Noometry

Model comparison

GLM-4.6 vs MiMo-V2.5-Pro

MiMo-V2.5-Pro is the stronger model overall, scoring 45.2 to 41.4 on the Noometry Index.

Last verified . 22 shared benchmarks.

GLM-4.6 Z.ai (Zhipu)

41.4

Rank #135 Confirmed

MiMo-V2.5-Pro Xiaomi

45.2

Rank #74 Confirmed

Summary

  • They share 22 benchmarks with published results for both. GLM-4.6 scores higher in 0 categories and MiMo-V2.5-Pro in 8 categories; 7 gaps are clear of the uncertainty.
  • The widest gap is in coding, where MiMo-V2.5-Pro leads 47.4 to 40.1.
  • The biggest single-benchmark swing is SciCode: 38.4% for GLM-4.6 and 50.2% for MiMo-V2.5-Pro.
  • MiMo-V2.5-Pro is cheaper at $0.43 / $0.87 per million input/output tokens, against $0.60 / $2.20 for GLM-4.6.
  • MiMo-V2.5-Pro accepts more context: 1.05M tokens versus 205K.

Side by side

GLM-4.6MiMo-V2.5-Pro
ProviderZ.ai (Zhipu)Xiaomi
Noometry Index41.445.2
Released2025-09-302026-04-22
WeightsOpenOpen
Context window205K1.05M
Max output131K131K
Input $ / M tokens$0.60$0.43
Output $ / M tokens$2.20$0.87
Results tracked2927

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Category by category

Coding MiMo-V2.5-Pro leads

GLM-4.6: 40.1 (#148), MiMo-V2.5-Pro: 47.4 (#60)

BenchmarkGLM-4.6MiMo-V2.5-Pro
LMArena WebDev13401479
SciCode38.4%50.2%
LMArena Coding14491503
ALE-Bench340.82899.8
SWE-bench Verified (bash only)55.4%—

Agentic & Tool Use Not comparable

GLM-4.6: 32.3 (#66), MiMo-V2.5-Pro: —

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

Reasoning MiMo-V2.5-Pro leads

GLM-4.6: 23.7 (#172), MiMo-V2.5-Pro: 26.8 (#130)

BenchmarkGLM-4.6MiMo-V2.5-Pro
CritPt1.1%4%
LMArena Hard Prompts14401488
Kagi LLM Benchmark47.4%—
NYT Connections (extended)—34.4%
DTBench—84.5%
LMCA—29.5%

Math Too close to call

GLM-4.6: 39.1 (#111), MiMo-V2.5-Pro: 40.0 (#96)

BenchmarkGLM-4.6MiMo-V2.5-Pro
LMArena Math14321481
ProofBench—22%
FrontierMath (Feb 2025 set)3.8%—
FrontierMath Tier 4 (v1)2.1%—

Knowledge MiMo-V2.5-Pro leads

GLM-4.6: 40.2 (#124), MiMo-V2.5-Pro: 42.2 (#98)

BenchmarkGLM-4.6MiMo-V2.5-Pro
LMArena Expert14311503
Vectara Hallucination Rate9.5%—

Multilingual MiMo-V2.5-Pro leads

GLM-4.6: 53.5 (#66), MiMo-V2.5-Pro: 55.1 (#34)

BenchmarkGLM-4.6MiMo-V2.5-Pro
LMArena Non-English14261449
LMArena Chinese14991507
LMArena French14591488
LMArena German14471458
LMArena Japanese13931412
LMArena Korean14001437
LMArena Russian14191450
LMArena Spanish14361471

Instruction Following MiMo-V2.5-Pro leads

GLM-4.6: 74.3 (#98), MiMo-V2.5-Pro: 77.5 (#21)

BenchmarkGLM-4.6MiMo-V2.5-Pro
LMArena Instruction Following14101477

Long Context MiMo-V2.5-Pro leads

GLM-4.6: 43.4 (#94), MiMo-V2.5-Pro: 45.4 (#37)

BenchmarkGLM-4.6MiMo-V2.5-Pro
LMArena Longer Query14221483

Writing & Preference MiMo-V2.5-Pro leads

GLM-4.6: 61.1 (#90), MiMo-V2.5-Pro: 65.3 (#49)

BenchmarkGLM-4.6MiMo-V2.5-Pro
LMArena Text14401465
LMArena Creative Writing14111440
EQ-Bench Creative Writing14111493
LMArena Multi-Turn14271477
EQ-Bench 4—1208

Frequently asked questions

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

MiMo-V2.5-Pro is the stronger model overall, scoring 45.2 to 41.4 on the Noometry Index.

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

MiMo-V2.5-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-V2.5-Pro better for coding?

MiMo-V2.5-Pro scores higher on coding benchmarks: 47.4 versus 40.1 in the Noometry coding category.

Which has the bigger context window?

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

How many benchmarks do GLM-4.6 and MiMo-V2.5-Pro share?

22 benchmarks have published results for both models. GLM-4.6 has 29 scored results on Noometry and MiMo-V2.5-Pro has 27.

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