Model comparison

Llama 2-70B vs Llama 3.1-405B

Llama 3.1-405B is the stronger model overall, scoring 30.7 to 24.4 on the Noometry Index.

Last verified . 30 shared benchmarks.

Llama 2-70B Meta

24.4

Rank #349 Confirmed

Llama 3.1-405B Meta

30.7

Rank #288 Confirmed

Summary

  • They share 30 benchmarks with published results for both. Llama 2-70B scores higher in 0 categories and Llama 3.1-405B in 8 categories; 8 gaps are clear of the uncertainty.
  • The widest gap is in knowledge, where Llama 3.1-405B leads 30.4 to 7.4.
  • The biggest single-benchmark swing is MATH Level 5: 3.3% for Llama 2-70B and 49.8% for Llama 3.1-405B.

Side by side

Llama 2-70B and Llama 3.1-405B specifications
Llama 2-70BLlama 3.1-405B
ProviderMetaMeta
Noometry Index24.430.7
Released2023-07-182024-07-23
WeightsOpenOpen
Context window——
Max output——
Input $ / M tokens——
Output $ / M tokens——
Results tracked3542

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Category by category

Coding Llama 3.1-405B leads

Llama 2-70B: 31.4 (#286), Llama 3.1-405B: 33.1 (#262)

Coding benchmarks
BenchmarkLlama 2-70BLlama 3.1-405B
LMArena Coding10791291
WeirdML—21.4%

Agentic & Tool Use Not comparable

Llama 2-70B: —, Llama 3.1-405B: 21.0 (#140)

Agentic & Tool Use benchmarks
BenchmarkLlama 2-70BLlama 3.1-405B
TheAgentCompany—7.4%
Cybench—7.5%

Reasoning Llama 3.1-405B leads

Llama 2-70B: 14.4 (#325), Llama 3.1-405B: 16.8 (#300)

Reasoning benchmarks
BenchmarkLlama 2-70BLlama 3.1-405B
LMArena Hard Prompts10731269
DTBench41.6%61.4%
BIG-Bench Hard64.9%82.9%
Epoch Capabilities Index113.79128.75
ForecastBench51.459.9
HellaSwag85.3%89.2%
PIQA82.8%85.9%
WinoGrande80.2%89.2%
SimpleBench—23%
Kagi LLM Benchmark—45%
CommonsenseQA 2.050%—
LAMBADA78.9%—

Math Llama 3.1-405B leads

Llama 2-70B: 8.1 (#326), Llama 3.1-405B: 18.4 (#290)

Math benchmarks
BenchmarkLlama 2-70BLlama 3.1-405B
OTIS Mock AIME 2024-20250%9.7%
LMArena Math10911281
MATH Level 53.3%49.8%
Omni-MATH—24.9%
GSM8K69.6%—

Knowledge Llama 3.1-405B leads

Llama 2-70B: 7.4 (#310), Llama 3.1-405B: 30.4 (#227)

Knowledge benchmarks
BenchmarkLlama 2-70BLlama 3.1-405B
GPQA Diamond26.3%50.9%
LMArena Expert10391243
ARC (AI2) Challenge78.3%95.3%
MMLU69.9%84.5%
TriviaQA87.6%82.7%
MMLU-Pro—72.3%
Confabulations—17.6%
GPQA (HELM)—52.2%
BoolQ88.6%—
OpenBookQA60.2%—

Multilingual Llama 3.1-405B leads

Llama 2-70B: 27.7 (#274), Llama 3.1-405B: 40.7 (#214)

Multilingual benchmarks
BenchmarkLlama 2-70BLlama 3.1-405B
LMArena Non-English10451248
LMArena Chinese9951242
LMArena French10901279
LMArena German10411252
LMArena Japanese9271208
LMArena Korean9641184
LMArena Russian10831265
LMArena Spanish11431260

Instruction Following Llama 3.1-405B leads

Llama 2-70B: 54.9 (#278), Llama 3.1-405B: 65.9 (#214)

Instruction Following benchmarks
BenchmarkLlama 2-70BLlama 3.1-405B
LMArena Instruction Following10711259
IFEval—81.1%

Long Context Llama 3.1-405B leads

Llama 2-70B: 32.3 (#270), Llama 3.1-405B: 38.4 (#197)

Long Context benchmarks
BenchmarkLlama 2-70BLlama 3.1-405B
LMArena Longer Query10621266

Writing & Preference Llama 3.1-405B leads

Llama 2-70B: 32.3 (#279), Llama 3.1-405B: 38.9 (#251)

Writing & Preference benchmarks
BenchmarkLlama 2-70BLlama 3.1-405B
LMArena Text11151284
LMArena Creative Writing10751262
LMArena Multi-Turn10881297
EQ-Bench Creative Writing—870
WildBench—78.3%

Frequently asked questions

Is Llama 2-70B better than Llama 3.1-405B?

Llama 3.1-405B is the stronger model overall, scoring 30.7 to 24.4 on the Noometry Index.

Is Llama 2-70B or Llama 3.1-405B better for coding?

Llama 3.1-405B scores higher on coding benchmarks: 33.1 versus 31.4 in the Noometry coding category.

How many benchmarks do Llama 2-70B and Llama 3.1-405B share?

30 benchmarks have published results for both models. Llama 2-70B has 35 scored results on Noometry and Llama 3.1-405B has 42.

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