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GLM 4.6 vs MiMo-V2.5-Pro

MiMo-V2.5-Pro leads GLM 4.6 on coding, 31.6 to 8.8 on the Vibe Coding Index, and MiMo-V2.5-Pro costs 2.4x less per output token ($0.9152 vs $2.20 per million). Updated October 22, 2026; 53 benchmarks have a result for both.

Replace GLM 4.6 Replace MiMo-V2.5-Pro
GLM 4.6Z.ai
Coding
8.8
Overall
33.4
Output $/M
$2.20
1
MiMo-V2.5-ProXiaomi
Coding
31.6
Overall
39.3
Output $/M
$0.9152
52
vs boards won of 53 shared

GLM 4.6 or MiMo-V2.5-Pro: which to pick

Pick MiMo-V2.5-Pro for coding: it scores 31.6 on the Vibe Coding Index against 8.8 for GLM 4.6.

Pick GLM 4.6 for work measured by LegalBench (Vals AI), where it leads.

Model AGLM 4.6Z.ai8.8/1002 independent benchmarksNo cost per solved taskModel BMiMo-V2.5-ProXiaomi31.6/1004 independent benchmarks$0.03 per solved task
Shared results
53
GLM 4.6 only
16
MiMo-V2.5-Pro only
17
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 4.6 8.8 from 2 benchmarks, MiMo-V2.5-Pro 31.6 from 4 benchmarks.
Widest category
Agentic work, 17.3 points apart on average over 1 shared benchmark.
Widest single gap
IFBench: GLM 4.6 43.4% against MiMo-V2.5-Pro 79.9%, 36.5 points apart on a 0 to 100 scale.
Output price
GLM 4.6 $2.20 against MiMo-V2.5-Pro $0.9152 per million output tokens, 2.4x apart.
Context window
GLM 4.6 takes 205K tokens, MiMo-V2.5-Pro 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 4.6BarsMiMo-V2.5-ProTies
Coding09
Intelligence01
Agentic work01
Reasoning06
Math02
Knowledge17
Web design07
General019
All shared boards152
CodingLike-for-likeSix of these boards feed the Vibe Coding Index09
Coding benchmarks for GLM 4.6 and MiMo-V2.5-Pro
BenchmarkGLM 4.6MiMo-V2.5-ProLeader
SWE-bench Verified68.2%Not measuredOnly GLM 4.6 measured
LMArena WebDev13411480MiMo-V2.5-Pro
AA Coding Index45.860.2MiMo-V2.5-Pro
LiveCodeBench (Vals AI)81.0%81.3%MiMo-V2.5-Pro
SWE-bench Verified (Vals AI)Not measured74.0%Only MiMo-V2.5-Pro measured
Terminal-Bench 4 (Vals AI)Not measured0.5%Only MiMo-V2.5-Pro measured
Vibe Code Bench (Vals AI)3.1%34.1%MiMo-V2.5-Pro
Code migration (Vals AI)Not measured21.6%Only MiMo-V2.5-Pro measured
Terminal-Bench 2.1 (Vals AI)Not measured57.3%Only MiMo-V2.5-Pro measured
LiveCodeBench (Artificial Analysis run)69.5%Not measuredOnly GLM 4.6 measured
SciCodeNot measured50.6%Only MiMo-V2.5-Pro measured
Terminal-Bench Hard (Artificial Analysis run)28.8%43.2%MiMo-V2.5-Pro
Terminal-Bench24.5%Not measuredOnly GLM 4.6 measured
ALE-Bench340.8899.8MiMo-V2.5-Pro
LMArena Text: coding14491501MiMo-V2.5-Pro
LMArena Text: software and IT14551495MiMo-V2.5-Pro
LMArena WebDev: ReactNot measured1469Only MiMo-V2.5-Pro measured
LMArena WebDev: HTML13501473MiMo-V2.5-Pro
Neither model has a result on the other 21 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for GLM 4.6 and MiMo-V2.5-Pro
BenchmarkGLM 4.6MiMo-V2.5-ProLeader
AA Intelligence Index18.526.0MiMo-V2.5-Pro
Neither model has a result on the other 1 board here
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for GLM 4.6 and MiMo-V2.5-Pro
BenchmarkGLM 4.6MiMo-V2.5-ProLeader
AA Agentic IndexNot measured21.3Only MiMo-V2.5-Pro measured
Design Arena: agents building full-stack apps1029Not measuredOnly GLM 4.6 measured
Design Arena: agents building mobile apps1096Not measuredOnly GLM 4.6 measured
Design Arena: agents building Android apps1084Not measuredOnly GLM 4.6 measured
Design Arena: agents building Godot games1180Not measuredOnly GLM 4.6 measured
Finance Agent (Vals AI)Not measured41.5%Only MiMo-V2.5-Pro measured
Tax Agent Bench (Vals AI)Not measured11.5%Only MiMo-V2.5-Pro measured
Legal Research Bench (Vals AI)Not measured15.9%Only MiMo-V2.5-Pro measured
Excel Modeling Benchmark (Vals AI)Not measured55.2%Only MiMo-V2.5-Pro measured
τ²-Bench Telecom76.9%94.2%MiMo-V2.5-Pro
Berkeley Function Calling Leaderboard (BFCL)72.4%Not measuredOnly GLM 4.6 measured
