Noometry

Model comparison

GLM-4.7 vs MiMo-V2-Omni

MiMo-V2-Omni is the stronger model overall, scoring 43.6 to 42.0 on the Noometry Index.

Last verified . 17 shared benchmarks.

GLM-4.7 Z.ai (Zhipu)

42.0

Rank #124 Confirmed

MiMo-V2-Omni Xiaomi

43.6

Rank #88 Confirmed

Summary

  • They share 17 benchmarks with published results for both. GLM-4.7 scores higher in 3 categories and MiMo-V2-Omni in 5 categories; 3 gaps are clear of the uncertainty.
  • The widest gap is in knowledge, where GLM-4.7 leads 47.0 to 40.5.
  • 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 Index42.043.6
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: 44.0 (#79), MiMo-V2-Omni: 43.3 (#89)

BenchmarkGLM-4.7MiMo-V2-Omni
LMArena Coding14541466
LMArena WebDev1435—
SciCode45.1%—
ALE-Bench399.48—

Agentic & Tool Use Not comparable

GLM-4.7: 26.5 (#103), MiMo-V2-Omni: —

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

Reasoning MiMo-V2-Omni leads

GLM-4.7: 24.3 (#164), MiMo-V2-Omni: 29.7 (#88)

BenchmarkGLM-4.7MiMo-V2-Omni
LMArena Hard Prompts14431445
SimpleBench47.7%—
CritPt1.7%—
Chess Puzzles6%—
Epoch Capabilities Index143.51—

Math Too close to call

GLM-4.7: 38.6 (#135), MiMo-V2-Omni: 39.1 (#115)

BenchmarkGLM-4.7MiMo-V2-Omni
LMArena Math14231430
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 (#80), MiMo-V2-Omni: 40.5 (#118)

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: 38.6 (#63)

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

Multilingual Too close to call

GLM-4.7: 52.8 (#79), MiMo-V2-Omni: 51.8 (#102)

BenchmarkGLM-4.7MiMo-V2-Omni
LMArena Non-English14171404
LMArena Chinese14951465
LMArena French14321447
LMArena German14241399
LMArena Japanese14391317
LMArena Korean13991355
LMArena Russian14231412
LMArena Spanish14341434

Instruction Following Too close to call

GLM-4.7: 74.4 (#95), MiMo-V2-Omni: 75.2 (#66)

BenchmarkGLM-4.7MiMo-V2-Omni
LMArena Instruction Following14111428

Long Context MiMo-V2-Omni leads

GLM-4.7: 42.8 (#116), MiMo-V2-Omni: 44.1 (#76)

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

Writing & Preference Too close to call

GLM-4.7: 60.9 (#93), MiMo-V2-Omni: 61.4 (#87)

BenchmarkGLM-4.7MiMo-V2-Omni
LMArena Text14351423
LMArena Creative Writing14011392
LMArena Multi-Turn14461445
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 43.6 to 42.0 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 (44.0 vs 43.3); 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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