Noometry

Model comparison

GPT-4.1 nano vs Llama 2-13B

Llama 2-13B is the stronger model overall, scoring 29.1 to 27.7 on the Noometry Index.

Last verified . 16 shared benchmarks.

GPT-4.1 nano OpenAI

27.7

Rank #322 Confirmed

Llama 2-13B Meta

29.1

Rank #304 Confirmed

Summary

  • They share 16 benchmarks with published results for both. GPT-4.1 nano scores higher in 3 categories and Llama 2-13B in 5 categories; 8 gaps are clear of the uncertainty.
  • The widest gap is in multilingual, where GPT-4.1 nano leads 41.8 to 26.7.
  • The biggest single-benchmark swing is DTBench: 52.5% for GPT-4.1 nano and 42.2% for Llama 2-13B.
  • Llama 2-13B has downloadable open weights; the other is API-only.

Side by side

GPT-4.1 nanoLlama 2-13B
ProviderOpenAIMeta
Noometry Index27.729.1
Released2025-04-142023-07-18
WeightsProprietaryOpen
Context window1.05M—
Max output33K—
Input $ / M tokens$0.10—
Output $ / M tokens$0.40—
Results tracked3832

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Category by category

Coding Llama 2-13B leads

GPT-4.1 nano: 23.8 (#325), Llama 2-13B: 30.4 (#287)

BenchmarkGPT-4.1 nanoLlama 2-13B
LMArena Coding13061062
Aider Polyglot8.9%—
SciCode25.9%—
WeirdML19%—

Agentic & Tool Use Not comparable

GPT-4.1 nano: 26.3 (#98), Llama 2-13B: —

BenchmarkGPT-4.1 nanoLlama 2-13B
Berkeley Function Calling Leaderboard33%—

Reasoning Llama 2-13B leads

GPT-4.1 nano: 8.6 (#344), Llama 2-13B: 12.3 (#333)

BenchmarkGPT-4.1 nanoLlama 2-13B
LMArena Hard Prompts12861051
DTBench52.5%42.2%
Epoch Capabilities Index129.62105.8
ARC-AGI-20%—
Kagi LLM Benchmark33.3%—
ARC-AGI-10%—
CritPt0%—
Chess Puzzles—0%
LMCA5.5%—
BIG-Bench Hard—58.2%
HellaSwag—80.7%
LAMBADA—76.5%
PIQA—80.8%
WinoGrande—72.8%

Math Llama 2-13B leads

GPT-4.1 nano: 25.5 (#246), Llama 2-13B: 28.7 (#226)

BenchmarkGPT-4.1 nanoLlama 2-13B
LMArena Math12741065
OTIS Mock AIME 2024-202528.9%—
Omni-MATH36.7%—
MATH Level 570%—
FrontierMath (Feb 2025 set)1%—
GSM8K—36.9%

Knowledge Llama 2-13B leads

GPT-4.1 nano: 21.8 (#268), Llama 2-13B: 27.5 (#244)

BenchmarkGPT-4.1 nanoLlama 2-13B
LMArena Expert12721030
GPQA Diamond48.9%—
SimpleQA Verified6%—
MMLU-Pro55%—
GPQA (HELM)50.7%—
ARC (AI2) Challenge—60.3%
BoolQ—82.4%
MMLU—55.6%
OpenBookQA—57%
TriviaQA—79.6%

Multimodal Not comparable

GPT-4.1 nano: 28.6 (#108), Llama 2-13B: —

BenchmarkGPT-4.1 nanoLlama 2-13B
LMArena Vision1063—
ScienceQA—55.8%

Multilingual GPT-4.1 nano leads

GPT-4.1 nano: 41.8 (#200), Llama 2-13B: 26.7 (#274)

BenchmarkGPT-4.1 nanoLlama 2-13B
LMArena Non-English12601024
LMArena Chinese12701001
LMArena German12881009
LMArena Japanese1199894
LMArena Russian12611055
LMArena French—1043
LMArena Korean—953
LMArena Spanish—1086

Instruction Following GPT-4.1 nano leads

GPT-4.1 nano: 68.0 (#188), Llama 2-13B: 53.5 (#282)

BenchmarkGPT-4.1 nanoLlama 2-13B
LMArena Instruction Following12671046
IFEval84.3%—

Long Context Llama 2-13B leads

GPT-4.1 nano: 23.7 (#291), Llama 2-13B: 32.3 (#264)

BenchmarkGPT-4.1 nanoLlama 2-13B
LMArena Longer Query12831064
Fiction.LiveBench25%—

Writing & Preference GPT-4.1 nano leads

GPT-4.1 nano: 40.7 (#238), Llama 2-13B: 30.0 (#284)

BenchmarkGPT-4.1 nanoLlama 2-13B
LMArena Text12851085
LMArena Creative Writing12601047
LMArena Multi-Turn12771050
EQ-Bench Creative Writing946—
WildBench81.2%—

Frequently asked questions

Is GPT-4.1 nano better than Llama 2-13B?

Llama 2-13B is the stronger model overall, scoring 29.1 to 27.7 on the Noometry Index.

Is GPT-4.1 nano or Llama 2-13B better for coding?

Llama 2-13B scores higher on coding benchmarks: 30.4 versus 23.8 in the Noometry coding category.

How many benchmarks do GPT-4.1 nano and Llama 2-13B share?

16 benchmarks have published results for both models. GPT-4.1 nano has 38 scored results on Noometry and Llama 2-13B has 32.

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