BrowseComp-Plus48.2%Not measuredOnly GLM 4.6 measured
Neither model has a result on the other 29 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index06
Reasoning benchmarks for GLM 4.6 and MiMo-V2.5-Pro
BenchmarkGLM 4.6MiMo-V2.5-ProLeader
GPQA Diamond74.5%82.6%MiMo-V2.5-Pro
GPQA Diamond (Artificial Analysis run)78.0%86.6%MiMo-V2.5-Pro
Humanity's Last Exam (Artificial Analysis run)14.5%35.7%MiMo-V2.5-Pro
LMArena Text: hard prompts14401486MiMo-V2.5-Pro
LMArena Text: expert14321501MiMo-V2.5-Pro
LMArena Text: hard prompts in English14451494MiMo-V2.5-Pro
Kagi LLM Benchmark45.7%Not measuredOnly GLM 4.6 measured
Neither model has a result on the other 15 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for GLM 4.6 and MiMo-V2.5-Pro
BenchmarkGLM 4.6MiMo-V2.5-ProLeader
AIME92.7%Not measuredOnly GLM 4.6 measured
MGSM (Vals AI)89.8%Not measuredOnly GLM 4.6 measured
AA Math Index86.0Not measuredOnly GLM 4.6 measured
AIME 2025 (Artificial Analysis run)86.0%Not measuredOnly GLM 4.6 measured
ProofBenchNot measured22.0%Only MiMo-V2.5-Pro measured
LMArena Text: math14321481MiMo-V2.5-Pro
LMArena Text: mathematical work14471491MiMo-V2.5-Pro
Neither model has a result on the other 6 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index17
Knowledge benchmarks for GLM 4.6 and MiMo-V2.5-Pro
BenchmarkGLM 4.6MiMo-V2.5-ProLeader
MMLU Pro82.2%84.6%MiMo-V2.5-Pro
LegalBench (Vals AI)79.6%77.1%GLM 4.6
CorpFin (Vals AI)56.8%61.4%MiMo-V2.5-Pro
TaxEval (Vals AI)66.2%73.8%MiMo-V2.5-Pro
MedQA (Vals AI)92.2%Not measuredOnly GLM 4.6 measured
MedCode (Vals AI)Not measured32.5%Only MiMo-V2.5-Pro measured
MMLU Pro (Artificial Analysis run)82.9%Not measuredOnly GLM 4.6 measured
LMArena Text: science14561482MiMo-V2.5-Pro
LMArena Text: medicine and healthcare14551473MiMo-V2.5-Pro
LMArena Text: legal and government14481474MiMo-V2.5-Pro
LMArena Text: business and finance14161466MiMo-V2.5-Pro
Neither model has a result on the other 7 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index07
Web design benchmarks for GLM 4.6 and MiMo-V2.5-Pro
BenchmarkGLM 4.6MiMo-V2.5-ProLeader
Design Arena: websites11741266MiMo-V2.5-Pro
Design Arena: UI components11451246MiMo-V2.5-Pro
Design Arena: game dev11501261MiMo-V2.5-Pro
Design Arena: data viz11611257MiMo-V2.5-Pro
Design Arena: 3D11281243MiMo-V2.5-Pro
Design Arena: all code categories11651265MiMo-V2.5-Pro
Design Arena: SVG11081181MiMo-V2.5-Pro
Design Arena: ASCII artNot measured1156Only MiMo-V2.5-Pro measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index019
General benchmarks for GLM 4.6 and MiMo-V2.5-Pro
BenchmarkGLM 4.6MiMo-V2.5-ProLeader
MedScribe (Vals AI)Not measured83.7%Only MiMo-V2.5-Pro measured
IFBench43.4%79.9%MiMo-V2.5-Pro
Long context reasoning (AA-LCR)54.0%79.7%MiMo-V2.5-Pro
DTBenchNot measured84.5%Only MiMo-V2.5-Pro measured
LMCANot measured29.5%Only MiMo-V2.5-Pro measured
LMArena Text14401464MiMo-V2.5-Pro
LMArena Text: instruction following14101475MiMo-V2.5-Pro
LMArena Text: creative writing14121438MiMo-V2.5-Pro
LMArena Text: English14491477MiMo-V2.5-Pro
LMArena Text: non-English14261448MiMo-V2.5-Pro
LMArena Text: Chinese14991507MiMo-V2.5-Pro
LMArena Text: German14471459MiMo-V2.5-Pro
LMArena Text: Japanese13931410MiMo-V2.5-Pro
LMArena Text: Korean14001435MiMo-V2.5-Pro
LMArena Text: French14591487MiMo-V2.5-Pro
LMArena Text: Spanish14361469MiMo-V2.5-Pro
LMArena Text: Russian14181449MiMo-V2.5-Pro
LMArena Text: Polish14471464MiMo-V2.5-Pro
LMArena Text: multi-turn14271476MiMo-V2.5-Pro
LMArena Text: longer queries14221481MiMo-V2.5-Pro
LMArena Text: writing and language14151451MiMo-V2.5-Pro
LMArena Text: entertainment and media14091438MiMo-V2.5-Pro
Neither model has a result on the other 24 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 4.6 and MiMo-V2.5-Pro
BenchmarkGLM 4.6MiMo-V2.5-ProLeader
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 4.6$0.0017Fits in one request
MiMo-V2.5-Pro$0.0009Fits in one request

