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GLM 5.3 Flash vs MiniMax M2.7

GLM 5.3 Flash leads MiniMax M2.7 on coding, 57.7 to 36.2 on the Vibe Coding Index, and GLM 5.3 Flash costs 2.4x less per output token ($0.50 vs $1.20 per million). Updated October 22, 2026; 64 benchmarks have a result for both.

Replace GLM 5.3 Flash Replace MiniMax M2.7
GLM 5.3 FlashZ.ai
Coding
57.7
Overall
66.0
Output $/M
$0.50
60
MiniMax M2.7MiniMax
Coding
36.2
Overall
44.5
Output $/M
$1.20
3
vs boards won of 64 shared, 1 tied

GLM 5.3 Flash or MiniMax M2.7: which to pick

Pick GLM 5.3 Flash for coding: it scores 57.7 on the Vibe Coding Index against 36.2 for MiniMax M2.7.

Pick MiniMax M2.7 for work measured by ALE-Bench or GPQA Diamond, where it leads.

Model AGLM 5.3 FlashZ.ai57.7/1006 independent benchmarks$0.02 per solved taskModel BMiniMax M2.7MiniMax36.2/1003 independent benchmarks$0.64 per solved task
Shared results
64
GLM 5.3 Flash only
38
MiniMax M2.7 only
10
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 Flash 57.7 from 6 benchmarks, MiniMax M2.7 36.2 from 3 benchmarks.
Widest category
Agentic work, 30.5 points apart on average over 5 shared benchmarks.
Widest single gap
AA Agentic Index: GLM 5.3 Flash 50.9 against MiniMax M2.7 15.3, 35.6 points apart on a 0 to 100 scale.
Output price
GLM 5.3 Flash $0.50 against MiniMax M2.7 $1.20 per million output tokens, 2.4x apart.
Context window
GLM 5.3 Flash 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 5.3 FlashBarsMiniMax M2.7Ties
Coding1211 tie
Intelligence20
Agentic work50
Reasoning51
Math30
Knowledge61
Web design80
General190
All shared boards6031 tie
CodingLike-for-likeSix of these boards feed the Vibe Coding Index121
Coding benchmarks for GLM 5.3 Flash and MiniMax M2.7
BenchmarkGLM 5.3 FlashMiniMax M2.7Leader
LiveBench coding80.6%Not measuredOnly GLM 5.3 Flash measured
LMArena WebDev16071399GLM 5.3 Flash
AA Coding Index71.552.6GLM 5.3 Flash
LiveCodeBench (Vals AI)80.5%79.9%GLM 5.3 Flash
SWE-bench Verified (Vals AI)92.0%73.8%GLM 5.3 Flash
Terminal-Bench 4 (Vals AI)37.4%Not measuredOnly GLM 5.3 Flash measured
Vibe Code Bench (Vals AI)30.8%11.9%GLM 5.3 Flash
Code migration (Vals AI)20.5%8.7%GLM 5.3 Flash
ProgramBench (Vals AI)0.0%0.0%Tie
IOI (Vals AI)52.5%Not measuredOnly GLM 5.3 Flash measured
Terminal-Bench 2.1 (Vals AI)62.9%48.7%GLM 5.3 Flash
Vibe Code Bench 1-100 (Vals AI)16.0%Not measuredOnly GLM 5.3 Flash measured
SkillsBench (Vals AI)40.2%Not measuredOnly GLM 5.3 Flash measured
CyberBench (Vals AI)67.9%Not measuredOnly GLM 5.3 Flash measured
SciCode51.6%50.1%GLM 5.3 Flash
Terminal-Bench Hard (Artificial Analysis run)Not measured39.4%Only MiniMax M2.7 measured
Terminal-BenchNot measured45.1%Only MiniMax M2.7 measured
FrontierCode31.8%Not measuredOnly GLM 5.3 Flash measured
FrontierSWE18.1%Not measuredOnly GLM 5.3 Flash measured
WeirdMLNot measured37.0%Only MiniMax M2.7 measured
ALE-Bench303.6599.3MiniMax M2.7
LMArena Text: coding15071454GLM 5.3 Flash
LMArena Text: software and IT15001446GLM 5.3 Flash
LMArena WebDev: React16241388GLM 5.3 Flash
LMArena WebDev: HTML15771401GLM 5.3 Flash
LMArena WebDev: from an image1578Not measuredOnly GLM 5.3 Flash measured
Neither model has a result on the other 13 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
Intelligence benchmarks for GLM 5.3 Flash and MiniMax M2.7
BenchmarkGLM 5.3 FlashMiniMax M2.7Leader
AA Intelligence Index41.822.8GLM 5.3 Flash
Epoch Capabilities Index153.1145.8GLM 5.3 Flash
Agentic workLike-for-likePublic boards, shown as published, kept out of the index50
Agentic work benchmarks for GLM 5.3 Flash and MiniMax M2.7
BenchmarkGLM 5.3 FlashMiniMax M2.7Leader
AA Agentic Index50.915.3GLM 5.3 Flash
LiveBench research70.1%Not measuredOnly GLM 5.3 Flash measured
LiveBench browser84.0%Not measuredOnly GLM 5.3 Flash measured
LiveBench long agentic28.4%Not measuredOnly GLM 5.3 Flash measured
SREBench (Vals AI)0.8%Not measuredOnly GLM 5.3 Flash measured
Finance Agent (Vals AI)57.9%27.9%GLM 5.3 Flash
Tax Agent Bench (Vals AI)31.6%6.9%GLM 5.3 Flash
Legal Research Bench (Vals AI)45.2%10.6%GLM 5.3 Flash
Harvey's Legal Agent Benchmark (Vals AI)10.4%Not measuredOnly GLM 5.3 Flash measured
Excel Modeling Benchmark (Vals AI)55.9%28.4%GLM 5.3 Flash
Terminal-Bench-Science (Vals AI)2.9%Not measuredOnly GLM 5.3 Flash measured
τ²-Bench TelecomNot measured84.8%Only MiniMax M2.7 measured
APEX-Agents52.8%Not measuredOnly GLM 5.3 Flash measured
Neither model has a result on the other 28 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index51
Reasoning benchmarks for GLM 5.3 Flash and MiniMax M2.7
BenchmarkGLM 5.3 FlashMiniMax M2.7Leader
GPQA Diamond86.4%86.6%MiniMax M2.7
