Noometry

Model comparison

Llama 3.2 1B vs MiMo-V2-Pro

MiMo-V2-Pro is the stronger model overall, scoring 43.0 to 20.1 on the Noometry Index. Llama 3.2 1B costs 7.7× 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 1B Meta

20.1

Rank #354 Confirmed

MiMo-V2-Pro Xiaomi

43.0

Rank #103 Confirmed

Summary

  • They share 13 benchmarks with published results for both. Llama 3.2 1B 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 21.3.
  • Llama 3.2 1B is cheaper at $0.027 / $0.20 per million input/output tokens, against $0.43 / $0.87 for MiMo-V2-Pro.
  • MiMo-V2-Pro accepts more context: 1.05M tokens versus 60K.
  • Llama 3.2 1B has downloadable open weights; the other is API-only.

Side by side

Llama 3.2 1BMiMo-V2-Pro
ProviderMetaXiaomi
Noometry Index20.143.0
Released2024-09-242026-03-18
WeightsOpenProprietary
Context window60K1.05M
Max output54K131K
Input $ / M tokens$0.027$0.43
Output $ / M tokens$0.20$0.87
Results tracked2223

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Category by category

Coding MiMo-V2-Pro leads

Llama 3.2 1B: 21.1 (#338), MiMo-V2-Pro: 43.8 (#83)

BenchmarkLlama 3.2 1BMiMo-V2-Pro
LMArena Coding10701476
LMArena WebDev—1433
BigCodeBench Instruct8.2%—
BigCodeBench Complete11.3%—
ALE-Bench—785.17

Agentic & Tool Use Not comparable

Llama 3.2 1B: 14.6 (#150), MiMo-V2-Pro: —

BenchmarkLlama 3.2 1BMiMo-V2-Pro
Berkeley Function Calling Leaderboard10.8%—
BALROG6.6%—

Reasoning MiMo-V2-Pro leads

Llama 3.2 1B: 16.2 (#308), MiMo-V2-Pro: 22.1 (#206)

BenchmarkLlama 3.2 1BMiMo-V2-Pro
LMArena Hard Prompts10441457
NYT Connections (extended)—25.8%
Chess Puzzles0%—
Thematic Generalization—45.9%
Epoch Capabilities Index101.99—

Math MiMo-V2-Pro leads

Llama 3.2 1B: 10.4 (#313), MiMo-V2-Pro: 39.5 (#102)

BenchmarkLlama 3.2 1BMiMo-V2-Pro
LMArena Math10861447
OTIS Mock AIME 2024-20250.6%—

Knowledge MiMo-V2-Pro leads

Llama 3.2 1B: 7.2 (#312), MiMo-V2-Pro: 41.4 (#111)

BenchmarkLlama 3.2 1BMiMo-V2-Pro
LMArena Expert10071478
GPQA Diamond23.9%—

Multilingual MiMo-V2-Pro leads

Llama 3.2 1B: 23.8 (#292), MiMo-V2-Pro: 52.7 (#81)

BenchmarkLlama 3.2 1BMiMo-V2-Pro
LMArena Non-English9731416
LMArena Chinese9591456
LMArena German10141417
LMArena Russian9411427
LMArena French—1469
LMArena Japanese—1366
LMArena Korean—1400
LMArena Spanish—1457

Instruction Following MiMo-V2-Pro leads

Llama 3.2 1B: 52.4 (#290), MiMo-V2-Pro: 76.0 (#49)

BenchmarkLlama 3.2 1BMiMo-V2-Pro
LMArena Instruction Following10311445

Long Context MiMo-V2-Pro leads

Llama 3.2 1B: 31.9 (#274), MiMo-V2-Pro: 41.5 (#138)

BenchmarkLlama 3.2 1BMiMo-V2-Pro
LMArena Longer Query10501455
CL-bench—15.7%
CL-bench Life—6.9%

Writing & Preference MiMo-V2-Pro leads

Llama 3.2 1B: 21.3 (#310), MiMo-V2-Pro: 62.8 (#70)

BenchmarkLlama 3.2 1BMiMo-V2-Pro
LMArena Text10551436
LMArena Creative Writing10331415
LMArena Multi-Turn10301456
EQ-Bench Creative Writing200—

Frequently asked questions

Is Llama 3.2 1B better than MiMo-V2-Pro?

MiMo-V2-Pro is the stronger model overall, scoring 43.0 to 20.1 on the Noometry Index. Llama 3.2 1B costs 7.7× 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 1B or MiMo-V2-Pro?

Llama 3.2 1B is cheaper. It lists at $0.027 per million input tokens and $0.20 per million output tokens; MiMo-V2-Pro lists at $0.43 and $0.87.

Is Llama 3.2 1B or MiMo-V2-Pro better for coding?

MiMo-V2-Pro scores higher on coding benchmarks: 43.8 versus 21.1 in the Noometry coding category.

Which has the bigger context window?

MiMo-V2-Pro does, with 1.05M tokens against 60K.

How many benchmarks do Llama 3.2 1B and MiMo-V2-Pro share?

13 benchmarks have published results for both models. Llama 3.2 1B has 22 scored results on Noometry and MiMo-V2-Pro has 23.

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