Noometry

Model comparison

GLM-4.6 vs MiMo-V2.6-Pro

MiMo-V2.6-Pro is the stronger model overall, scoring 46.7 to 40.9 on the Noometry Index.

Last verified . 13 shared benchmarks.

GLM-4.6 Z.ai (Zhipu)

40.9

Rank #132 Confirmed

MiMo-V2.6-Pro Xiaomi

46.7

Rank #55 Confirmed

Summary

  • They share 13 benchmarks with published results for both. GLM-4.6 scores higher in 0 categories and MiMo-V2.6-Pro in 8 categories; 8 gaps are clear of the uncertainty.
  • The widest gap is in coding, where MiMo-V2.6-Pro leads 53.2 to 39.9.
  • 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 Index40.946.7
Released2025-09-302026-09-22
WeightsOpenOpen
Context window205K1.05M
Max output131K131K
Input $ / M tokens$0.60$0.43
Output $ / M tokens$2.20$0.87
Results tracked2914

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Category by category

Coding MiMo-V2.6-Pro leads

GLM-4.6: 39.9 (#137), MiMo-V2.6-Pro: 53.2 (#27)

BenchmarkGLM-4.6MiMo-V2.6-Pro
LMArena WebDev13391618
LMArena Coding14491538
SWE-bench Verified (bash only)55.4%—
SciCode38.4%—
ALE-Bench340.82—

Agentic & Tool Use Not comparable

GLM-4.6: 32.1 (#60), MiMo-V2.6-Pro: —

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

Reasoning MiMo-V2.6-Pro leads

GLM-4.6: 23.1 (#168), MiMo-V2.6-Pro: 30.4 (#76)

BenchmarkGLM-4.6MiMo-V2.6-Pro
LMArena Hard Prompts14401511
Kagi LLM Benchmark47.4%—
CritPt1.1%—

Math MiMo-V2.6-Pro leads

GLM-4.6: 36.4 (#115), MiMo-V2.6-Pro: 37.9 (#91)

BenchmarkGLM-4.6MiMo-V2.6-Pro
LMArena Math14311497
FrontierMath (Feb 2025 set)3.8%—
FrontierMath Tier 4 (v1)2.1%—

Knowledge MiMo-V2.6-Pro leads

GLM-4.6: 39.6 (#118), MiMo-V2.6-Pro: 43.0 (#85)

BenchmarkGLM-4.6MiMo-V2.6-Pro
LMArena Expert14321548
Vectara Hallucination Rate9.5%—

Multimodal Not comparable

GLM-4.6: —, MiMo-V2.6-Pro: 40.2 (#40)

BenchmarkGLM-4.6MiMo-V2.6-Pro
LMArena Vision—1265

Multilingual MiMo-V2.6-Pro leads

GLM-4.6: 53.6 (#62), MiMo-V2.6-Pro: 57.0 (#15)

BenchmarkGLM-4.6MiMo-V2.6-Pro
LMArena Non-English14261474
LMArena Chinese15001532
LMArena Russian14181481
LMArena French1459—
LMArena German1447—
LMArena Japanese1394—
LMArena Korean1401—
LMArena Spanish1436—

Instruction Following MiMo-V2.6-Pro leads

GLM-4.6: 74.5 (#93), MiMo-V2.6-Pro: 78.2 (#10)

BenchmarkGLM-4.6MiMo-V2.6-Pro
LMArena Instruction Following14101490

Long Context MiMo-V2.6-Pro leads

GLM-4.6: 43.4 (#91), MiMo-V2.6-Pro: 46.0 (#26)

BenchmarkGLM-4.6MiMo-V2.6-Pro
LMArena Longer Query14231500

Writing & Preference MiMo-V2.6-Pro leads

GLM-4.6: 61.3 (#86), MiMo-V2.6-Pro: 66.9 (#32)

BenchmarkGLM-4.6MiMo-V2.6-Pro
LMArena Text14401491
LMArena Creative Writing14111465
LMArena Multi-Turn14271463
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 46.7 to 40.9 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: 53.2 versus 39.9 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?

13 benchmarks have published results for both models. GLM-4.6 has 29 scored results on Noometry and MiMo-V2.6-Pro has 14.

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