LiveBench reasoning77.6%Not measuredOnly GLM 5.3 Flash measured
MMMU Pro (Vals AI)86.0%Not measuredOnly GLM 5.3 Flash measured
GPQA Diamond (Artificial Analysis run)91.2%87.4%GLM 5.3 Flash
Humanity's Last Exam (Artificial Analysis run)39.9%29.6%GLM 5.3 Flash
GPQA Diamond (Epoch AI run)90.2%Not measuredOnly GLM 5.3 Flash measured
ARC-AGI91.0%Not measuredOnly GLM 5.3 Flash measured
ARC-AGI-265.8%Not measuredOnly GLM 5.3 Flash measured
Chess puzzles21.0%Not measuredOnly GLM 5.3 Flash measured
Mystery game puzzles8.0%Not measuredOnly GLM 5.3 Flash measured
LMArena Text: hard prompts14901422GLM 5.3 Flash
LMArena Text: expert15111443GLM 5.3 Flash
LMArena Text: hard prompts in English14881441GLM 5.3 Flash
LMArena Vision: diagrams1317Not measuredOnly GLM 5.3 Flash measured
LMArena Vision: homework1318Not measuredOnly GLM 5.3 Flash measured
Neither model has a result on the other 7 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index30
Math benchmarks for GLM 5.3 Flash and MiniMax M2.7
BenchmarkGLM 5.3 FlashMiniMax M2.7Leader
AIMENot measured91.0%Only MiniMax M2.7 measured
OTIS Mock AIME93.9%Not measuredOnly GLM 5.3 Flash measured
FrontierMath, tiers 1 to 355.8%Not measuredOnly GLM 5.3 Flash measured
FrontierMath, tier 417.1%Not measuredOnly GLM 5.3 Flash measured
ProofBench21.0%3.0%GLM 5.3 Flash
LMArena Text: math15031421GLM 5.3 Flash
LMArena Text: mathematical work14971437GLM 5.3 Flash
Neither model has a result on the other 6 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index61
Knowledge benchmarks for GLM 5.3 Flash and MiniMax M2.7
BenchmarkGLM 5.3 FlashMiniMax M2.7Leader
MMLU Pro86.1%80.4%GLM 5.3 Flash
LegalBench (Vals AI)83.9%84.0%MiniMax M2.7
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)75.6%66.6%GLM 5.3 Flash
MedCode (Vals AI)Not measured34.4%Only MiniMax M2.7 measured
LMArena Text: science14841416GLM 5.3 Flash
LMArena Text: medicine and healthcare14751409GLM 5.3 Flash
LMArena Text: legal and government14731403GLM 5.3 Flash
LMArena Text: business and finance14601407GLM 5.3 Flash
Neither model has a result on the other 8 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index80
Web design benchmarks for GLM 5.3 Flash and MiniMax M2.7
BenchmarkGLM 5.3 FlashMiniMax M2.7Leader
Design Arena: websites12721243GLM 5.3 Flash
Design Arena: UI components13161210GLM 5.3 Flash
Design Arena: game dev12781209GLM 5.3 Flash
Design Arena: data viz12621233GLM 5.3 Flash
Design Arena: 3D13161195GLM 5.3 Flash
Design Arena: all code categories12821233GLM 5.3 Flash
Design Arena: SVG12851137GLM 5.3 Flash
Design Arena: ASCII art12671136GLM 5.3 Flash
LiveBench design77.2%Not measuredOnly GLM 5.3 Flash measured
GeneralLike-for-likePublic boards, shown as published, kept out of the index190
General benchmarks for GLM 5.3 Flash and MiniMax M2.7
BenchmarkGLM 5.3 FlashMiniMax M2.7Leader
Vals Index50.9%Not measuredOnly GLM 5.3 Flash measured
LiveBench data analysis76.4%Not measuredOnly GLM 5.3 Flash measured
MedScribe (Vals AI)88.9%79.9%GLM 5.3 Flash
IFBenchNot measured75.7%Only MiniMax M2.7 measured
Long context reasoning (AA-LCR)80.0%78.3%GLM 5.3 Flash
Surface Evolver Bench52.5%Not measuredOnly GLM 5.3 Flash measured
ExploitBenchNot measured13.3%Only MiniMax M2.7 measured
LMArena Text14711405GLM 5.3 Flash
LMArena Text: instruction following14781405GLM 5.3 Flash
LMArena Text: creative writing14411354GLM 5.3 Flash
LMArena Vision1294Not measuredOnly GLM 5.3 Flash measured
LMArena Text: English14741426GLM 5.3 Flash
LMArena Text: non-English14621382GLM 5.3 Flash
LMArena Text: Chinese15311442GLM 5.3 Flash
LMArena Text: German14621398GLM 5.3 Flash
LMArena Text: Japanese14441260GLM 5.3 Flash
LMArena Text: Korean14361314GLM 5.3 Flash
LMArena Text: French14911424GLM 5.3 Flash
LMArena Text: Spanish14751402GLM 5.3 Flash
LMArena Text: Russian14651383GLM 5.3 Flash
LMArena Text: Polish14501387GLM 5.3 Flash
LMArena Text: multi-turn14691412GLM 5.3 Flash
LMArena Text: longer queries14811418GLM 5.3 Flash
LMArena Text: writing and language14551376GLM 5.3 Flash
LMArena Text: entertainment and media14401360GLM 5.3 Flash
LMArena Vision: English1295Not measuredOnly GLM 5.3 Flash measured
LMArena Vision: Chinese1363Not measuredOnly GLM 5.3 Flash measured
LMArena Vision: reading text1299Not measuredOnly GLM 5.3 Flash measured
LMArena Vision: humor1279Not measuredOnly GLM 5.3 Flash measured
LMArena Vision: creative writing1270Not measuredOnly GLM 5.3 Flash measured
Neither model has a result on the other 16 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 5.3 Flash and MiniMax M2.7
BenchmarkGLM 5.3 FlashMiniMax 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 5.3 Flash$0.0004Fits in one request
MiniMax M2.7$0.0009Fits in one request

