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GLM 4.7 vs MiMo-V2.5

Updated October 21, 2026. 58 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace GLM 4.7 Replace MiMo-V2.5
GLM 4.7Z.ai
Coding
33.6
Overall
22.2
Output $/M
$1.98
27
MiMo-V2.5Xiaomi
Coding
34.0
Overall
21.8
Output $/M
$0.238
31
vs boards won of 58 shared

Decision reading

GLM 4.7 and MiMo-V2.5 are level for coding, 33.6 against 34.0 on the Vibe Coding Index, so price and context decide.

Pick GLM 4.7 when what matters is LiveCodeBench (Vals AI) or LMArena WebDev: HTML, where it leads. Pick MiMo-V2.5 when output tokens cost 8.3x less, or when a solved task costs $0.01 against $0.29.

Model AGLM 4.7Z.ai33.6/1001 independent benchmark$0.29 per solved taskModel BMiMo-V2.5Xiaomi34.0/1001 independent benchmark$0.01 per solved task
Shared results
58
GLM 4.7 only
22
MiMo-V2.5 only
22
Like-for-like categories
8 of 9

Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.

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 33.6 from 1 benchmark, MiMo-V2.5 34.0 from 1 benchmark.
Widest category
Math, 6.2 points apart on average over 3 shared benchmarks.
Widest single gap
LMArena Text: Japanese: GLM 4.7 1439 against MiMo-V2.5 1306, 22.1 points apart on a 0 to 100 scale.
Output price
GLM 4.7 $1.98 against MiMo-V2.5 $0.238 per million output tokens, 8.3x apart.
Context window
GLM 4.7 takes 205K tokens, MiMo-V2.5 1.05M.

