Noometry

Model comparison

Llama 3.2 3B vs MiMo-V2-Pro

MiMo-V2-Pro is the stronger model overall, scoring 43.0 to 28.9 on the Noometry Index. Llama 3.2 3B costs 4.5× less per token, which makes it the better buy when MiMo-V2-Pro's lead doesn't matter for your workload.

Last verified . 13 shared benchmarks.

Llama 3.2 3B Meta

28.9

Rank #321 Confirmed

MiMo-V2-Pro Xiaomi

43.0

Rank #103 Confirmed

Summary

  • They share 13 benchmarks with published results for both. Llama 3.2 3B scores higher in 0 categories and MiMo-V2-Pro in 8 categories; 8 gaps are clear of the uncertainty.
  • The widest gap is in writing & preference, where MiMo-V2-Pro leads 62.8 to 24.7.
  • Llama 3.2 3B is cheaper at $0.05 / $0.33 per million input/output tokens, against $0.43 / $0.87 for MiMo-V2-Pro.
  • MiMo-V2-Pro accepts more context: 1.05M tokens versus 131K.
  • Llama 3.2 3B has downloadable open weights; the other is API-only.

Side by side

Llama 3.2 3BMiMo-V2-Pro
ProviderMetaXiaomi
Noometry Index28.943.0
Released2024-09-242026-03-18
WeightsOpenProprietary
Context window131K1.05M
Max output118K131K
Input $ / M tokens$0.05$0.43
Output $ / M tokens$0.33$0.87
Results tracked1823

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Category by category

Coding MiMo-V2-Pro leads

Llama 3.2 3B: 27.6 (#319), MiMo-V2-Pro: 43.8 (#83)

BenchmarkLlama 3.2 3BMiMo-V2-Pro
LMArena Coding10981476
LMArena WebDev—1433
BigCodeBench Instruct23.4%—
BigCodeBench Complete28.3%—
ALE-Bench—785.17

Agentic & Tool Use Not comparable

Llama 3.2 3B: 20.1 (#143), MiMo-V2-Pro: —

BenchmarkLlama 3.2 3BMiMo-V2-Pro
Berkeley Function Calling Leaderboard21.9%—
BALROG10.1%—

Reasoning MiMo-V2-Pro leads

Llama 3.2 3B: 21.0 (#228), MiMo-V2-Pro: 22.1 (#206)

BenchmarkLlama 3.2 3BMiMo-V2-Pro
LMArena Hard Prompts10951457
NYT Connections (extended)—25.8%
Thematic Generalization—45.9%

Math MiMo-V2-Pro leads

Llama 3.2 3B: 32.4 (#214), MiMo-V2-Pro: 39.5 (#102)

BenchmarkLlama 3.2 3BMiMo-V2-Pro
LMArena Math11261447

Knowledge MiMo-V2-Pro leads

Llama 3.2 3B: 29.7 (#235), MiMo-V2-Pro: 41.4 (#111)

BenchmarkLlama 3.2 3BMiMo-V2-Pro
LMArena Expert10901478

Multilingual MiMo-V2-Pro leads

Llama 3.2 3B: 26.2 (#281), MiMo-V2-Pro: 52.7 (#81)

BenchmarkLlama 3.2 3BMiMo-V2-Pro
LMArena Non-English10191416
LMArena Chinese10171456
LMArena German10561417
LMArena Russian9491427
LMArena French—1469
LMArena Japanese—1366
LMArena Korean—1400
LMArena Spanish—1457

Instruction Following MiMo-V2-Pro leads

Llama 3.2 3B: 56.0 (#275), MiMo-V2-Pro: 76.0 (#49)

BenchmarkLlama 3.2 3BMiMo-V2-Pro
LMArena Instruction Following10891445

Long Context MiMo-V2-Pro leads

Llama 3.2 3B: 33.4 (#261), MiMo-V2-Pro: 41.5 (#138)

BenchmarkLlama 3.2 3BMiMo-V2-Pro
LMArena Longer Query11001455
CL-bench—15.7%
CL-bench Life—6.9%

Writing & Preference MiMo-V2-Pro leads

Llama 3.2 3B: 24.7 (#307), MiMo-V2-Pro: 62.8 (#70)

BenchmarkLlama 3.2 3BMiMo-V2-Pro
LMArena Text11101436
LMArena Creative Writing10941415
LMArena Multi-Turn11051456
EQ-Bench Creative Writing595—

Frequently asked questions

Is Llama 3.2 3B better than MiMo-V2-Pro?

MiMo-V2-Pro is the stronger model overall, scoring 43.0 to 28.9 on the Noometry Index. Llama 3.2 3B costs 4.5× less per token, which makes it the better buy when MiMo-V2-Pro's lead doesn't matter for your workload.

Which is cheaper, Llama 3.2 3B or MiMo-V2-Pro?

Llama 3.2 3B is cheaper. It lists at $0.05 per million input tokens and $0.33 per million output tokens; MiMo-V2-Pro lists at $0.43 and $0.87.

Is Llama 3.2 3B or MiMo-V2-Pro better for coding?

MiMo-V2-Pro scores higher on coding benchmarks: 43.8 versus 27.6 in the Noometry coding category.

Which has the bigger context window?

MiMo-V2-Pro does, with 1.05M tokens against 131K.

How many benchmarks do Llama 3.2 3B and MiMo-V2-Pro share?

13 benchmarks have published results for both models. Llama 3.2 3B has 18 scored results on Noometry and MiMo-V2-Pro has 23.

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