GLM 5.3 Flash costs less

Repository review

50K input + 3K output tokens

GLM 5.3 Flash$0.0090Fits in one request
MiniMax M2.7$0.019Fits in one request

GLM 5.3 Flash costs less

Agent loop

200K input + 10K output tokens

GLM 5.3 Flash$0.035Fits in one request
MiniMax M2.7$0.072Exceeds the context window

GLM 5.3 Flash 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 Flash and MiniMax M2.7
SpecificationZ.ai GLM 5.3 FlashMiniMax M2.7
MakerZ.aiMiniMax
WeightsOpen weightsOpen weights
ReleasedAugust 26, 2026March 18, 2026
Context window1.05M205K
Input $/Mtok$0.15$0.30
Output $/Mtok$0.50$1.20
Coding index57.736.2
Independent measurements behind it63
Cost per solved task$0.02$0.64
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 5.3 Flash better than MiniMax M2.7 for coding?

Pick GLM 5.3 Flash for coding: it scores 57.7 on the Vibe Coding Index against 36.2 for MiniMax M2.7. Pick MiniMax M2.7 for work measured by ALE-Bench or GPQA Diamond, where it leads.

Which is cheaper, GLM 5.3 Flash or MiniMax M2.7?

GLM 5.3 Flash costs $0.15 per million input tokens and $0.50 per million output tokens; MiniMax M2.7 costs $0.30 and $1.20. Per solved SWE-bench Verified task, GLM 5.3 Flash costs $0.02 and MiniMax M2.7 $0.64.

Which has the bigger context window, GLM 5.3 Flash or MiniMax M2.7?

GLM 5.3 Flash, with 1.05M tokens against 205K.

How many benchmarks is the GLM 5.3 Flash vs MiniMax M2.7 comparison based on?

64 benchmarks have a result for both models as of October 22, 2026. GLM 5.3 Flash's coding index rests on 6 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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<p style="font:13px/1.4 sans-serif;margin:6px 0 0">Source: <a href="https://llmbenchmark.ai/compare/glm-5-3-flash-vs-minimax-minimax-m2-7">LLMBenchmark.ai, GLM 5.3 Flash vs MiniMax M2.7</a></p>