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GLM 5.2 vs MiMo-V2.5

GLM 5.2 leads MiMo-V2.5 on coding, 61.1 to 50.0 on the Vibe Coding Index, and MiMo-V2.5 costs 13.1x less per output token ($0.336 vs $4.40 per million). Updated October 21, 2026; 66 benchmarks have a result for both.

Replace GLM 5.2 Replace MiMo-V2.5
GLM 5.2Z.ai
Coding
61.1
Overall
62.5
Output $/M
$4.40
65
MiMo-V2.5Xiaomi
Coding
50.0
Overall
50.7
Output $/M
$0.336
1
vs boards won of 66 shared

GLM 5.2 or MiMo-V2.5: which to pick

Pick GLM 5.2 for coding: it scores 61.1 on the Vibe Coding Index against 50.0 for MiMo-V2.5.

Pick MiMo-V2.5 for output tokens 13.1x cheaper ($0.336 vs $4.40 per million) and a lower cost per solved task ($0.01 against $0.86).

Model AGLM 5.2Z.ai61.1/1004 independent benchmarks$0.86 per solved taskModel BMiMo-V2.5Xiaomi50.0/1003 independent benchmarks$0.01 per solved task
Shared results
66
GLM 5.2 only
32
MiMo-V2.5 only
14
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, MiMo-V2.5 50.0 from 3 benchmarks.
Widest category
Coding, 14.6 points apart on average over 14 shared benchmarks.
Widest single gap
LMArena WebDev: React: GLM 5.2 1617 against MiMo-V2.5 1425, 29.2 points apart on a 0 to 100 scale.
Output price
GLM 5.2 $4.40 against MiMo-V2.5 $0.336 per million output tokens, 13.1x apart.
Context window
GLM 5.2 takes 1.05M 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 5.2BarsMiMo-V2.5Ties
Coding131
Intelligence10
Agentic work50
Reasoning60
Math30
Knowledge90
Web design80
General200
All shared boards651
CodingLike-for-likeSix of these boards feed the Vibe Coding Index131
Coding benchmarks for GLM 5.2 and MiMo-V2.5
BenchmarkGLM 5.2MiMo-V2.5Leader
LMArena WebDev16041438GLM 5.2
AA Coding Index68.856.8GLM 5.2
LiveCodeBench (Vals AI)69.5%81.5%MiMo-V2.5
SWE-bench Verified (Vals AI)82.8%71.0%GLM 5.2
Vibe Code Bench (Vals AI)64.0%42.2%GLM 5.2
Code migration (Vals AI)37.9%14.2%GLM 5.2
ProgramBench (Vals AI)0.5%Not measuredOnly GLM 5.2 measured
Terminal-Bench 2.1 (Vals AI)67.8%60.7%GLM 5.2
SkillsBench (Vals AI)45.1%Not measuredOnly GLM 5.2 measured
SciCode51.2%43.9%GLM 5.2
Terminal-Bench Hard (Artificial Analysis run)50.8%41.7%GLM 5.2
FrontierCode24.5%Not measuredOnly GLM 5.2 measured
WeirdML70.1%Not measuredOnly GLM 5.2 measured
ALE-Bench1047.0514.0GLM 5.2
LMArena Text: coding14871469GLM 5.2
LMArena Text: software and IT14861463GLM 5.2
LMArena WebDev: React16171425GLM 5.2
LMArena WebDev: HTML15651429GLM 5.2
Neither model has a result on the other 21 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for GLM 5.2 and MiMo-V2.5
BenchmarkGLM 5.2MiMo-V2.5Leader
AA Intelligence Index33.721.8GLM 5.2
Epoch Capabilities Index151.9Not measuredOnly GLM 5.2 measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index50
Agentic work benchmarks for GLM 5.2 and MiMo-V2.5
BenchmarkGLM 5.2MiMo-V2.5Leader
AA Agentic Index38.415.8GLM 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 apps1160Not measuredOnly GLM 5.2 measured
Design Arena: agents building Android apps1178Not 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 slides1175Not measuredOnly GLM 5.2 measured
Design Arena: PowerPoint slides1195Not measuredOnly GLM 5.2 measured
SREBench (Vals AI)0.0%Not measuredOnly GLM 5.2 measured
Finance Agent (Vals AI)49.7%36.7%GLM 5.2
Tax Agent Bench (Vals AI)Not measured7.8%Only MiMo-V2.5 measured
Legal Research Bench (Vals AI)31.3%9.1%GLM 5.2
Excel Modeling Benchmark (Vals AI)61.5%55.1%GLM 5.2
τ²-Bench Telecom99.1%90.6%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
Neither model has a result on the other 23 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index60
Reasoning benchmarks for GLM 5.2 and MiMo-V2.5
BenchmarkGLM 5.2MiMo-V2.5Leader
GPQA Diamond85.6%81.6%GLM 5.2
MMMU Pro (Vals AI)Not measured80.0%Only MiMo-V2.5 measured
GPQA Diamond (Artificial Analysis run)89.5%84.9%GLM 5.2
Humanity's Last Exam (Artificial Analysis run)41.1%27.2%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 prompts14811450GLM 5.2
LMArena Text: expert14871459GLM 5.2
LMArena Text: hard prompts in English14811461GLM 5.2
LMArena Vision: diagramsNot measured1269Only MiMo-V2.5 measured
LMArena Vision: homeworkNot measured1262Only MiMo-V2.5 measured
Kagi LLM Benchmark62.6%Not measuredOnly GLM 5.2 measured
Neither model has a result on the other 5 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index30
Math benchmarks for GLM 5.2 and MiMo-V2.5
BenchmarkGLM 5.2MiMo-V2.5Leader
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%16.0%GLM 5.2
LMArena Text: math14831436GLM 5.2
LMArena Text: mathematical work14891451GLM 5.2
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index90
Knowledge benchmarks for GLM 5.2 and MiMo-V2.5
BenchmarkGLM 5.2MiMo-V2.5Leader
MMLU Pro86.7%82.9%GLM 5.2
LegalBench (Vals AI)84.1%78.9%GLM 5.2
CorpFin (Vals AI)66.1%59.9%GLM 5.2
TaxEval (Vals AI)73.3%71.8%GLM 5.2
MortgageTax (Vals AI)Not measured59.3%Only MiMo-V2.5 measured
MedCode (Vals AI)40.8%31.9%GLM 5.2
SimpleQA Verified34.2%Not measuredOnly GLM 5.2 measured
LMArena Text: science14891440GLM 5.2
LMArena Text: medicine and healthcare14751432GLM 5.2
LMArena Text: legal and government14861425GLM 5.2
LMArena Text: business and finance14511432GLM 5.2
Neither model has a result on the other 7 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index80
Web design benchmarks for GLM 5.2 and MiMo-V2.5
BenchmarkGLM 5.2MiMo-V2.5Leader
Design Arena: websites12851266GLM 5.2
Design Arena: UI components12961258GLM 5.2
Design Arena: game dev12681249GLM 5.2
Design Arena: data viz12851249GLM 5.2
Design Arena: 3D12881216GLM 5.2
Design Arena: all code categories12881260GLM 5.2
Design Arena: SVG12141178GLM 5.2
Design Arena: ASCII art11901140GLM 5.2
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index200
General benchmarks for GLM 5.2 and MiMo-V2.5
BenchmarkGLM 5.2MiMo-V2.5Leader
SAGE (Vals AI)Not measured43.3%Only MiMo-V2.5 measured
MedScribe (Vals AI)83.5%72.2%GLM 5.2
Vals Multimodal IndexNot measured52.8%Only MiMo-V2.5 measured
IFBench73.3%67.1%GLM 5.2
Long context reasoning (AA-LCR)78.3%73.0%GLM 5.2
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
LMArena Text14701428GLM 5.2
LMArena Text: instruction following14661435GLM 5.2
LMArena Text: creative writing14631393GLM 5.2
LMArena VisionNot measured1247Only MiMo-V2.5 measured
LMArena Text: English14751447GLM 5.2
LMArena Text: non-English14591405GLM 5.2
LMArena Text: Chinese15221469GLM 5.2
LMArena Text: German14701421GLM 5.2
LMArena Text: Japanese14501306GLM 5.2
LMArena Text: Korean14401361GLM 5.2
LMArena Text: French14791448GLM 5.2
LMArena Text: Spanish14781414GLM 5.2
LMArena Text: Russian14681396GLM 5.2
LMArena Text: Polish14561413GLM 5.2
LMArena Text: multi-turn14691445GLM 5.2
LMArena Text: longer queries14801445GLM 5.2
LMArena Text: writing and language14631409GLM 5.2
LMArena Text: entertainment and media14501399GLM 5.2
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 14 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 MiMo-V2.5
BenchmarkGLM 5.2MiMo-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 5.2$0.0036Fits in one request
MiMo-V2.5$0.0003Fits in one request

