Noometry

Model comparison

GLM-4.6 vs MiMo-V2-Omni

MiMo-V2-Omni is the stronger model overall, scoring 43.6 to 41.4 on the Noometry Index.

Last verified . 17 shared benchmarks.

GLM-4.6 Z.ai (Zhipu)

41.4

Rank #135 Confirmed

MiMo-V2-Omni Xiaomi

43.6

Rank #88 Confirmed

Summary

  • They share 17 benchmarks with published results for both. GLM-4.6 scores higher in 2 categories and MiMo-V2-Omni in 6 categories; 3 gaps are clear of the uncertainty.
  • The widest gap is in reasoning, where MiMo-V2-Omni leads 29.7 to 23.7.
  • MiMo-V2-Omni is cheaper at $0.14 / $0.28 per million input/output tokens, against $0.60 / $2.20 for GLM-4.6.
  • MiMo-V2-Omni accepts more context: 262K tokens versus 205K.
  • GLM-4.6 has downloadable open weights; the other is API-only.

Side by side

GLM-4.6MiMo-V2-Omni
ProviderZ.ai (Zhipu)Xiaomi
Noometry Index41.443.6
Released2025-09-302026-03-18
WeightsOpenProprietary
Context window205K262K
Max output131K131K
Input $ / M tokens$0.60$0.14
Output $ / M tokens$2.20$0.28
Results tracked2918

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Category by category

Coding MiMo-V2-Omni leads

GLM-4.6: 40.1 (#148), MiMo-V2-Omni: 43.3 (#89)

BenchmarkGLM-4.6MiMo-V2-Omni
LMArena Coding14491466
SWE-bench Verified (bash only)55.4%—
LMArena WebDev1340—
SciCode38.4%—
ALE-Bench340.82—

Agentic & Tool Use Not comparable

GLM-4.6: 32.3 (#66), MiMo-V2-Omni: —

BenchmarkGLM-4.6MiMo-V2-Omni
Terminal-Bench24.5%—
Berkeley Function Calling Leaderboard72.4%—

Reasoning MiMo-V2-Omni leads

GLM-4.6: 23.7 (#172), MiMo-V2-Omni: 29.7 (#88)

BenchmarkGLM-4.6MiMo-V2-Omni
LMArena Hard Prompts14401445
Kagi LLM Benchmark47.4%—
CritPt1.1%—

Math Too close to call

GLM-4.6: 39.1 (#111), MiMo-V2-Omni: 39.1 (#115)

BenchmarkGLM-4.6MiMo-V2-Omni
LMArena Math14321430
FrontierMath (Feb 2025 set)3.8%—
FrontierMath Tier 4 (v1)2.1%—

Knowledge Too close to call

GLM-4.6: 40.2 (#124), MiMo-V2-Omni: 40.5 (#118)

BenchmarkGLM-4.6MiMo-V2-Omni
LMArena Expert14311449
Vectara Hallucination Rate9.5%—

Multimodal Not comparable

GLM-4.6: —, MiMo-V2-Omni: 38.6 (#63)

BenchmarkGLM-4.6MiMo-V2-Omni
LMArena Vision—1228

Multilingual GLM-4.6 leads

GLM-4.6: 53.5 (#66), MiMo-V2-Omni: 51.8 (#102)

BenchmarkGLM-4.6MiMo-V2-Omni
LMArena Non-English14261404
LMArena Chinese14991465
LMArena French14591447
LMArena German14471399
LMArena Japanese13931317
LMArena Korean14001355
LMArena Russian14191412
LMArena Spanish14361434

Instruction Following Too close to call

GLM-4.6: 74.3 (#98), MiMo-V2-Omni: 75.2 (#66)

BenchmarkGLM-4.6MiMo-V2-Omni
LMArena Instruction Following14101428

Long Context Too close to call

GLM-4.6: 43.4 (#94), MiMo-V2-Omni: 44.1 (#76)

BenchmarkGLM-4.6MiMo-V2-Omni
LMArena Longer Query14221442

Writing & Preference Too close to call

GLM-4.6: 61.1 (#90), MiMo-V2-Omni: 61.4 (#87)

BenchmarkGLM-4.6MiMo-V2-Omni
LMArena Text14401423
LMArena Creative Writing14111392
LMArena Multi-Turn14271445
EQ-Bench Creative Writing1411—

Frequently asked questions

Is GLM-4.6 better than MiMo-V2-Omni?

MiMo-V2-Omni is the stronger model overall, scoring 43.6 to 41.4 on the Noometry Index.

Which is cheaper, GLM-4.6 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-4.6 lists at $0.60 and $2.20.

Is GLM-4.6 or MiMo-V2-Omni better for coding?

MiMo-V2-Omni scores higher on coding benchmarks: 43.3 versus 40.1 in the Noometry coding category.

Which has the bigger context window?

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

How many benchmarks do GLM-4.6 and MiMo-V2-Omni share?

17 benchmarks have published results for both models. GLM-4.6 has 29 scored results on Noometry and MiMo-V2-Omni has 18.

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