Noometry

Model comparison

GLM-4.7 vs MiMo-V2-Omni

MiMo-V2-Omni is the stronger model overall, scoring 42.9 to 41.7 on the Noometry Index.

Last verified . 17 shared benchmarks.

GLM-4.7 Z.ai (Zhipu)

41.7

Rank #117 Confirmed

MiMo-V2-Omni Xiaomi

42.9

Rank #91 Confirmed

Summary

  • They share 17 benchmarks with published results for both. GLM-4.7 scores higher in 4 categories and MiMo-V2-Omni in 4 categories; 4 gaps are clear of the uncertainty.
  • The widest gap is in knowledge, where GLM-4.7 leads 47.0 to 39.9.
  • MiMo-V2-Omni is cheaper at $0.14 / $0.28 per million input/output tokens, against $0.60 / $2.20 for GLM-4.7.
  • MiMo-V2-Omni accepts more context: 262K tokens versus 205K.
  • GLM-4.7 has downloadable open weights; the other is API-only.

Side by side

GLM-4.7MiMo-V2-Omni
ProviderZ.ai (Zhipu)Xiaomi
Noometry Index41.742.9
Released2025-12-222026-03-18
WeightsOpenProprietary
Context window205K262K
Max output131K131K
Input $ / M tokens$0.60$0.14
Output $ / M tokens$2.20$0.28
Results tracked3618

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Category by category

Coding Too close to call

GLM-4.7: 43.6 (#74), MiMo-V2-Omni: 42.7 (#87)

BenchmarkGLM-4.7MiMo-V2-Omni
LMArena Coding14541467
LMArena WebDev1435—
SciCode45.1%—
ALE-Bench399.48—

Agentic & Tool Use Not comparable

GLM-4.7: 26.4 (#96), MiMo-V2-Omni: —

BenchmarkGLM-4.7MiMo-V2-Omni
Terminal-Bench33.4%—
Vending-Bench 22,377—

Reasoning MiMo-V2-Omni leads

GLM-4.7: 23.7 (#161), MiMo-V2-Omni: 28.6 (#86)

BenchmarkGLM-4.7MiMo-V2-Omni
LMArena Hard Prompts14431445
SimpleBench47.7%—
CritPt1.7%—
Chess Puzzles6%—
Epoch Capabilities Index143.53—

Math GLM-4.7 leads

GLM-4.7: 37.7 (#92), MiMo-V2-Omni: 36.4 (#112)

BenchmarkGLM-4.7MiMo-V2-Omni
LMArena Math14231432
OTIS Mock AIME 2024-202583.3%—
ProofBench6%—
FrontierMath (Feb 2025 set)2.4%—
FrontierMath Tier 4 (v1)0%—

Knowledge GLM-4.7 leads

GLM-4.7: 47.0 (#74), MiMo-V2-Omni: 39.9 (#114)

BenchmarkGLM-4.7MiMo-V2-Omni
LMArena Expert14241449
GPQA Diamond83.3%—
SimpleQA Verified32.2%—
Vectara Hallucination Rate11.7%—

Multimodal Not comparable

GLM-4.7: —, MiMo-V2-Omni: 37.9 (#62)

BenchmarkGLM-4.7MiMo-V2-Omni
LMArena Vision—1228

Multilingual Too close to call

GLM-4.7: 53.0 (#75), MiMo-V2-Omni: 52.0 (#97)

BenchmarkGLM-4.7MiMo-V2-Omni
LMArena Non-English14171404
LMArena Chinese14951469
LMArena French14321448
LMArena German14261398
LMArena Japanese14391318
LMArena Korean14011355
LMArena Russian14231412
LMArena Spanish14331432

Instruction Following Too close to call

GLM-4.7: 74.6 (#90), MiMo-V2-Omni: 75.4 (#62)

BenchmarkGLM-4.7MiMo-V2-Omni
LMArena Instruction Following14111428

Long Context MiMo-V2-Omni leads

GLM-4.7: 42.8 (#111), MiMo-V2-Omni: 44.1 (#72)

BenchmarkGLM-4.7MiMo-V2-Omni
LMArena Longer Query14321442
CL-bench15.9%—
CL-bench Life10.9%—

Writing & Preference Too close to call

GLM-4.7: 61.1 (#89), MiMo-V2-Omni: 61.6 (#83)

BenchmarkGLM-4.7MiMo-V2-Omni
LMArena Text14361424
LMArena Creative Writing14011393
LMArena Multi-Turn14461446
EQ-Bench Creative Writing1413—

Frequently asked questions

Is GLM-4.7 better than MiMo-V2-Omni?

MiMo-V2-Omni is the stronger model overall, scoring 42.9 to 41.7 on the Noometry Index.

Which is cheaper, GLM-4.7 or MiMo-V2-Omni?

MiMo-V2-Omni is cheaper. It lists at $0.14 per million input tokens and $0.28 per million output tokens; GLM-4.7 lists at $0.60 and $2.20.

Is GLM-4.7 or MiMo-V2-Omni better for coding?

They score almost the same on coding (43.6 vs 42.7); test both on your own repository before choosing.

Which has the bigger context window?

MiMo-V2-Omni does, with 262K tokens against 205K.

How many benchmarks do GLM-4.7 and MiMo-V2-Omni share?

17 benchmarks have published results for both models. GLM-4.7 has 36 scored results on Noometry and MiMo-V2-Omni has 18.

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