Noometry

Model comparison

DeepSeek V4.1 Flash vs Llama 2-7B

DeepSeek V4.1 Flash is the stronger model overall, scoring 52.8 to 29.1 on the Noometry Index.

Last verified . 16 shared benchmarks.

DeepSeek V4.1 Flash DeepSeek

52.8

Rank #38 Confirmed

Llama 2-7B Meta

29.1

Rank #317 Confirmed

Summary

  • They share 16 benchmarks with published results for both. DeepSeek V4.1 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 writing & preference, where DeepSeek V4.1 Flash leads 65.4 to 28.0.

Side by side

DeepSeek V4.1 FlashLlama 2-7B
ProviderDeepSeekMeta
Noometry Index52.829.1
Released2026-09-092023-07-18
WeightsOpenOpen
Context window1M—
Max output393K—
Input $ / M tokens$0.15—
Output $ / M tokens$0.60—
Results tracked3729

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Category by category

Coding DeepSeek V4.1 Flash leads

DeepSeek V4.1 Flash: 52.9 (#32), Llama 2-7B: 29.2 (#307)

BenchmarkDeepSeek V4.1 FlashLlama 2-7B
LMArena Coding15061002
LMArena WebDev1619—
SciCode51.9%—
ALE-Bench1,092—

Agentic & Tool Use Not comparable

DeepSeek V4.1 Flash: 31.2 (#69), Llama 2-7B: —

BenchmarkDeepSeek V4.1 FlashLlama 2-7B
APEX-Agents39.5%—
GDP.pdf19.8%—

Reasoning DeepSeek V4.1 Flash leads

DeepSeek V4.1 Flash: 50.2 (#36), Llama 2-7B: 15.7 (#312)

BenchmarkDeepSeek V4.1 FlashLlama 2-7B
LMArena Hard Prompts14831009
Epoch Capabilities Index154.999.06
NYT Connections (extended)89.6%—
CritPt14.3%—
Chess Puzzles—0%
Mystery Game Puzzles43%—
DTBench89.9%—
LMCA47%—
Surface Evolver Bench46.3%—
BIG-Bench Hard—39.2%
HellaSwag—77.2%
LAMBADA—73.3%
PIQA—78.8%
WinoGrande—69.2%

Math DeepSeek V4.1 Flash leads

DeepSeek V4.1 Flash: 66.7 (#25), Llama 2-7B: 30.7 (#233)

BenchmarkDeepSeek V4.1 FlashLlama 2-7B
LMArena Math14771042
FrontierMath (Tiers 1-3)67.4%—
FrontierMath Tier 426.8%—
OTIS Mock AIME 2024-202598.3%—
ProofBench54%—
GSM8K—16.7%

Knowledge DeepSeek V4.1 Flash leads

DeepSeek V4.1 Flash: 57.9 (#38), Llama 2-7B: 28.2 (#248)

BenchmarkDeepSeek V4.1 FlashLlama 2-7B
LMArena Expert15061036
GPQA Diamond89.8%—
ARC (AI2) Challenge—45.9%
BoolQ—77.9%
MMLU—45.8%
OpenBookQA—58.6%
TriviaQA—73.7%

Multimodal Not comparable

DeepSeek V4.1 Flash: 39.1 (#61), Llama 2-7B: —

BenchmarkDeepSeek V4.1 FlashLlama 2-7B
LMArena Vision1277—
Furniture Assembly34.2%—
ScienceQA—43.1%

Multilingual DeepSeek V4.1 Flash leads

DeepSeek V4.1 Flash: 55.0 (#35), Llama 2-7B: 23.8 (#293)

BenchmarkDeepSeek V4.1 FlashLlama 2-7B
LMArena Non-English1448973
LMArena Chinese1497973
LMArena French1452970
LMArena German1484978
LMArena Russian1471995
LMArena Spanish14591007
LMArena Japanese1412—
LMArena Korean1452—

Instruction Following DeepSeek V4.1 Flash leads

DeepSeek V4.1 Flash: 77.3 (#26), Llama 2-7B: 50.8 (#298)

BenchmarkDeepSeek V4.1 FlashLlama 2-7B
LMArena Instruction Following14741006

Long Context DeepSeek V4.1 Flash leads

DeepSeek V4.1 Flash: 45.2 (#47), Llama 2-7B: 30.4 (#287)

BenchmarkDeepSeek V4.1 FlashLlama 2-7B
LMArena Longer Query1475999

Writing & Preference DeepSeek V4.1 Flash leads

DeepSeek V4.1 Flash: 65.4 (#48), Llama 2-7B: 28.0 (#298)

BenchmarkDeepSeek V4.1 FlashLlama 2-7B
LMArena Text14621053
LMArena Creative Writing14351033
LMArena Multi-Turn14571029
EQ-Bench Creative Writing1540—

Frequently asked questions

Is DeepSeek V4.1 Flash better than Llama 2-7B?

DeepSeek V4.1 Flash is the stronger model overall, scoring 52.8 to 29.1 on the Noometry Index.

Is DeepSeek V4.1 Flash or Llama 2-7B better for coding?

DeepSeek V4.1 Flash scores higher on coding benchmarks: 52.9 versus 29.2 in the Noometry coding category.

How many benchmarks do DeepSeek V4.1 Flash and Llama 2-7B share?

16 benchmarks have published results for both models. DeepSeek V4.1 Flash has 37 scored results on Noometry and Llama 2-7B has 29.

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