Noometry

Model comparison

DeepSeek V4 Flash vs Llama 2-7B

DeepSeek V4 Flash is the stronger model overall, scoring 53.6 to 28.6 on the Noometry Index.

Last verified . 17 shared benchmarks.

DeepSeek V4 Flash DeepSeek

53.6

Rank #32 Confirmed

Llama 2-7B Meta

28.6

Rank #315 Confirmed

Summary

  • They share 17 benchmarks with published results for both. DeepSeek V4 Flash scores higher in 8 categories and Llama 2-7B in 0 categories; 8 gaps are clear of the uncertainty.
  • The widest gap is in reasoning, where DeepSeek V4 Flash leads 53.7 to 15.1.
  • The biggest single-benchmark swing is Chess Puzzles: 33% for DeepSeek V4 Flash and 0% for Llama 2-7B.

Side by side

DeepSeek V4 FlashLlama 2-7B
ProviderDeepSeekMeta
Noometry Index53.628.6
Released2026-04-242023-07-18
WeightsOpenOpen
Context window1M—
Max output393K—
Input $ / M tokens$0.15—
Output $ / M tokens$0.60—
Results tracked4129

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Category by category

Coding DeepSeek V4 Flash leads

DeepSeek V4 Flash: 47.6 (#56), Llama 2-7B: 28.7 (#302)

BenchmarkDeepSeek V4 FlashLlama 2-7B
LMArena Coding14571002
FrontierCode18.8%—
LMArena WebDev1581—
SciCode49.9%—
WeirdML63%—
ALE-Bench1,306—

Reasoning DeepSeek V4 Flash leads

DeepSeek V4 Flash: 53.7 (#25), Llama 2-7B: 15.1 (#309)

BenchmarkDeepSeek V4 FlashLlama 2-7B
Chess Puzzles33%0%
LMArena Hard Prompts14431009
Epoch Capabilities Index154.5198.54
ARC-AGI-261.4%—
SimpleBench61.1%—
Kagi LLM Benchmark52.2%—
NYT Connections (extended)89.6%—
ARC-AGI-189%—
CritPt16.6%—
Mystery Game Puzzles34%—
DTBench90.9%—
LMCA41.7%—
BIG-Bench Hard—39.2%
HellaSwag—77.2%
LAMBADA—73.3%
PIQA—78.8%
WinoGrande—69.2%

Math DeepSeek V4 Flash leads

DeepSeek V4 Flash: 60.0 (#31), Llama 2-7B: 28.2 (#231)

BenchmarkDeepSeek V4 FlashLlama 2-7B
LMArena Math14271042
FrontierMath (Tiers 1-3)57.5%—
FrontierMath Tier 424.4%—
MathArena Final-Answer Competitions76.5%—
OTIS Mock AIME 2024-202594.4%—
ProofBench56%—
GSM8K—16.7%

Knowledge DeepSeek V4 Flash leads

DeepSeek V4 Flash: 55.5 (#43), Llama 2-7B: 27.7 (#243)

BenchmarkDeepSeek V4 FlashLlama 2-7B
LMArena Expert14411036
GPQA Diamond91%—
SimpleQA Verified33.6%—
ARC (AI2) Challenge—45.9%
BoolQ—77.9%
MMLU—45.8%
OpenBookQA—58.6%
TriviaQA—73.7%

Multimodal Not comparable

DeepSeek V4 Flash: —, Llama 2-7B: —

BenchmarkDeepSeek V4 FlashLlama 2-7B
ScienceQA—43.1%

Multilingual DeepSeek V4 Flash leads

DeepSeek V4 Flash: 53.2 (#71), Llama 2-7B: 23.9 (#288)

BenchmarkDeepSeek V4 FlashLlama 2-7B
LMArena Non-English1420973
LMArena Chinese1467973
LMArena French1438969
LMArena German1418978
LMArena Russian1428995
LMArena Spanish14371007
LMArena Japanese1403—
LMArena Korean1383—

Instruction Following DeepSeek V4 Flash leads

DeepSeek V4 Flash: 75.1 (#76), Llama 2-7B: 51.0 (#293)

BenchmarkDeepSeek V4 FlashLlama 2-7B
LMArena Instruction Following14211006

Long Context DeepSeek V4 Flash leads

DeepSeek V4 Flash: 43.8 (#81), Llama 2-7B: 30.4 (#282)

BenchmarkDeepSeek V4 FlashLlama 2-7B
LMArena Longer Query1435999

Writing & Preference DeepSeek V4 Flash leads

DeepSeek V4 Flash: 64.0 (#59), Llama 2-7B: 28.1 (#293)

BenchmarkDeepSeek V4 FlashLlama 2-7B
LMArena Text14321053
LMArena Creative Writing14031033
LMArena Multi-Turn14491029
EQ-Bench Creative Writing1559—

Frequently asked questions

Is DeepSeek V4 Flash better than Llama 2-7B?

DeepSeek V4 Flash is the stronger model overall, scoring 53.6 to 28.6 on the Noometry Index.

Is DeepSeek V4 Flash or Llama 2-7B better for coding?

DeepSeek V4 Flash scores higher on coding benchmarks: 47.6 versus 28.7 in the Noometry coding category.

How many benchmarks do DeepSeek V4 Flash and Llama 2-7B share?

17 benchmarks have published results for both models. DeepSeek V4 Flash has 41 scored results on Noometry and Llama 2-7B has 29.

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