Model comparison

Grok 4.1 Fast vs MiMo-V2-Omni

MiMo-V2-Omni is the stronger model overall, scoring 43.6 to 41.4 on the Noometry Index.

Last verified . 18 shared benchmarks.

Grok 4.1 Fast xAI

41.4

Rank #136 Confirmed

MiMo-V2-Omni Xiaomi

43.6

Rank #88 Confirmed

Summary

  • They share 18 benchmarks with published results for both. Grok 4.1 Fast scores higher in 1 category and MiMo-V2-Omni in 8 categories; 8 gaps are clear of the uncertainty.
  • The widest gap is in reasoning, where Grok 4.1 Fast leads 43.4 to 29.7.
  • MiMo-V2-Omni is cheaper at $0.14 / $0.28 per million input/output tokens, against $0.20 / $0.50 for Grok 4.1 Fast.
  • MiMo-V2-Omni accepts more context: 262K tokens versus 128K.

Side by side

Grok 4.1 Fast and MiMo-V2-Omni specifications
Grok 4.1 FastMiMo-V2-Omni
ProviderxAIXiaomi
Noometry Index41.443.6
Released2025-06-272026-03-18
WeightsProprietaryProprietary
Context window128K262K
Max output30K131K
Input $ / M tokens$0.20$0.14
Output $ / M tokens$0.50$0.28
Results tracked3218

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Category by category

Coding MiMo-V2-Omni leads

Grok 4.1 Fast: 34.1 (#245), MiMo-V2-Omni: 43.3 (#89)

Coding benchmarks
BenchmarkGrok 4.1 FastMiMo-V2-Omni
LMArena Coding14111466
LMArena WebDev1242—
ALE-Bench394.93—

Agentic & Tool Use Not comparable

Grok 4.1 Fast: 36.3 (#39), MiMo-V2-Omni: —

Agentic & Tool Use benchmarks
BenchmarkGrok 4.1 FastMiMo-V2-Omni
Berkeley Function Calling Leaderboard69.6%—
τ²-bench Banking13.1%—
LMArena Search1171—
Vending-Bench 21,107—

Reasoning Grok 4.1 Fast leads

Grok 4.1 Fast: 43.4 (#49), MiMo-V2-Omni: 29.7 (#88)

Reasoning benchmarks
BenchmarkGrok 4.1 FastMiMo-V2-Omni
LMArena Hard Prompts14071445
SimpleBench56%—
NYT Connections (extended)87.4%—
DTBench87.7%—
ForecastBench61—

Math MiMo-V2-Omni leads

Grok 4.1 Fast: 31.9 (#221), MiMo-V2-Omni: 39.1 (#115)

Math benchmarks
BenchmarkGrok 4.1 FastMiMo-V2-Omni
LMArena Math14081430
MathArena Final-Answer Competitions60.9%—
ProofBench4%—

Knowledge MiMo-V2-Omni leads

Grok 4.1 Fast: 33.1 (#207), MiMo-V2-Omni: 40.5 (#118)

Knowledge benchmarks
BenchmarkGrok 4.1 FastMiMo-V2-Omni
LMArena Expert13991449
Vectara Hallucination Rate17.8%—

Multimodal MiMo-V2-Omni leads

Grok 4.1 Fast: 37.0 (#76), MiMo-V2-Omni: 38.6 (#63)

Multimodal benchmarks
BenchmarkGrok 4.1 FastMiMo-V2-Omni
LMArena Vision12011228

Multilingual Too close to call

Grok 4.1 Fast: 51.0 (#114), MiMo-V2-Omni: 51.8 (#102)

Multilingual benchmarks
BenchmarkGrok 4.1 FastMiMo-V2-Omni
LMArena Non-English13911404
LMArena Chinese14411465
LMArena French14151447
LMArena German14041399
LMArena Japanese13491317
LMArena Korean13611355
LMArena Russian13871412
LMArena Spanish14131434

Instruction Following MiMo-V2-Omni leads

Grok 4.1 Fast: 72.7 (#133), MiMo-V2-Omni: 75.2 (#66)

Instruction Following benchmarks
BenchmarkGrok 4.1 FastMiMo-V2-Omni
LMArena Instruction Following13761428

Long Context MiMo-V2-Omni leads

Grok 4.1 Fast: 42.4 (#126), MiMo-V2-Omni: 44.1 (#76)

Long Context benchmarks
BenchmarkGrok 4.1 FastMiMo-V2-Omni
LMArena Longer Query13901442

Writing & Preference MiMo-V2-Omni leads

Grok 4.1 Fast: 57.2 (#131), MiMo-V2-Omni: 61.4 (#87)

Writing & Preference benchmarks
BenchmarkGrok 4.1 FastMiMo-V2-Omni
LMArena Text14081423
LMArena Creative Writing13941392
LMArena Multi-Turn13891445
EQ-Bench Creative Writing1327—

Frequently asked questions

Is Grok 4.1 Fast better than MiMo-V2-Omni?

MiMo-V2-Omni is the stronger model overall, scoring 43.6 to 41.4 on the Noometry Index.

Which is cheaper, Grok 4.1 Fast or MiMo-V2-Omni?

MiMo-V2-Omni is cheaper. It lists at $0.14 per million input tokens and $0.28 per million output tokens; Grok 4.1 Fast lists at $0.20 and $0.50.

Is Grok 4.1 Fast or MiMo-V2-Omni better for coding?

MiMo-V2-Omni scores higher on coding benchmarks: 43.3 versus 34.1 in the Noometry coding category.

Which has the bigger context window?

MiMo-V2-Omni does, with 262K tokens against 128K.

How many benchmarks do Grok 4.1 Fast and MiMo-V2-Omni share?

18 benchmarks have published results for both models. Grok 4.1 Fast has 32 scored results on Noometry and MiMo-V2-Omni has 18.

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