Noometry

Model comparison

DeepSeek-V3.1 vs MiMo-V2-Flash

DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 41.3 on the Noometry Index. MiMo-V2-Flash costs 2.4× less per token, which makes it the better buy when DeepSeek-V3.1's lead doesn't matter for your workload.

Last verified . 17 shared benchmarks.

DeepSeek-V3.1 DeepSeek

42.8

Rank #108 Confirmed

MiMo-V2-Flash Xiaomi

41.3

Rank #138 Confirmed

Summary

  • They share 17 benchmarks with published results for both. DeepSeek-V3.1 scores higher in 7 categories and MiMo-V2-Flash in 1 category; 4 gaps are clear of the uncertainty.
  • The widest gap is in long context, where MiMo-V2-Flash leads 43.0 to 36.3.
  • MiMo-V2-Flash is cheaper at $0.14 / $0.28 per million input/output tokens, against $0.25 / $0.95 for DeepSeek-V3.1.
  • MiMo-V2-Flash accepts more context: 262K tokens versus 164K.

Side by side

DeepSeek-V3.1MiMo-V2-Flash
ProviderDeepSeekXiaomi
Noometry Index42.841.3
Released2025-08-212025-12-16
WeightsOpenOpen
Context window164K262K
Max output8K66K
Input $ / M tokens$0.25$0.14
Output $ / M tokens$0.95$0.28
Results tracked2721

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Category by category

Coding DeepSeek-V3.1 leads

DeepSeek-V3.1: 40.3 (#144), MiMo-V2-Flash: 36.1 (#211)

BenchmarkDeepSeek-V3.1MiMo-V2-Flash
LMArena Coding14171443
LMArena WebDev—1330
SciCode—25.9%
WeirdML38.4%—
ALE-Bench—737.95

Reasoning DeepSeek-V3.1 leads

DeepSeek-V3.1: 27.9 (#110), MiMo-V2-Flash: 24.9 (#157)

BenchmarkDeepSeek-V3.1MiMo-V2-Flash
LMArena Hard Prompts14171420
SimpleBench40%—
Kagi LLM Benchmark53.2%—
CritPt—0%
DTBench82.7%—
LMCA24.3%—
Epoch Capabilities Index139.92—
ForecastBench58—

Math Too close to call

DeepSeek-V3.1: 38.9 (#122), MiMo-V2-Flash: 38.3 (#139)

BenchmarkDeepSeek-V3.1MiMo-V2-Flash
LMArena Math14201396

Knowledge DeepSeek-V3.1 leads

DeepSeek-V3.1: 43.7 (#90), MiMo-V2-Flash: 39.7 (#131)

BenchmarkDeepSeek-V3.1MiMo-V2-Flash
LMArena Expert14051425
Vectara Hallucination Rate5.5%—

Multilingual Too close to call

DeepSeek-V3.1: 51.6 (#106), MiMo-V2-Flash: 51.0 (#113)

BenchmarkDeepSeek-V3.1MiMo-V2-Flash
LMArena Non-English14001392
LMArena Chinese14691462
LMArena French14471429
LMArena German14111395
LMArena Japanese13781325
LMArena Korean13371358
LMArena Russian14051387
LMArena Spanish14311420

Instruction Following Too close to call

DeepSeek-V3.1: 73.9 (#110), MiMo-V2-Flash: 73.5 (#120)

BenchmarkDeepSeek-V3.1MiMo-V2-Flash
LMArena Instruction Following14001392

Long Context MiMo-V2-Flash leads

DeepSeek-V3.1: 36.3 (#232), MiMo-V2-Flash: 43.0 (#110)

BenchmarkDeepSeek-V3.1MiMo-V2-Flash
LMArena Longer Query14221409
Fiction.LiveBench52.8%—

Writing & Preference Too close to call

DeepSeek-V3.1: 60.3 (#98), MiMo-V2-Flash: 59.7 (#106)

BenchmarkDeepSeek-V3.1MiMo-V2-Flash
LMArena Text14201411
LMArena Creative Writing14011375
LMArena Multi-Turn14081404
EQ-Bench Creative Writing1436—

Frequently asked questions

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

DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 41.3 on the Noometry Index. MiMo-V2-Flash costs 2.4× less per token, which makes it the better buy when DeepSeek-V3.1's lead doesn't matter for your workload.

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

MiMo-V2-Flash is cheaper. It lists at $0.14 per million input tokens and $0.28 per million output tokens; DeepSeek-V3.1 lists at $0.25 and $0.95.

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

DeepSeek-V3.1 scores higher on coding benchmarks: 40.3 versus 36.1 in the Noometry coding category.

Which has the bigger context window?

MiMo-V2-Flash does, with 262K tokens against 164K.

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

17 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and MiMo-V2-Flash has 21.

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