Noometry

Model comparison

DeepSeek-V3.1 vs MiMo-V2.6-Pro

MiMo-V2.6-Pro is the stronger model overall, scoring 50.3 to 42.8 on the Noometry Index.

Last verified . 12 shared benchmarks.

DeepSeek-V3.1 DeepSeek

42.8

Rank #108 Confirmed

MiMo-V2.6-Pro Xiaomi

50.3

Rank #49 Confirmed

Summary

  • They share 12 benchmarks with published results for both. DeepSeek-V3.1 scores higher in 1 category and MiMo-V2.6-Pro in 7 categories; 7 gaps are clear of the uncertainty.
  • The widest gap is in math, where MiMo-V2.6-Pro leads 54.5 to 38.9.
  • DeepSeek-V3.1 is cheaper at $0.25 / $0.95 per million input/output tokens, against $0.43 / $0.87 for MiMo-V2.6-Pro.
  • MiMo-V2.6-Pro accepts more context: 1.05M tokens versus 164K.

Side by side

DeepSeek-V3.1MiMo-V2.6-Pro
ProviderDeepSeekXiaomi
Noometry Index42.850.3
Released2025-08-212026-09-21
WeightsOpenOpen
Context window164K1.05M
Max output8K131K
Input $ / M tokens$0.25$0.43
Output $ / M tokens$0.95$0.87
Results tracked2719

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Category by category

Coding MiMo-V2.6-Pro leads

DeepSeek-V3.1: 40.3 (#144), MiMo-V2.6-Pro: 55.5 (#23)

BenchmarkDeepSeek-V3.1MiMo-V2.6-Pro
LMArena Coding14171534
LMArena WebDev—1629
SciCode—60.9%
WeirdML38.4%—
ALE-Bench—1,158

Agentic & Tool Use Not comparable

DeepSeek-V3.1: —, MiMo-V2.6-Pro: 37.5 (#35)

BenchmarkDeepSeek-V3.1MiMo-V2.6-Pro
APEX-Agents—59.5%

Reasoning MiMo-V2.6-Pro leads

DeepSeek-V3.1: 27.9 (#110), MiMo-V2.6-Pro: 43.1 (#50)

BenchmarkDeepSeek-V3.1MiMo-V2.6-Pro
LMArena Hard Prompts14171512
SimpleBench40%—
Kagi LLM Benchmark53.2%—
CritPt—26.6%
DTBench82.7%—
LMCA24.3%—
Epoch Capabilities Index139.92—
ForecastBench58—

Math MiMo-V2.6-Pro leads

DeepSeek-V3.1: 38.9 (#122), MiMo-V2.6-Pro: 54.5 (#45)

BenchmarkDeepSeek-V3.1MiMo-V2.6-Pro
LMArena Math14201494
ProofBench—70%

Knowledge Too close to call

DeepSeek-V3.1: 43.7 (#90), MiMo-V2.6-Pro: 43.5 (#92)

BenchmarkDeepSeek-V3.1MiMo-V2.6-Pro
LMArena Expert14051543
Vectara Hallucination Rate5.5%—

Multimodal Not comparable

DeepSeek-V3.1: —, MiMo-V2.6-Pro: 40.8 (#43)

BenchmarkDeepSeek-V3.1MiMo-V2.6-Pro
LMArena Vision—1264

Multilingual MiMo-V2.6-Pro leads

DeepSeek-V3.1: 51.6 (#106), MiMo-V2.6-Pro: 56.9 (#14)

BenchmarkDeepSeek-V3.1MiMo-V2.6-Pro
LMArena Non-English14001474
LMArena Chinese14691529
LMArena Russian14051480
LMArena French1447—
LMArena German1411—
LMArena Japanese1378—
LMArena Korean1337—
LMArena Spanish1431—

Instruction Following MiMo-V2.6-Pro leads

DeepSeek-V3.1: 73.9 (#110), MiMo-V2.6-Pro: 78.2 (#12)

BenchmarkDeepSeek-V3.1MiMo-V2.6-Pro
LMArena Instruction Following14001493

Long Context MiMo-V2.6-Pro leads

DeepSeek-V3.1: 36.3 (#232), MiMo-V2.6-Pro: 46.0 (#27)

BenchmarkDeepSeek-V3.1MiMo-V2.6-Pro
LMArena Longer Query14221501
Fiction.LiveBench52.8%—

Writing & Preference MiMo-V2.6-Pro leads

DeepSeek-V3.1: 60.3 (#98), MiMo-V2.6-Pro: 66.8 (#33)

BenchmarkDeepSeek-V3.1MiMo-V2.6-Pro
LMArena Text14201492
LMArena Creative Writing14011468
LMArena Multi-Turn14081464
EQ-Bench Creative Writing1436—

Frequently asked questions

Is DeepSeek-V3.1 better than MiMo-V2.6-Pro?

MiMo-V2.6-Pro is the stronger model overall, scoring 50.3 to 42.8 on the Noometry Index.

Which is cheaper, DeepSeek-V3.1 or MiMo-V2.6-Pro?

DeepSeek-V3.1 is cheaper. It lists at $0.25 per million input tokens and $0.95 per million output tokens; MiMo-V2.6-Pro lists at $0.43 and $0.87.

Is DeepSeek-V3.1 or MiMo-V2.6-Pro better for coding?

MiMo-V2.6-Pro scores higher on coding benchmarks: 55.5 versus 40.3 in the Noometry coding category.

Which has the bigger context window?

MiMo-V2.6-Pro does, with 1.05M tokens against 164K.

How many benchmarks do DeepSeek-V3.1 and MiMo-V2.6-Pro share?

12 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and MiMo-V2.6-Pro has 19.

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