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 4.7BarsMiMo-V2.5Ties
Coding28
Intelligence10
Agentic work10
Reasoning24
Math03
Knowledge54
Web design17
General155
All shared boards2731
CodingLike-for-likeFour of these boards feed the Vibe Coding Index28
Coding benchmarks for GLM 4.7 and MiMo-V2.5
BenchmarkGLM 4.7MiMo-V2.5Leader
LMArena WebDev14351438MiMo-V2.5
AA Coding Index45.356.8MiMo-V2.5
LiveCodeBench (Vals AI)82.2%81.5%GLM 4.7
SWE-bench Verified (Vals AI)69.4%71.0%MiMo-V2.5
Vibe Code Bench (Vals AI)Not measured42.2%Only MiMo-V2.5 measured
Code migration (Vals AI)Not measured14.2%Only MiMo-V2.5 measured
Terminal-Bench 2.1 (Vals AI)Not measured60.7%Only MiMo-V2.5 measured
LiveCodeBench (Artificial Analysis run)89.4%Not measuredOnly GLM 4.7 measured
SciCodeNot measured43.9%Only MiMo-V2.5 measured
Terminal-Bench Hard (Artificial Analysis run)31.8%41.7%MiMo-V2.5
Terminal-Bench33.4%Not measuredOnly GLM 4.7 measured
ALE-Bench399.5514.0MiMo-V2.5
LMArena Text: coding14541469MiMo-V2.5
LMArena Text: software and IT14531463MiMo-V2.5
LMArena WebDev: React14201425MiMo-V2.5
LMArena WebDev: HTML14471429GLM 4.7
Neither model has a result on the other 22 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for GLM 4.7 and MiMo-V2.5
BenchmarkGLM 4.7MiMo-V2.5Leader
AA Intelligence Index22.221.8GLM 4.7
Epoch Capabilities Index143.6Not measuredOnly GLM 4.7 measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for GLM 4.7 and MiMo-V2.5
BenchmarkGLM 4.7MiMo-V2.5Leader
AA Agentic IndexNot measured15.8Only MiMo-V2.5 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 apps1118Not measuredOnly GLM 4.7 measured
Design Arena: agents building Godot games1058Not measuredOnly GLM 4.7 measured
Finance Agent (Vals AI)Not measured36.7%Only MiMo-V2.5 measured
Tax Agent Bench (Vals AI)Not measured7.8%Only MiMo-V2.5 measured
Legal Research Bench (Vals AI)Not measured9.1%Only MiMo-V2.5 measured
Excel Modeling Benchmark (Vals AI)Not measured55.1%Only MiMo-V2.5 measured
τ²-Bench Telecom95.9%90.6%GLM 4.7
Vending-Bench 2$2,377Not measuredOnly GLM 4.7 measured
Neither model has a result on the other 28 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index24
Reasoning benchmarks for GLM 4.7 and MiMo-V2.5
BenchmarkGLM 4.7MiMo-V2.5Leader
GPQA Diamond80.0%81.6%MiMo-V2.5
MMMU Pro (Vals AI)Not measured80.0%Only MiMo-V2.5 measured
GPQA Diamond (Artificial Analysis run)85.9%84.9%GLM 4.7
Humanity's Last Exam (Artificial Analysis run)27.4%27.2%GLM 4.7
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 prompts14431450MiMo-V2.5
LMArena Text: expert14241459MiMo-V2.5
LMArena Text: hard prompts in English14521461MiMo-V2.5
LMArena Vision: diagramsNot measured1269Only MiMo-V2.5 measured
LMArena Vision: homeworkNot measured1262Only MiMo-V2.5 measured
Neither model has a result on the other 9 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index03
Math benchmarks for GLM 4.7 and MiMo-V2.5
BenchmarkGLM 4.7MiMo-V2.5Leader
AIME93.3%Not measuredOnly GLM 4.7 measured
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%16.0%MiMo-V2.5
LMArena Text: math14241436MiMo-V2.5
LMArena Text: mathematical work14111451MiMo-V2.5
Neither model has a result on the other 5 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index54
Knowledge benchmarks for GLM 4.7 and MiMo-V2.5
BenchmarkGLM 4.7MiMo-V2.5Leader
MMLU Pro82.7%82.9%MiMo-V2.5
LegalBench (Vals AI)83.4%78.9%GLM 4.7
CaseLaw (Vals AI)54.9%Not measuredOnly GLM 4.7 measured
CorpFin (Vals AI)46.4%59.9%MiMo-V2.5
TaxEval (Vals AI)68.8%71.8%MiMo-V2.5
MortgageTax (Vals AI)Not measured59.3%Only MiMo-V2.5 measured
MedQA (Vals AI)93.7%Not measuredOnly GLM 4.7 measured
MedCode (Vals AI)32.8%31.9%GLM 4.7
MMLU Pro (Artificial Analysis run)85.6%Not measuredOnly GLM 4.7 measured
SimpleQA Verified32.2%Not measuredOnly GLM 4.7 measured
LMArena Text: science14571440GLM 4.7
LMArena Text: medicine and healthcare14691432GLM 4.7
LMArena Text: legal and government14441425GLM 4.7
LMArena Text: business and finance14291432MiMo-V2.5
Neither model has a result on the other 3 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index17
Web design benchmarks for GLM 4.7 and MiMo-V2.5
BenchmarkGLM 4.7MiMo-V2.5Leader
Design Arena: websites12261266MiMo-V2.5
Design Arena: UI components11991257MiMo-V2.5
Design Arena: game dev11891249MiMo-V2.5
Design Arena: data viz11951249MiMo-V2.5
Design Arena: 3D11911216MiMo-V2.5
Design Arena: all code categories12171260MiMo-V2.5
Design Arena: SVG11421178MiMo-V2.5
Design Arena: ASCII art11651141GLM 4.7
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index155
General benchmarks for GLM 4.7 and MiMo-V2.5
BenchmarkGLM 4.7MiMo-V2.5Leader
SAGE (Vals AI)Not measured43.3%Only MiMo-V2.5 measured
MedScribe (Vals AI)68.6%72.2%MiMo-V2.5
Vals Multimodal IndexNot measured52.8%Only MiMo-V2.5 measured
IFBench67.9%67.1%GLM 4.7
Long context reasoning (AA-LCR)71.0%73.0%MiMo-V2.5
CL-bench15.9%Not measuredOnly GLM 4.7 measured
CL-bench Life10.9%Not measuredOnly GLM 4.7 measured
LMArena Text14351428GLM 4.7
LMArena Text: instruction following14121435MiMo-V2.5
LMArena Text: creative writing14011393GLM 4.7
LMArena VisionNot measured1247Only MiMo-V2.5 measured
LMArena Text: English14531447GLM 4.7
LMArena Text: non-English14171405GLM 4.7
LMArena Text: Chinese14951469GLM 4.7
LMArena Text: German14261421GLM 4.7
LMArena Text: Japanese14391306GLM 4.7
LMArena Text: Korean13991361GLM 4.7
LMArena Text: French14321448MiMo-V2.5
LMArena Text: Spanish14341414GLM 4.7
LMArena Text: Russian14231396GLM 4.7
LMArena Text: Polish14351413GLM 4.7
LMArena Text: multi-turn14461445GLM 4.7
LMArena Text: longer queries14321445MiMo-V2.5
LMArena Text: writing and language14111409GLM 4.7
LMArena Text: entertainment and media14001399GLM 4.7
LMArena Vision: EnglishNot measured1259Only MiMo-V2.5 measured
LMArena Vision: ChineseNot measured1326Only MiMo-V2.5 measured
LMArena Vision: reading textNot measured1256Only MiMo-V2.5 measured
LMArena Vision: humorNot measured1226Only MiMo-V2.5 measured
LMArena Vision: recognizing thingsNot measured1189Only MiMo-V2.5 measured
LMArena Vision: creative writingNot measured1230Only MiMo-V2.5 measured
Neither model has a result on the other 15 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 MiMo-V2.5
BenchmarkGLM 4.7MiMo-V2.5Leader
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$0.0015Fits in one request
MiMo-V2.5$0.0002Fits in one request

