Noometry

Model comparison

GLM-4.7 vs MiMo-V2.6-Pro

MiMo-V2.6-Pro is the stronger model overall, scoring 50.3 to 42.0 on the Noometry Index.

Last verified . 17 shared benchmarks.

GLM-4.7 Z.ai (Zhipu)

42.0

Rank #124 Confirmed

MiMo-V2.6-Pro Xiaomi

50.3

Rank #49 Confirmed

Summary

  • They share 17 benchmarks with published results for both. GLM-4.7 scores higher in 1 category and MiMo-V2.6-Pro in 8 categories; 9 gaps are clear of the uncertainty.
  • The widest gap is in reasoning, where MiMo-V2.6-Pro leads 43.1 to 24.3.
  • The biggest single-benchmark swing is ProofBench: 6% for GLM-4.7 and 70% 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-4.7.
  • MiMo-V2.6-Pro accepts more context: 1.05M tokens versus 205K.

Side by side

GLM-4.7MiMo-V2.6-Pro
ProviderZ.ai (Zhipu)Xiaomi
Noometry Index42.050.3
Released2025-12-222026-09-21
WeightsOpenOpen
Context window205K1.05M
Max output131K131K
Input $ / M tokens$0.60$0.43
Output $ / M tokens$2.20$0.87
Results tracked3619

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Category by category

Coding MiMo-V2.6-Pro leads

GLM-4.7: 44.0 (#79), MiMo-V2.6-Pro: 55.5 (#23)

BenchmarkGLM-4.7MiMo-V2.6-Pro
LMArena WebDev14351629
SciCode45.1%60.9%
LMArena Coding14541534
ALE-Bench399.481,158

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

GLM-4.7: 26.5 (#103), MiMo-V2.6-Pro: 37.5 (#35)

BenchmarkGLM-4.7MiMo-V2.6-Pro
Terminal-Bench33.4%—
APEX-Agents—59.5%
Vending-Bench 22,377—

Reasoning MiMo-V2.6-Pro leads

GLM-4.7: 24.3 (#164), MiMo-V2.6-Pro: 43.1 (#50)

BenchmarkGLM-4.7MiMo-V2.6-Pro
CritPt1.7%26.6%
LMArena Hard Prompts14431512
SimpleBench47.7%—
Chess Puzzles6%—
Epoch Capabilities Index143.51—

Math MiMo-V2.6-Pro leads

GLM-4.7: 38.6 (#135), MiMo-V2.6-Pro: 54.5 (#45)

BenchmarkGLM-4.7MiMo-V2.6-Pro
ProofBench6%70%
LMArena Math14231494
OTIS Mock AIME 2024-202583.3%—
FrontierMath (Feb 2025 set)2.4%—
FrontierMath Tier 4 (v1)0%—

Knowledge GLM-4.7 leads

GLM-4.7: 47.0 (#80), MiMo-V2.6-Pro: 43.5 (#92)

BenchmarkGLM-4.7MiMo-V2.6-Pro
LMArena Expert14241543
GPQA Diamond83.3%—
SimpleQA Verified32.2%—
Vectara Hallucination Rate11.7%—

Multimodal Not comparable

GLM-4.7: —, MiMo-V2.6-Pro: 40.8 (#43)

BenchmarkGLM-4.7MiMo-V2.6-Pro
LMArena Vision—1264

Multilingual MiMo-V2.6-Pro leads

GLM-4.7: 52.8 (#79), MiMo-V2.6-Pro: 56.9 (#14)

BenchmarkGLM-4.7MiMo-V2.6-Pro
LMArena Non-English14171474
LMArena Chinese14951529
LMArena Russian14231480
LMArena French1432—
LMArena German1424—
LMArena Japanese1439—
LMArena Korean1399—
LMArena Spanish1434—

Instruction Following MiMo-V2.6-Pro leads

GLM-4.7: 74.4 (#95), MiMo-V2.6-Pro: 78.2 (#12)

BenchmarkGLM-4.7MiMo-V2.6-Pro
LMArena Instruction Following14111493

Long Context MiMo-V2.6-Pro leads

GLM-4.7: 42.8 (#116), MiMo-V2.6-Pro: 46.0 (#27)

BenchmarkGLM-4.7MiMo-V2.6-Pro
LMArena Longer Query14321501
CL-bench15.9%—
CL-bench Life10.9%—

Writing & Preference MiMo-V2.6-Pro leads

GLM-4.7: 60.9 (#93), MiMo-V2.6-Pro: 66.8 (#33)

BenchmarkGLM-4.7MiMo-V2.6-Pro
LMArena Text14351492
LMArena Creative Writing14011468
LMArena Multi-Turn14461464
EQ-Bench Creative Writing1413—

Frequently asked questions

Is GLM-4.7 better than MiMo-V2.6-Pro?

MiMo-V2.6-Pro is the stronger model overall, scoring 50.3 to 42.0 on the Noometry Index.

Which is cheaper, GLM-4.7 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-4.7 lists at $0.60 and $2.20.

Is GLM-4.7 or MiMo-V2.6-Pro better for coding?

MiMo-V2.6-Pro scores higher on coding benchmarks: 55.5 versus 44.0 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-4.7 and MiMo-V2.6-Pro share?

17 benchmarks have published results for both models. GLM-4.7 has 36 scored results on Noometry and MiMo-V2.6-Pro has 19.

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