Noometry

Model comparison

Llama 3.2 3B vs Mixtral 8x7B

Llama 3.2 3B is the stronger model overall, scoring 28.9 to 27.1 on the Noometry Index.

Last verified . 13 shared benchmarks.

Llama 3.2 3B Meta

28.9

Rank #321 Confirmed

Mixtral 8x7B Mistral AI

27.1

Rank #334 Confirmed

Summary

  • They share 13 benchmarks with published results for both. Llama 3.2 3B scores higher in 4 categories and Mixtral 8x7B in 4 categories; 7 gaps are clear of the uncertainty.
  • The widest gap is in knowledge, where Llama 3.2 3B leads 29.7 to 11.0.
  • Llama 3.2 3B is cheaper at $0.05 / $0.33 per million input/output tokens, against $0.70 / $0.70 for Mixtral 8x7B.
  • Llama 3.2 3B accepts more context: 131K tokens versus 32K.

Side by side

Llama 3.2 3BMixtral 8x7B
ProviderMetaMistral AI
Noometry Index28.927.1
Released2024-09-242023-12-11
WeightsOpenOpen
Context window131K32K
Max output118K32K
Input $ / M tokens$0.05$0.70
Output $ / M tokens$0.33$0.70
Results tracked1838

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Category by category

Coding Mixtral 8x7B leads

Llama 3.2 3B: 27.6 (#319), Mixtral 8x7B: 32.8 (#269)

BenchmarkLlama 3.2 3BMixtral 8x7B
LMArena Coding10981126
BigCodeBench Instruct23.4%—
BigCodeBench Complete28.3%—
HumanEval+—39.6%
MBPP+—49.7%

Agentic & Tool Use Not comparable

Llama 3.2 3B: 20.1 (#143), Mixtral 8x7B: —

BenchmarkLlama 3.2 3BMixtral 8x7B
Berkeley Function Calling Leaderboard21.9%—
BALROG10.1%—

Reasoning Llama 3.2 3B leads

Llama 3.2 3B: 21.0 (#228), Mixtral 8x7B: 18.2 (#285)

BenchmarkLlama 3.2 3BMixtral 8x7B
LMArena Hard Prompts10951115
DTBench—49.6%
Adversarial NLI—55.2%
Epoch Capabilities Index—118.47
ForecastBench—56.3
HellaSwag—86.7%
PIQA—83.6%
WinoGrande—77.2%

Math Llama 3.2 3B leads

Llama 3.2 3B: 32.4 (#214), Mixtral 8x7B: 18.8 (#289)

BenchmarkLlama 3.2 3BMixtral 8x7B
LMArena Math11261147
Omni-MATH—10.5%
MATH Level 5—10%
GSM8K—74.4%

Knowledge Llama 3.2 3B leads

Llama 3.2 3B: 29.7 (#235), Mixtral 8x7B: 11.0 (#301)

BenchmarkLlama 3.2 3BMixtral 8x7B
LMArena Expert10901088
GPQA Diamond—30.6%
MMLU-Pro—33.5%
GPQA (HELM)—29.6%
ARC (AI2) Challenge—87.3%
MMLU—70.6%
OpenBookQA—85.8%
TriviaQA—82.2%

Multilingual Mixtral 8x7B leads

Llama 3.2 3B: 26.2 (#281), Mixtral 8x7B: 29.6 (#266)

BenchmarkLlama 3.2 3BMixtral 8x7B
LMArena Non-English10191077
LMArena Chinese10171055
LMArena German10561114
LMArena Russian9491090
LMArena French—1166
LMArena Japanese—931
LMArena Korean—968
LMArena Spanish—1111

Instruction Following Llama 3.2 3B leads

Llama 3.2 3B: 56.0 (#275), Mixtral 8x7B: 51.0 (#297)

BenchmarkLlama 3.2 3BMixtral 8x7B
LMArena Instruction Following10891109
IFEval—57.5%

Long Context Too close to call

Llama 3.2 3B: 33.4 (#261), Mixtral 8x7B: 33.4 (#260)

BenchmarkLlama 3.2 3BMixtral 8x7B
LMArena Longer Query11001103

Writing & Preference Mixtral 8x7B leads

Llama 3.2 3B: 24.7 (#307), Mixtral 8x7B: 34.2 (#270)

BenchmarkLlama 3.2 3BMixtral 8x7B
LMArena Text11101132
LMArena Creative Writing10941109
LMArena Multi-Turn11051115
EQ-Bench Creative Writing595—
WildBench—67.3%

Frequently asked questions

Is Llama 3.2 3B better than Mixtral 8x7B?

Llama 3.2 3B is the stronger model overall, scoring 28.9 to 27.1 on the Noometry Index.

Which is cheaper, Llama 3.2 3B or Mixtral 8x7B?

Llama 3.2 3B is cheaper. It lists at $0.05 per million input tokens and $0.33 per million output tokens; Mixtral 8x7B lists at $0.70 and $0.70.

Is Llama 3.2 3B or Mixtral 8x7B better for coding?

Mixtral 8x7B scores higher on coding benchmarks: 32.8 versus 27.6 in the Noometry coding category.

Which has the bigger context window?

Llama 3.2 3B does, with 131K tokens against 32K.

How many benchmarks do Llama 3.2 3B and Mixtral 8x7B share?

13 benchmarks have published results for both models. Llama 3.2 3B has 18 scored results on Noometry and Mixtral 8x7B has 38.

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