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GLM 4.7 Flash vs MiniMax M2.5

MiniMax M2.5 leads GLM 4.7 Flash on the Artificial Analysis Intelligence Index, 22.8 to 14.9, and GLM 4.7 Flash costs 3.0x less per output token ($0.40 vs $1.20 per million). Updated October 22, 2026; 41 benchmarks have a result for both.

Replace GLM 4.7 Flash Replace MiniMax M2.5
GLM 4.7 FlashZ.ai
Coding
No data
Overall
42.1
Output $/M
$0.40
6
MiniMax M2.5MiniMax
Coding
37.2
Overall
37.3
Output $/M
$1.20
35
vs boards won of 41 shared

GLM 4.7 Flash or MiniMax M2.5: which to pick

Only MiniMax M2.5 has a coding score, 37.2 on the Vibe Coding Index; GLM 4.7 Flash has none, so the two cannot be ranked on coding.

Pick GLM 4.7 Flash for output tokens 3.0x cheaper ($0.40 vs $1.20 per million) and work measured by LMArena Text: coding or LMArena Text: software and IT, where it leads.

Model AGLM 4.7 FlashZ.aiNot benchmarkedNo coding benchmarkNo cost per solved taskModel BMiniMax M2.5MiniMax37.2/1003 independent benchmarks$0.62 per solved task
Shared results
41
GLM 4.7 Flash only
2
MiniMax M2.5 only
27
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 Flash not benchmarked, MiniMax M2.5 37.2 from 3 benchmarks.
Widest category
Reasoning, 9.6 points apart on average over 5 shared benchmarks.
Widest single gap
Long context reasoning (AA-LCR): GLM 4.7 Flash 41.7% against MiniMax M2.5 73.3%, 31.7 points apart on a 0 to 100 scale.
Output price
GLM 4.7 Flash $0.40 against MiniMax M2.5 $1.20 per million output tokens, 3.0x apart.
Context window
GLM 4.7 Flash takes 200K 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 4.7 FlashBarsMiniMax M2.5Ties
Coding21
Intelligence01
Agentic work10
Reasoning05
Math02
Knowledge04
Web design16
General216
All shared boards635
CodingLike-for-likeSix of these boards feed the Vibe Coding Index21
Coding benchmarks for GLM 4.7 Flash and MiniMax M2.5
BenchmarkGLM 4.7 FlashMiniMax M2.5Leader
SWE-bench VerifiedNot measured75.8%Only MiniMax M2.5 measured
SWE-bench MultilingualNot measured68.3%Only MiniMax M2.5 measured
LMArena WebDevNot measured1386Only MiniMax M2.5 measured
LiveCodeBench (Vals AI)Not measured79.2%Only MiniMax M2.5 measured
SWE-bench Verified (Vals AI)Not measured74.2%Only MiniMax M2.5 measured
Vibe Code Bench (Vals AI)Not measured14.8%Only MiniMax M2.5 measured
Terminal-Bench Hard (Artificial Analysis run)22.0%34.8%MiniMax M2.5
Terminal-BenchNot measured42.7%Only MiniMax M2.5 measured
ALE-BenchNot measured618.2Only MiniMax M2.5 measured
LMArena Text: coding13821380GLM 4.7 Flash
LMArena Text: software and IT13891383GLM 4.7 Flash
LMArena WebDev: ReactNot measured1374Only MiniMax M2.5 measured
LMArena WebDev: HTMLNot measured1414Only MiniMax M2.5 measured
Neither model has a result on the other 26 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for GLM 4.7 Flash and MiniMax M2.5
BenchmarkGLM 4.7 FlashMiniMax M2.5Leader
AA Intelligence Index14.922.8MiniMax M2.5
Epoch Capabilities IndexNot measured146.6Only MiniMax M2.5 measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for GLM 4.7 Flash and MiniMax M2.5
BenchmarkGLM 4.7 FlashMiniMax M2.5Leader
τ²-Bench Telecom98.8%95.3%GLM 4.7 Flash
Vending-Bench 2Not measured-$23Only MiniMax M2.5 measured
Neither model has a result on the other 39 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
Reasoning benchmarks for GLM 4.7 Flash and MiniMax M2.5
BenchmarkGLM 4.7 FlashMiniMax M2.5Leader
GPQA DiamondNot measured82.1%Only MiniMax M2.5 measured
GPQA Diamond (Artificial Analysis run)58.1%84.8%MiniMax M2.5
Humanity's Last Exam (Artificial Analysis run)7.6%20.5%MiniMax M2.5
GPQA Diamond (Epoch AI run)60.5%Not measuredOnly GLM 4.7 Flash measured
ARC-AGINot measured63.7%Only MiniMax M2.5 measured
ARC-AGI-2Not measured4.9%Only MiniMax M2.5 measured
LMArena Text: hard prompts13561372MiniMax M2.5
LMArena Text: expert13571378MiniMax M2.5
LMArena Text: hard prompts in English13691384MiniMax M2.5
Kagi LLM BenchmarkNot measured55.2%Only MiniMax M2.5 measured
Neither model has a result on the other 12 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for GLM 4.7 Flash and MiniMax M2.5
BenchmarkGLM 4.7 FlashMiniMax M2.5Leader
AIMENot measured88.8%Only MiniMax M2.5 measured
OTIS Mock AIME58.3%Not measuredOnly GLM 4.7 Flash measured
ProofBenchNot measured4.0%Only MiniMax M2.5 measured
LMArena Text: math13551378MiniMax M2.5
LMArena Text: mathematical work13691378MiniMax M2.5
Neither model has a result on the other 8 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for GLM 4.7 Flash and MiniMax M2.5
BenchmarkGLM 4.7 FlashMiniMax M2.5Leader
MMLU ProNot measured80.1%Only MiniMax M2.5 measured
LegalBench (Vals AI)Not measured80.0%Only MiniMax M2.5 measured
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)Not measured68.2%Only MiniMax M2.5 measured
MedQA (Vals AI)Not measured92.5%Only MiniMax M2.5 measured
LMArena Text: science13581369MiniMax M2.5
LMArena Text: medicine and healthcare13431357MiniMax M2.5
LMArena Text: legal and government13571366MiniMax M2.5
LMArena Text: business and finance13431356MiniMax M2.5
Neither model has a result on the other 8 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index16
Web design benchmarks for GLM 4.7 Flash and MiniMax M2.5
BenchmarkGLM 4.7 FlashMiniMax M2.5Leader
Design Arena: websites11941222MiniMax M2.5
Design Arena: UI components12051173GLM 4.7 Flash
Design Arena: game dev11341176MiniMax M2.5
Design Arena: data viz11231172MiniMax M2.5
Design Arena: 3D11201170MiniMax M2.5
Design Arena: all code categories11771207MiniMax M2.5
Design Arena: SVG10371150MiniMax M2.5
Neither model has a result on the other 2 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index216
General benchmarks for GLM 4.7 Flash and MiniMax M2.5
BenchmarkGLM 4.7 FlashMiniMax M2.5Leader
IFBench60.8%71.6%MiniMax M2.5
Long context reasoning (AA-LCR)41.7%73.3%MiniMax M2.5
CL-benchNot measured11.4%Only MiniMax M2.5 measured
CL-bench LifeNot measured6.3%Only MiniMax M2.5 measured
LMArena Text13511359MiniMax M2.5
LMArena Text: instruction following13271353MiniMax M2.5
LMArena Text: creative writing12961331MiniMax M2.5
LMArena Text: English13731380MiniMax M2.5
LMArena Text: non-English13301338MiniMax M2.5
LMArena Text: Chinese14031393GLM 4.7 Flash
LMArena Text: German13361361MiniMax M2.5
LMArena Text: JapaneseNot measured1171Only MiniMax M2.5 measured
LMArena Text: Korean12831233GLM 4.7 Flash
LMArena Text: French13321363MiniMax M2.5
LMArena Text: Spanish13501353MiniMax M2.5
LMArena Text: Russian13331359MiniMax M2.5
LMArena Text: Polish12911334MiniMax M2.5
LMArena Text: multi-turn13421364MiniMax M2.5
LMArena Text: longer queries13451366MiniMax M2.5
LMArena Text: writing and language13201345MiniMax M2.5
LMArena Text: entertainment and media13041329MiniMax M2.5
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 4.7 Flash and MiniMax M2.5
BenchmarkGLM 4.7 FlashMiniMax 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 4.7 Flash$0.0003Fits in one request
MiniMax M2.5$0.0009Fits in one request

