Noometry

Model comparison

DeepSeek V4 Flash vs Llama 3-8B

DeepSeek V4 Flash is the stronger model overall, scoring 53.6 to 25.5 on the Noometry Index.

Last verified . 22 shared benchmarks.

DeepSeek V4 Flash DeepSeek

53.6

Rank #35 Confirmed

Llama 3-8B Meta

25.5

Rank #344 Confirmed

Summary

  • They share 22 benchmarks with published results for both. DeepSeek V4 Flash scores higher in 8 categories and Llama 3-8B in 0 categories; 8 gaps are clear of the uncertainty.
  • The widest gap is in math, where DeepSeek V4 Flash leads 60.3 to 8.8.
  • The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 94.4% for DeepSeek V4 Flash and 1.9% for Llama 3-8B.

Side by side

DeepSeek V4 FlashLlama 3-8B
ProviderDeepSeekMeta
Noometry Index53.625.5
Released2026-04-242024-04-18
WeightsOpenOpen
Context window1M—
Max output393K—
Input $ / M tokens$0.15—
Output $ / M tokens$0.60—
Results tracked4134

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Category by category

Coding DeepSeek V4 Flash leads

DeepSeek V4 Flash: 47.9 (#59), Llama 3-8B: 31.0 (#289)

BenchmarkDeepSeek V4 FlashLlama 3-8B
LMArena Coding14571152
FrontierCode18.8%—
LMArena WebDev1582—
SciCode49.9%—
WeirdML63%—
BigCodeBench Instruct—31.9%
BigCodeBench Complete—36.9%
ALE-Bench1,306—
HumanEval+—56.7%
MBPP+—54.8%

Reasoning DeepSeek V4 Flash leads

DeepSeek V4 Flash: 53.7 (#30), Llama 3-8B: 14.3 (#326)

BenchmarkDeepSeek V4 FlashLlama 3-8B
Chess Puzzles33%0%
LMArena Hard Prompts14441133
DTBench90.9%43.9%
Epoch Capabilities Index154.49116.45
ARC-AGI-261.4%—
SimpleBench61.1%—
Kagi LLM Benchmark52.2%—
NYT Connections (extended)89.6%—
ARC-AGI-189%—
CritPt16.6%—
Mystery Game Puzzles34%—
LMCA41.7%—
Adversarial NLI—57.3%
ForecastBench—58.6
WinoGrande—75.7%

Math DeepSeek V4 Flash leads

DeepSeek V4 Flash: 60.3 (#37), Llama 3-8B: 8.8 (#323)

BenchmarkDeepSeek V4 FlashLlama 3-8B
OTIS Mock AIME 2024-202594.4%1.9%
LMArena Math14271151
FrontierMath (Tiers 1-3)57.5%—
FrontierMath Tier 424.4%—
MathArena Final-Answer Competitions76.5%—
ProofBench56%—
MATH Level 5—6.1%

Knowledge DeepSeek V4 Flash leads

DeepSeek V4 Flash: 55.4 (#48), Llama 3-8B: 7.8 (#308)

BenchmarkDeepSeek V4 FlashLlama 3-8B
GPQA Diamond91%26.1%
LMArena Expert14411113
SimpleQA Verified33.6%—
ARC (AI2) Challenge—82.8%
MMLU—68.8%
OpenBookQA—82.6%
TriviaQA—67.7%

Multilingual DeepSeek V4 Flash leads

DeepSeek V4 Flash: 53.0 (#72), Llama 3-8B: 30.8 (#261)

BenchmarkDeepSeek V4 FlashLlama 3-8B
LMArena Non-English14201098
LMArena Chinese14681076
LMArena French14391159
LMArena German14181104
LMArena Japanese1406967
LMArena Korean13841004
LMArena Russian14281109
LMArena Spanish14361173

Instruction Following DeepSeek V4 Flash leads

DeepSeek V4 Flash: 74.9 (#81), Llama 3-8B: 58.4 (#260)

BenchmarkDeepSeek V4 FlashLlama 3-8B
LMArena Instruction Following14211127

Long Context DeepSeek V4 Flash leads

DeepSeek V4 Flash: 43.8 (#85), Llama 3-8B: 34.2 (#251)

BenchmarkDeepSeek V4 FlashLlama 3-8B
LMArena Longer Query14341128

Writing & Preference DeepSeek V4 Flash leads

DeepSeek V4 Flash: 63.8 (#61), Llama 3-8B: 37.5 (#256)

BenchmarkDeepSeek V4 FlashLlama 3-8B
LMArena Text14321166
LMArena Creative Writing14031150
LMArena Multi-Turn14491152
EQ-Bench Creative Writing1559—

Frequently asked questions

Is DeepSeek V4 Flash better than Llama 3-8B?

DeepSeek V4 Flash is the stronger model overall, scoring 53.6 to 25.5 on the Noometry Index.

Is DeepSeek V4 Flash or Llama 3-8B better for coding?

DeepSeek V4 Flash scores higher on coding benchmarks: 47.9 versus 31.0 in the Noometry coding category.

How many benchmarks do DeepSeek V4 Flash and Llama 3-8B share?

22 benchmarks have published results for both models. DeepSeek V4 Flash has 41 scored results on Noometry and Llama 3-8B has 34.

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