Noometry

Model comparison

GPT-4.1 nano vs Llama 3-8B

GPT-4.1 nano is the stronger model overall, scoring 27.9 to 25.5 on the Noometry Index.

Last verified . 19 shared benchmarks.

GPT-4.1 nano OpenAI

27.9

Rank #327 Confirmed

Llama 3-8B Meta

25.5

Rank #344 Confirmed

Summary

  • They share 19 benchmarks with published results for both. GPT-4.1 nano scores higher in 5 categories and Llama 3-8B in 3 categories; 8 gaps are clear of the uncertainty.
  • The widest gap is in math, where GPT-4.1 nano leads 26.9 to 8.8.
  • The biggest single-benchmark swing is MATH Level 5: 70% for GPT-4.1 nano and 6.1% for Llama 3-8B.
  • Llama 3-8B has downloadable open weights; the other is API-only.

Side by side

GPT-4.1 nanoLlama 3-8B
ProviderOpenAIMeta
Noometry Index27.925.5
Released2025-04-142024-04-18
WeightsProprietaryOpen
Context window1.05M—
Max output33K—
Input $ / M tokens$0.10—
Output $ / M tokens$0.40—
Results tracked3834

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Category by category

Coding Llama 3-8B leads

GPT-4.1 nano: 24.1 (#330), Llama 3-8B: 31.0 (#289)

BenchmarkGPT-4.1 nanoLlama 3-8B
LMArena Coding13061152
Aider Polyglot8.9%—
SciCode25.9%—
WeirdML19%—
BigCodeBench Instruct—31.9%
BigCodeBench Complete—36.9%
HumanEval+—56.7%
MBPP+—54.8%

Agentic & Tool Use Not comparable

GPT-4.1 nano: 26.5 (#104), Llama 3-8B: —

BenchmarkGPT-4.1 nanoLlama 3-8B
Berkeley Function Calling Leaderboard33%—

Reasoning Llama 3-8B leads

GPT-4.1 nano: 8.5 (#349), Llama 3-8B: 14.3 (#326)

BenchmarkGPT-4.1 nanoLlama 3-8B
LMArena Hard Prompts12861133
DTBench52.5%43.9%
Epoch Capabilities Index129.62116.45
ARC-AGI-20%—
Kagi LLM Benchmark33.3%—
ARC-AGI-10%—
CritPt0%—
Chess Puzzles—0%
LMCA5.5%—
Adversarial NLI—57.3%
ForecastBench—58.6
WinoGrande—75.7%

Math GPT-4.1 nano leads

GPT-4.1 nano: 26.9 (#252), Llama 3-8B: 8.8 (#323)

BenchmarkGPT-4.1 nanoLlama 3-8B
OTIS Mock AIME 2024-202528.9%1.9%
LMArena Math12741151
MATH Level 570%6.1%
Omni-MATH36.7%—
FrontierMath (Feb 2025 set)1%—

Knowledge GPT-4.1 nano leads

GPT-4.1 nano: 21.8 (#273), Llama 3-8B: 7.8 (#308)

BenchmarkGPT-4.1 nanoLlama 3-8B
GPQA Diamond48.9%26.1%
LMArena Expert12721113
SimpleQA Verified6%—
MMLU-Pro55%—
GPQA (HELM)50.7%—
ARC (AI2) Challenge—82.8%
MMLU—68.8%
OpenBookQA—82.6%
TriviaQA—67.7%

Multimodal Not comparable

GPT-4.1 nano: 29.2 (#113), Llama 3-8B: —

BenchmarkGPT-4.1 nanoLlama 3-8B
LMArena Vision1063—

Multilingual GPT-4.1 nano leads

GPT-4.1 nano: 41.6 (#205), Llama 3-8B: 30.8 (#261)

BenchmarkGPT-4.1 nanoLlama 3-8B
LMArena Non-English12601098
LMArena Chinese12701076
LMArena German12881104
LMArena Japanese1198967
LMArena Russian12611109
LMArena French—1159
LMArena Korean—1004
LMArena Spanish—1173

Instruction Following GPT-4.1 nano leads

GPT-4.1 nano: 67.8 (#193), Llama 3-8B: 58.4 (#260)

BenchmarkGPT-4.1 nanoLlama 3-8B
LMArena Instruction Following12671127
IFEval84.3%—

Long Context Llama 3-8B leads

GPT-4.1 nano: 23.7 (#296), Llama 3-8B: 34.2 (#251)

BenchmarkGPT-4.1 nanoLlama 3-8B
LMArena Longer Query12831128
Fiction.LiveBench25%—

Writing & Preference GPT-4.1 nano leads

GPT-4.1 nano: 40.5 (#243), Llama 3-8B: 37.5 (#256)

BenchmarkGPT-4.1 nanoLlama 3-8B
LMArena Text12851166
LMArena Creative Writing12601150
LMArena Multi-Turn12771152
EQ-Bench Creative Writing946—
WildBench81.2%—

Frequently asked questions

Is GPT-4.1 nano better than Llama 3-8B?

GPT-4.1 nano is the stronger model overall, scoring 27.9 to 25.5 on the Noometry Index.

Is GPT-4.1 nano or Llama 3-8B better for coding?

Llama 3-8B scores higher on coding benchmarks: 31.0 versus 24.1 in the Noometry coding category.

How many benchmarks do GPT-4.1 nano and Llama 3-8B share?

19 benchmarks have published results for both models. GPT-4.1 nano has 38 scored results on Noometry and Llama 3-8B has 34.

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