Noometry

Model comparison

Llama 3.2 3B vs Mistral 7B

Llama 3.2 3B is the stronger model overall, scoring 28.9 to 23.0 on the Noometry Index.

Last verified . 15 shared benchmarks.

Llama 3.2 3B Meta

28.9

Rank #321 Confirmed

Mistral 7B Mistral AI

23.0

Rank #351 Confirmed

Summary

  • They share 15 benchmarks with published results for both. Llama 3.2 3B scores higher in 7 categories and Mistral 7B in 1 category; 7 gaps are clear of the uncertainty.
  • The widest gap is in math, where Llama 3.2 3B leads 32.4 to 8.1.
  • Llama 3.2 3B is cheaper at $0.05 / $0.33 per million input/output tokens, against $0.25 / $0.25 for Mistral 7B.
  • Llama 3.2 3B accepts more context: 131K tokens versus 8K.

Side by side

Llama 3.2 3BMistral 7B
ProviderMetaMistral AI
Noometry Index28.923.0
Released2024-09-242023-09-27
WeightsOpenOpen
Context window131K8K
Max output118K8K
Input $ / M tokens$0.05$0.25
Output $ / M tokens$0.33$0.25
Results tracked1837

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Category by category

Coding Llama 3.2 3B leads

Llama 3.2 3B: 27.6 (#319), Mistral 7B: 26.4 (#326)

BenchmarkLlama 3.2 3BMistral 7B
BigCodeBench Instruct23.4%19.5%
LMArena Coding10981082
BigCodeBench Complete28.3%27.3%
HumanEval+—36%
MBPP+—42.1%

Agentic & Tool Use Not comparable

Llama 3.2 3B: 20.1 (#143), Mistral 7B: —

BenchmarkLlama 3.2 3BMistral 7B
Berkeley Function Calling Leaderboard21.9%—
BALROG10.1%—

Reasoning Llama 3.2 3B leads

Llama 3.2 3B: 21.0 (#228), Mistral 7B: 13.1 (#336)

BenchmarkLlama 3.2 3BMistral 7B
LMArena Hard Prompts10951067
Chess Puzzles—0%
DTBench—42.5%
Adversarial NLI—47.1%
BIG-Bench Hard—56.1%
Epoch Capabilities Index—112.21
HellaSwag—81%
PIQA—83%
WinoGrande—75.3%

Math Llama 3.2 3B leads

Llama 3.2 3B: 32.4 (#214), Mistral 7B: 8.1 (#325)

BenchmarkLlama 3.2 3BMistral 7B
LMArena Math11261085
OTIS Mock AIME 2024-2025—0.3%
MATH Level 5—3.7%
GSM8K—54.4%

Knowledge Llama 3.2 3B leads

Llama 3.2 3B: 29.7 (#235), Mistral 7B: 7.4 (#311)

BenchmarkLlama 3.2 3BMistral 7B
LMArena Expert10901036
GPQA Diamond—15.2%
ARC (AI2) Challenge—78.6%
BoolQ—87.4%
MMLU—62.5%
OpenBookQA—79.8%
TriviaQA—75.2%

Multilingual Too close to call

Llama 3.2 3B: 26.2 (#281), Mistral 7B: 25.8 (#283)

BenchmarkLlama 3.2 3BMistral 7B
LMArena Non-English10191012
LMArena Chinese10171009
LMArena German1056987
LMArena Russian9491018
LMArena French—1037
LMArena Japanese—878
LMArena Spanish—1026

Instruction Following Llama 3.2 3B leads

Llama 3.2 3B: 56.0 (#275), Mistral 7B: 54.2 (#280)

BenchmarkLlama 3.2 3BMistral 7B
LMArena Instruction Following10891060

Long Context Llama 3.2 3B leads

Llama 3.2 3B: 33.4 (#261), Mistral 7B: 32.2 (#271)

BenchmarkLlama 3.2 3BMistral 7B
LMArena Longer Query11001060

Writing & Preference Mistral 7B leads

Llama 3.2 3B: 24.7 (#307), Mistral 7B: 30.7 (#286)

BenchmarkLlama 3.2 3BMistral 7B
LMArena Text11101090
LMArena Creative Writing10941068
LMArena Multi-Turn11051062
EQ-Bench Creative Writing595—

Frequently asked questions

Is Llama 3.2 3B better than Mistral 7B?

Llama 3.2 3B is the stronger model overall, scoring 28.9 to 23.0 on the Noometry Index.

Which is cheaper, Llama 3.2 3B or Mistral 7B?

Llama 3.2 3B is cheaper. It lists at $0.05 per million input tokens and $0.33 per million output tokens; Mistral 7B lists at $0.25 and $0.25.

Is Llama 3.2 3B or Mistral 7B better for coding?

Llama 3.2 3B scores higher on coding benchmarks: 27.6 versus 26.4 in the Noometry coding category.

Which has the bigger context window?

Llama 3.2 3B does, with 131K tokens against 8K.

How many benchmarks do Llama 3.2 3B and Mistral 7B share?

15 benchmarks have published results for both models. Llama 3.2 3B has 18 scored results on Noometry and Mistral 7B has 37.

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