Noometry

Model comparison

MiMo-V2-Omni vs Qwen3.5 27B

MiMo-V2-Omni is the stronger model overall, scoring 43.6 to 41.9 on the Noometry Index.

Last verified . 18 shared benchmarks.

MiMo-V2-Omni Xiaomi

43.6

Rank #88 Confirmed

Qwen3.5 27B Alibaba (Qwen)

41.9

Rank #127 Confirmed

Summary

  • They share 18 benchmarks with published results for both. MiMo-V2-Omni scores higher in 8 categories and Qwen3.5 27B in 1 category; 6 gaps are clear of the uncertainty.
  • The widest gap is in coding, where MiMo-V2-Omni leads 43.3 to 38.9.
  • MiMo-V2-Omni is cheaper at $0.14 / $0.28 per million input/output tokens, against $0.30 / $2.40 for Qwen3.5 27B.
  • Qwen3.5 27B has downloadable open weights; the other is API-only.

Side by side

MiMo-V2-OmniQwen3.5 27B
ProviderXiaomiAlibaba (Qwen)
Noometry Index43.641.9
Released2026-03-182026-02-23
WeightsProprietaryOpen
Context window262K262K
Max output131K66K
Input $ / M tokens$0.14$0.30
Output $ / M tokens$0.28$2.40
Results tracked1828

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Category by category

Coding MiMo-V2-Omni leads

MiMo-V2-Omni: 43.3 (#89), Qwen3.5 27B: 38.9 (#168)

BenchmarkMiMo-V2-OmniQwen3.5 27B
LMArena Coding14661427
LMArena WebDev—1358
WeirdML—39.5%
ALE-Bench—349.45

Agentic & Tool Use Not comparable

MiMo-V2-Omni: —, Qwen3.5 27B: —

BenchmarkMiMo-V2-OmniQwen3.5 27B
Vending-Bench 2—201.98

Reasoning MiMo-V2-Omni leads

MiMo-V2-Omni: 29.7 (#88), Qwen3.5 27B: 27.5 (#117)

BenchmarkMiMo-V2-OmniQwen3.5 27B
LMArena Hard Prompts14451414
NYT Connections (extended)—47.9%
Thematic Generalization—45.5%
DTBench—82.4%
LMCA—34%

Math Too close to call

MiMo-V2-Omni: 39.1 (#115), Qwen3.5 27B: 38.8 (#127)

BenchmarkMiMo-V2-OmniQwen3.5 27B
LMArena Math14301429
MathArena Final-Answer Competitions—56.7%

Knowledge MiMo-V2-Omni leads

MiMo-V2-Omni: 40.5 (#118), Qwen3.5 27B: 38.0 (#150)

BenchmarkMiMo-V2-OmniQwen3.5 27B
LMArena Expert14491428
Vectara Hallucination Rate—12.1%

Multimodal Too close to call

MiMo-V2-Omni: 38.6 (#63), Qwen3.5 27B: 39.4 (#59)

BenchmarkMiMo-V2-OmniQwen3.5 27B
LMArena Vision12281241

Multilingual MiMo-V2-Omni leads

MiMo-V2-Omni: 51.8 (#102), Qwen3.5 27B: 50.8 (#115)

BenchmarkMiMo-V2-OmniQwen3.5 27B
LMArena Non-English14041390
LMArena Chinese14651478
LMArena French14471410
LMArena German13991393
LMArena Japanese13171345
LMArena Korean13551358
LMArena Russian14121390
LMArena Spanish14341407

Instruction Following MiMo-V2-Omni leads

MiMo-V2-Omni: 75.2 (#66), Qwen3.5 27B: 73.5 (#119)

BenchmarkMiMo-V2-OmniQwen3.5 27B
LMArena Instruction Following14281393

Long Context Too close to call

MiMo-V2-Omni: 44.1 (#76), Qwen3.5 27B: 43.1 (#106)

BenchmarkMiMo-V2-OmniQwen3.5 27B
LMArena Longer Query14421413

Writing & Preference MiMo-V2-Omni leads

MiMo-V2-Omni: 61.4 (#87), Qwen3.5 27B: 59.3 (#111)

BenchmarkMiMo-V2-OmniQwen3.5 27B
LMArena Text14231409
LMArena Creative Writing13921362
LMArena Multi-Turn14451410

Frequently asked questions

Is MiMo-V2-Omni better than Qwen3.5 27B?

MiMo-V2-Omni is the stronger model overall, scoring 43.6 to 41.9 on the Noometry Index.

Which is cheaper, MiMo-V2-Omni or Qwen3.5 27B?

MiMo-V2-Omni is cheaper. It lists at $0.14 per million input tokens and $0.28 per million output tokens; Qwen3.5 27B lists at $0.30 and $2.40.

Is MiMo-V2-Omni or Qwen3.5 27B better for coding?

MiMo-V2-Omni scores higher on coding benchmarks: 43.3 versus 38.9 in the Noometry coding category.

Which has the bigger context window?

Both accept 262K tokens.

How many benchmarks do MiMo-V2-Omni and Qwen3.5 27B share?

18 benchmarks have published results for both models. MiMo-V2-Omni has 18 scored results on Noometry and Qwen3.5 27B has 28.

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