Noometry

Model comparison

GLM-4.6 vs MiMo-V2.6-Pro

MiMo-V2.6-Pro is the stronger model overall, scoring 50.3 to 41.4 on the Noometry Index.

Last verified . 16 shared benchmarks.

GLM-4.6 Z.ai (Zhipu)

41.4

Rank #135 Confirmed

MiMo-V2.6-Pro Xiaomi

50.3

Rank #49 Confirmed

Summary

  • They share 16 benchmarks with published results for both. GLM-4.6 scores higher in 0 categories and MiMo-V2.6-Pro in 9 categories; 9 gaps are clear of the uncertainty.
  • The widest gap is in reasoning, where MiMo-V2.6-Pro leads 43.1 to 23.7.
  • The biggest single-benchmark swing is CritPt: 1.1% for GLM-4.6 and 26.6% for MiMo-V2.6-Pro.
  • MiMo-V2.6-Pro is cheaper at $0.43 / $0.87 per million input/output tokens, against $0.60 / $2.20 for GLM-4.6.
  • MiMo-V2.6-Pro accepts more context: 1.05M tokens versus 205K.

Side by side

GLM-4.6MiMo-V2.6-Pro
ProviderZ.ai (Zhipu)Xiaomi
Noometry Index41.450.3
Released2025-09-302026-09-21
WeightsOpenOpen
Context window205K1.05M
Max output131K131K
Input $ / M tokens$0.60$0.43
Output $ / M tokens$2.20$0.87
Results tracked2919

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Category by category

Coding MiMo-V2.6-Pro leads

GLM-4.6: 40.1 (#148), MiMo-V2.6-Pro: 55.5 (#23)

BenchmarkGLM-4.6MiMo-V2.6-Pro
LMArena WebDev13401629
SciCode38.4%60.9%
LMArena Coding14491534
ALE-Bench340.821,158
SWE-bench Verified (bash only)55.4%—

Agentic & Tool Use MiMo-V2.6-Pro leads

GLM-4.6: 32.3 (#66), MiMo-V2.6-Pro: 37.5 (#35)

BenchmarkGLM-4.6MiMo-V2.6-Pro
Terminal-Bench24.5%—
APEX-Agents—59.5%
Berkeley Function Calling Leaderboard72.4%—

Reasoning MiMo-V2.6-Pro leads

GLM-4.6: 23.7 (#172), MiMo-V2.6-Pro: 43.1 (#50)

BenchmarkGLM-4.6MiMo-V2.6-Pro
CritPt1.1%26.6%
LMArena Hard Prompts14401512
Kagi LLM Benchmark47.4%—

Math MiMo-V2.6-Pro leads

GLM-4.6: 39.1 (#111), MiMo-V2.6-Pro: 54.5 (#45)

BenchmarkGLM-4.6MiMo-V2.6-Pro
LMArena Math14321494
ProofBench—70%
FrontierMath (Feb 2025 set)3.8%—
FrontierMath Tier 4 (v1)2.1%—

Knowledge MiMo-V2.6-Pro leads

GLM-4.6: 40.2 (#124), MiMo-V2.6-Pro: 43.5 (#92)

BenchmarkGLM-4.6MiMo-V2.6-Pro
LMArena Expert14311543
Vectara Hallucination Rate9.5%—

Multimodal Not comparable

GLM-4.6: —, MiMo-V2.6-Pro: 40.8 (#43)

BenchmarkGLM-4.6MiMo-V2.6-Pro
LMArena Vision—1264

Multilingual MiMo-V2.6-Pro leads

GLM-4.6: 53.5 (#66), MiMo-V2.6-Pro: 56.9 (#14)

BenchmarkGLM-4.6MiMo-V2.6-Pro
LMArena Non-English14261474
LMArena Chinese14991529
LMArena Russian14191480
LMArena French1459—
LMArena German1447—
LMArena Japanese1393—
LMArena Korean1400—
LMArena Spanish1436—

Instruction Following MiMo-V2.6-Pro leads

GLM-4.6: 74.3 (#98), MiMo-V2.6-Pro: 78.2 (#12)

BenchmarkGLM-4.6MiMo-V2.6-Pro
LMArena Instruction Following14101493

Long Context MiMo-V2.6-Pro leads

GLM-4.6: 43.4 (#94), MiMo-V2.6-Pro: 46.0 (#27)

BenchmarkGLM-4.6MiMo-V2.6-Pro
LMArena Longer Query14221501

Writing & Preference MiMo-V2.6-Pro leads

GLM-4.6: 61.1 (#90), MiMo-V2.6-Pro: 66.8 (#33)

BenchmarkGLM-4.6MiMo-V2.6-Pro
LMArena Text14401492
LMArena Creative Writing14111468
LMArena Multi-Turn14271464
EQ-Bench Creative Writing1411—

Frequently asked questions

Is GLM-4.6 better than MiMo-V2.6-Pro?

MiMo-V2.6-Pro is the stronger model overall, scoring 50.3 to 41.4 on the Noometry Index.

Which is cheaper, GLM-4.6 or MiMo-V2.6-Pro?

MiMo-V2.6-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-V2.6-Pro better for coding?

MiMo-V2.6-Pro scores higher on coding benchmarks: 55.5 versus 40.1 in the Noometry coding category.

Which has the bigger context window?

MiMo-V2.6-Pro does, with 1.05M tokens against 205K.

How many benchmarks do GLM-4.6 and MiMo-V2.6-Pro share?

16 benchmarks have published results for both models. GLM-4.6 has 29 scored results on Noometry and MiMo-V2.6-Pro has 19.

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