Noometry

Model comparison

DeepSeek-V3.1 vs MiMo-V2-Pro

DeepSeek-V3.1 and MiMo-V2-Pro score almost the same on the Noometry Index (42.8 vs 43.0), so choose on price, context window or the category you care about most.

Last verified . 17 shared benchmarks.

DeepSeek-V3.1 DeepSeek

42.8

Rank #108 Confirmed

MiMo-V2-Pro Xiaomi

43.0

Rank #103 Confirmed

Summary

  • They share 17 benchmarks with published results for both. DeepSeek-V3.1 scores higher in 2 categories and MiMo-V2-Pro in 6 categories; 7 gaps are clear of the uncertainty.
  • The widest gap is in reasoning, where DeepSeek-V3.1 leads 27.9 to 22.1.
  • DeepSeek-V3.1 is cheaper at $0.25 / $0.95 per million input/output tokens, against $0.43 / $0.87 for MiMo-V2-Pro.
  • MiMo-V2-Pro accepts more context: 1.05M tokens versus 164K.
  • DeepSeek-V3.1 has downloadable open weights; the other is API-only.

Side by side

DeepSeek-V3.1MiMo-V2-Pro
ProviderDeepSeekXiaomi
Noometry Index42.843.0
Released2025-08-212026-03-18
WeightsOpenProprietary
Context window164K1.05M
Max output8K131K
Input $ / M tokens$0.25$0.43
Output $ / M tokens$0.95$0.87
Results tracked2723

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Category by category

Coding MiMo-V2-Pro leads

DeepSeek-V3.1: 40.3 (#144), MiMo-V2-Pro: 43.8 (#83)

BenchmarkDeepSeek-V3.1MiMo-V2-Pro
LMArena Coding14171476
LMArena WebDev—1433
WeirdML38.4%—
ALE-Bench—785.17

Reasoning DeepSeek-V3.1 leads

DeepSeek-V3.1: 27.9 (#110), MiMo-V2-Pro: 22.1 (#206)

BenchmarkDeepSeek-V3.1MiMo-V2-Pro
LMArena Hard Prompts14171457
SimpleBench40%—
Kagi LLM Benchmark53.2%—
NYT Connections (extended)—25.8%
Thematic Generalization—45.9%
DTBench82.7%—
LMCA24.3%—
Epoch Capabilities Index139.92—
ForecastBench58—

Math Too close to call

DeepSeek-V3.1: 38.9 (#122), MiMo-V2-Pro: 39.5 (#102)

BenchmarkDeepSeek-V3.1MiMo-V2-Pro
LMArena Math14201447

Knowledge DeepSeek-V3.1 leads

DeepSeek-V3.1: 43.7 (#90), MiMo-V2-Pro: 41.4 (#111)

BenchmarkDeepSeek-V3.1MiMo-V2-Pro
LMArena Expert14051478
Vectara Hallucination Rate5.5%—

Multilingual MiMo-V2-Pro leads

DeepSeek-V3.1: 51.6 (#106), MiMo-V2-Pro: 52.7 (#81)

BenchmarkDeepSeek-V3.1MiMo-V2-Pro
LMArena Non-English14001416
LMArena Chinese14691456
LMArena French14471469
LMArena German14111417
LMArena Japanese13781366
LMArena Korean13371400
LMArena Russian14051427
LMArena Spanish14311457

Instruction Following MiMo-V2-Pro leads

DeepSeek-V3.1: 73.9 (#110), MiMo-V2-Pro: 76.0 (#49)

BenchmarkDeepSeek-V3.1MiMo-V2-Pro
LMArena Instruction Following14001445

Long Context MiMo-V2-Pro leads

DeepSeek-V3.1: 36.3 (#232), MiMo-V2-Pro: 41.5 (#138)

BenchmarkDeepSeek-V3.1MiMo-V2-Pro
LMArena Longer Query14221455
Fiction.LiveBench52.8%—
CL-bench—15.7%
CL-bench Life—6.9%

Writing & Preference MiMo-V2-Pro leads

DeepSeek-V3.1: 60.3 (#98), MiMo-V2-Pro: 62.8 (#70)

BenchmarkDeepSeek-V3.1MiMo-V2-Pro
LMArena Text14201436
LMArena Creative Writing14011415
LMArena Multi-Turn14081456
EQ-Bench Creative Writing1436—

Frequently asked questions

Is DeepSeek-V3.1 better than MiMo-V2-Pro?

DeepSeek-V3.1 and MiMo-V2-Pro score almost the same on the Noometry Index (42.8 vs 43.0), so choose on price, context window or the category you care about most.

Which is cheaper, DeepSeek-V3.1 or MiMo-V2-Pro?

DeepSeek-V3.1 is cheaper. It lists at $0.25 per million input tokens and $0.95 per million output tokens; MiMo-V2-Pro lists at $0.43 and $0.87.

Is DeepSeek-V3.1 or MiMo-V2-Pro better for coding?

MiMo-V2-Pro scores higher on coding benchmarks: 43.8 versus 40.3 in the Noometry coding category.

Which has the bigger context window?

MiMo-V2-Pro does, with 1.05M tokens against 164K.

How many benchmarks do DeepSeek-V3.1 and MiMo-V2-Pro share?

17 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and MiMo-V2-Pro has 23.

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