Noometry

Model comparison

GLM-4.6 vs MiMo-V2-Pro

MiMo-V2-Pro is the stronger model overall, scoring 43.0 to 41.4 on the Noometry Index.

Last verified . 19 shared benchmarks.

GLM-4.6 Z.ai (Zhipu)

41.4

Rank #135 Confirmed

MiMo-V2-Pro Xiaomi

43.0

Rank #103 Confirmed

Summary

  • They share 19 benchmarks with published results for both. GLM-4.6 scores higher in 3 categories and MiMo-V2-Pro in 5 categories; 6 gaps are clear of the uncertainty.
  • The widest gap is in coding, where MiMo-V2-Pro leads 43.8 to 40.1.
  • MiMo-V2-Pro is cheaper at $0.43 / $0.87 per million input/output tokens, against $0.60 / $2.20 for GLM-4.6.
  • MiMo-V2-Pro accepts more context: 1.05M tokens versus 205K.
  • GLM-4.6 has downloadable open weights; the other is API-only.

Side by side

GLM-4.6MiMo-V2-Pro
ProviderZ.ai (Zhipu)Xiaomi
Noometry Index41.443.0
Released2025-09-302026-03-18
WeightsOpenProprietary
Context window205K1.05M
Max output131K131K
Input $ / M tokens$0.60$0.43
Output $ / M tokens$2.20$0.87
Results tracked2923

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Category by category

Coding MiMo-V2-Pro leads

GLM-4.6: 40.1 (#148), MiMo-V2-Pro: 43.8 (#83)

BenchmarkGLM-4.6MiMo-V2-Pro
LMArena WebDev13401433
LMArena Coding14491476
ALE-Bench340.82785.17
SWE-bench Verified (bash only)55.4%—
SciCode38.4%—

Agentic & Tool Use Not comparable

GLM-4.6: 32.3 (#66), MiMo-V2-Pro: —

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

Reasoning GLM-4.6 leads

GLM-4.6: 23.7 (#172), MiMo-V2-Pro: 22.1 (#206)

BenchmarkGLM-4.6MiMo-V2-Pro
LMArena Hard Prompts14401457
Kagi LLM Benchmark47.4%—
NYT Connections (extended)—25.8%
CritPt1.1%—
Thematic Generalization—45.9%

Math Too close to call

GLM-4.6: 39.1 (#111), MiMo-V2-Pro: 39.5 (#102)

BenchmarkGLM-4.6MiMo-V2-Pro
LMArena Math14321447
FrontierMath (Feb 2025 set)3.8%—
FrontierMath Tier 4 (v1)2.1%—

Knowledge MiMo-V2-Pro leads

GLM-4.6: 40.2 (#124), MiMo-V2-Pro: 41.4 (#111)

BenchmarkGLM-4.6MiMo-V2-Pro
LMArena Expert14311478
Vectara Hallucination Rate9.5%—

Multilingual Too close to call

GLM-4.6: 53.5 (#66), MiMo-V2-Pro: 52.7 (#81)

BenchmarkGLM-4.6MiMo-V2-Pro
LMArena Non-English14261416
LMArena Chinese14991456
LMArena French14591469
LMArena German14471417
LMArena Japanese13931366
LMArena Korean14001400
LMArena Russian14191427
LMArena Spanish14361457

Instruction Following MiMo-V2-Pro leads

GLM-4.6: 74.3 (#98), MiMo-V2-Pro: 76.0 (#49)

BenchmarkGLM-4.6MiMo-V2-Pro
LMArena Instruction Following14101445

Long Context GLM-4.6 leads

GLM-4.6: 43.4 (#94), MiMo-V2-Pro: 41.5 (#138)

BenchmarkGLM-4.6MiMo-V2-Pro
LMArena Longer Query14221455
CL-bench—15.7%
CL-bench Life—6.9%

Writing & Preference MiMo-V2-Pro leads

GLM-4.6: 61.1 (#90), MiMo-V2-Pro: 62.8 (#70)

BenchmarkGLM-4.6MiMo-V2-Pro
LMArena Text14401436
LMArena Creative Writing14111415
LMArena Multi-Turn14271456
EQ-Bench Creative Writing1411—

Frequently asked questions

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

MiMo-V2-Pro is the stronger model overall, scoring 43.0 to 41.4 on the Noometry Index.

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

MiMo-V2-Pro is cheaper. It lists at $0.43 per million input tokens and $0.87 per million output tokens; GLM-4.6 lists at $0.60 and $2.20.

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

MiMo-V2-Pro scores higher on coding benchmarks: 43.8 versus 40.1 in the Noometry coding category.

Which has the bigger context window?

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

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

19 benchmarks have published results for both models. GLM-4.6 has 29 scored results on Noometry and MiMo-V2-Pro has 23.

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