MiMo-V2.5 costs less

Repository review

50K input + 3K output tokens

GLM 5.2$0.083Fits in one request
MiMo-V2.5$0.0094Fits in one request

MiMo-V2.5 costs less

Agent loop

200K input + 10K output tokens

GLM 5.2$0.324Fits in one request
MiMo-V2.5$0.037Fits 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 5.2 and MiMo-V2.5
SpecificationZ.ai GLM 5.2Xiaomi MiMo-V2.5
MakerZ.aiXiaomi
WeightsOpen weightsOpen weights
ReleasedJune 16, 2026April 22, 2026
Context window1.05M1.05M
Input $/Mtok$1.40$0.168
Output $/Mtok$4.40$0.336
Coding index61.150.0
Independent measurements behind it43
Cost per solved task$0.86$0.01
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 5.2 better than MiMo-V2.5 for coding?

Pick GLM 5.2 for coding: it scores 61.1 on the Vibe Coding Index against 50.0 for MiMo-V2.5. Pick MiMo-V2.5 for output tokens 13.1x cheaper ($0.336 vs $4.40 per million) and a lower cost per solved task ($0.01 against $0.86).

Which is cheaper, GLM 5.2 or MiMo-V2.5?

GLM 5.2 costs $1.40 per million input tokens and $4.40 per million output tokens; MiMo-V2.5 costs $0.168 and $0.336. Per solved SWE-bench Verified task, GLM 5.2 costs $0.86 and MiMo-V2.5 $0.01.

Which has the bigger context window, GLM 5.2 or MiMo-V2.5?

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

How many benchmarks is the GLM 5.2 vs MiMo-V2.5 comparison based on?

66 benchmarks have a result for both models as of October 21, 2026. GLM 5.2's coding index rests on 4 independent benchmarks and MiMo-V2.5'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-5-2-vs-xiaomi-mimo-v2-5">LLMBenchmark.ai, GLM 5.2 vs MiMo-V2.5</a></p>