GLM 4.7 Flash costs less

Repository review

50K input + 3K output tokens

GLM 4.7 Flash$0.0045Fits in one request
MiniMax M2.5$0.019Fits in one request

GLM 4.7 Flash costs less

Agent loop

200K input + 10K output tokens

GLM 4.7 Flash$0.017Exceeds the context window
MiniMax M2.5$0.072Exceeds the context window

GLM 4.7 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 4.7 Flash and MiniMax M2.5
SpecificationZ.ai GLM 4.7 FlashMiniMax M2.5
MakerZ.aiMiniMax
WeightsOpen weightsOpen weights
ReleasedJanuary 19, 2026February 12, 2026
Context window200K205K
Input $/Mtok$0.0653$0.30
Output $/Mtok$0.40$1.20
Coding indexNot benchmarked37.2
Independent measurements behind it03
Cost per solved taskNot computable$0.62
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.7 Flash better than MiniMax M2.5 for coding?

Only MiniMax M2.5 has a coding score, 37.2 on the Vibe Coding Index; GLM 4.7 Flash has none, so the two cannot be ranked on coding. Pick GLM 4.7 Flash for output tokens 3.0x cheaper ($0.40 vs $1.20 per million) and work measured by LMArena Text: coding or LMArena Text: software and IT, where it leads.

Which is cheaper, GLM 4.7 Flash or MiniMax M2.5?

GLM 4.7 Flash costs $0.0653 per million input tokens and $0.40 per million output tokens; MiniMax M2.5 costs $0.30 and $1.20. Cost per solved task is known for MiniMax M2.5 only.

Which has the bigger context window, GLM 4.7 Flash or MiniMax M2.5?

MiniMax M2.5, with 205K tokens against 200K.

How many benchmarks is the GLM 4.7 Flash vs MiniMax M2.5 comparison based on?

41 benchmarks have a result for both models as of October 22, 2026. GLM 4.7 Flash's coding index rests on 0 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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