax M2.5
BenchmarkGLM 5.3MiniMax 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 5.3$0.0036Fits in one request
MiniMax M2.5$0.0009Fits in one request

MiniMax M2.5 costs less

Repository review

50K input + 3K output tokens

GLM 5.3$0.083Fits in one request
MiniMax M2.5$0.019Fits in one request

MiniMax M2.5 costs less

Agent loop

200K input + 10K output tokens

GLM 5.3$0.324Fits in one request
MiniMax M2.5$0.072Exceeds 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 5.3 and MiniMax M2.5
SpecificationZ.ai GLM 5.3MiniMax M2.5
MakerZ.aiMiniMax
WeightsOpen weightsOpen weights
ReleasedAugust 18, 2026February 12, 2026
Context window1.05M205K
Input $/Mtok$1.40$0.30
Output $/Mtok$4.40$1.20
Coding index70.637.2
Independent measurements behind it63
Cost per solved task$0.36$0.62
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 5.3 better than MiniMax M2.5 for coding?

Pick GLM 5.3 for coding: it scores 70.6 on the Vibe Coding Index against 37.2 for MiniMax M2.5. Pick MiniMax M2.5 for output tokens 3.7x cheaper ($1.20 vs $4.40 per million).

Which is cheaper, GLM 5.3 or MiniMax M2.5?

GLM 5.3 costs $1.40 per million input tokens and $4.40 per million output tokens; MiniMax M2.5 costs $0.30 and $1.20. Per solved SWE-bench Verified task, GLM 5.3 costs $0.36 and MiniMax M2.5 $0.62.

Which has the bigger context window, GLM 5.3 or MiniMax M2.5?

GLM 5.3, with 1.05M tokens against 205K.

How many benchmarks is the GLM 5.3 vs MiniMax M2.5 comparison based on?

53 benchmarks have a result for both models as of October 22, 2026. GLM 5.3's coding index rests on 6 independent benchmarks and MiniMax M2.5's on 3. Where only one model has a result, the page says so instead of naming a winner.

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