MiMo-V2.5-Pro costs less

Repository review

50K input + 3K output tokens

GLM 4.6$0.037Fits in one request
MiMo-V2.5-Pro$0.026Fits in one request

MiMo-V2.5-Pro costs less

Agent loop

200K input + 10K output tokens

GLM 4.6$0.142Exceeds the context window
MiMo-V2.5-Pro$0.101Fits in one request

MiMo-V2.5-Pro 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 4.6 and MiMo-V2.5-Pro
SpecificationZ.ai GLM 4.6Xiaomi MiMo-V2.5-Pro
MakerZ.aiXiaomi
WeightsOpen weightsOpen weights
ReleasedSeptember 30, 2025April 22, 2026
Context window205K1.05M
Input $/Mtok$0.60$0.4575
Output $/Mtok$2.20$0.9152
Coding index8.831.6
Independent measurements behind it24
Cost per solved taskNot computable$0.03
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.6 better than MiMo-V2.5-Pro for coding?

Pick MiMo-V2.5-Pro for coding: it scores 31.6 on the Vibe Coding Index against 8.8 for GLM 4.6. Pick GLM 4.6 for work measured by LegalBench (Vals AI), where it leads.

Which is cheaper, GLM 4.6 or MiMo-V2.5-Pro?

GLM 4.6 costs $0.60 per million input tokens and $2.20 per million output tokens; MiMo-V2.5-Pro costs $0.4575 and $0.9152. Cost per solved task is known for MiMo-V2.5-Pro only.

Which has the bigger context window, GLM 4.6 or MiMo-V2.5-Pro?

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

How many benchmarks is the GLM 4.6 vs MiMo-V2.5-Pro comparison based on?

53 benchmarks have a result for both models as of October 22, 2026. GLM 4.6's coding index rests on 2 independent benchmarks and MiMo-V2.5-Pro's on 4. 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-4-6-vs-xiaomi-mimo-v2-5-pro">LLMBenchmark.ai, GLM 4.6 vs MiMo-V2.5-Pro</a></p>