MiMo-V2.5 costs less

Repository review

50K input + 3K output tokens

GLM 4.7$0.033Fits in one request
MiMo-V2.5$0.0067Fits in one request

MiMo-V2.5 costs less

Agent loop

200K input + 10K output tokens

GLM 4.7$0.128Exceeds the context window
MiMo-V2.5$0.026Fits in one request

MiMo-V2.5 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 4.7 and MiMo-V2.5
SpecificationZ.ai GLM 4.7Xiaomi MiMo-V2.5
MakerZ.aiXiaomi
WeightsOpen weightsOpen weights
ReleasedDecember 22, 2025April 22, 2026
Context window205K1.05M
Input $/Mtok$0.54$0.119
Output $/Mtok$1.98$0.238
Coding index33.634.0
Independent measurements behind it11
Cost per solved task$0.29$0.01
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.7 better than MiMo-V2.5 for coding?

GLM 4.7 and MiMo-V2.5 are level for coding, 33.6 against 34.0 on the Vibe Coding Index, so price and context decide. Pick GLM 4.7 when what matters is LiveCodeBench (Vals AI) or LMArena WebDev: HTML, where it leads. Pick MiMo-V2.5 when output tokens cost 8.3x less, or when a solved task costs $0.01 against $0.29.

Which is cheaper, GLM 4.7 or MiMo-V2.5?

GLM 4.7 costs $0.54 per million input tokens and $1.98 per million output tokens; MiMo-V2.5 costs $0.119 and $0.238. Per solved SWE-bench Verified task, GLM 4.7 costs $0.29 and MiMo-V2.5 $0.01.

Which has the bigger context window, GLM 4.7 or MiMo-V2.5?

MiMo-V2.5, with 1.05M tokens against 205K.

How many benchmarks is the GLM 4.7 vs MiMo-V2.5 comparison based on?

58 benchmarks have a result for both models as of October 21, 2026. GLM 4.7's coding index rests on 1 independent benchmark and MiMo-V2.5's on 1. Where only one model has a result, the page says so instead of naming a winner.

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