Noometry

Model comparison

Deepseek Coder v2 vs Llama 4 Scout

Deepseek Coder v2 is the stronger model overall, scoring 35.9 to 27.7 on the Noometry Index.

Last verified . 18 shared benchmarks.

Deepseek Coder v2 DeepSeek

35.9

Rank #220 Confirmed

Llama 4 Scout Meta

27.7

Rank #330 Confirmed

Summary

  • They share 18 benchmarks with published results for both. Deepseek Coder v2 scores higher in 6 categories and Llama 4 Scout in 2 categories; 7 gaps are clear of the uncertainty.
  • The widest gap is in coding, where Deepseek Coder v2 leads 38.1 to 20.2.
  • The biggest single-benchmark swing is BigCodeBench Complete: 59.7% for Deepseek Coder v2 and 43.1% for Llama 4 Scout.

Side by side

Deepseek Coder v2Llama 4 Scout
ProviderDeepSeekMeta
Noometry Index35.927.7
Released2024-06-172025-04-05
WeightsOpenOpen
Context window—128K
Max output—4K
Input $ / M tokens—$0.10
Output $ / M tokens—$0.30
Results tracked2443

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Category by category

Coding Deepseek Coder v2 leads

Deepseek Coder v2: 38.1 (#183), Llama 4 Scout: 20.2 (#339)

BenchmarkDeepseek Coder v2Llama 4 Scout
LMArena Coding12511286
BigCodeBench Complete59.7%43.1%
SWE-bench Verified (bash only)—9.1%
SciCode—17%
BigCodeBench Instruct48.2%—
HumanEval+82.3%—
MBPP+75.1%—

Agentic & Tool Use Not comparable

Deepseek Coder v2: —, Llama 4 Scout: 24.6 (#119)

BenchmarkDeepseek Coder v2Llama 4 Scout
Berkeley Function Calling Leaderboard—28.1%

Reasoning Deepseek Coder v2 leads

Deepseek Coder v2: 23.6 (#176), Llama 4 Scout: 9.1 (#345)

BenchmarkDeepseek Coder v2Llama 4 Scout
LMArena Hard Prompts12071266
ARC-AGI-2—0%
Kagi LLM Benchmark—36.9%
ARC-AGI-1—0.5%
CritPt—0%
DTBench—57.9%
LMCA—12%
Epoch Capabilities Index—129.64
ForecastBench—57.5
WinoGrande83.7%—

Math Deepseek Coder v2 leads

Deepseek Coder v2: 34.9 (#190), Llama 4 Scout: 19.6 (#286)

BenchmarkDeepseek Coder v2Llama 4 Scout
LMArena Math12411287
OTIS Mock AIME 2024-2025—7.8%
Omni-MATH—37.3%
MATH Level 5—62.3%
FrontierMath (Feb 2025 set)—0%
GSM8K94.5%—

Knowledge Too close to call

Deepseek Coder v2: 32.3 (#212), Llama 4 Scout: 31.9 (#217)

BenchmarkDeepseek Coder v2Llama 4 Scout
LMArena Expert11811235
GPQA Diamond—51.8%
MMLU-Pro—74.2%
Vectara Hallucination Rate—7.7%
GPQA (HELM)—50.7%
ARC (AI2) Challenge64.3%—

Multimodal Not comparable

Deepseek Coder v2: —, Llama 4 Scout: 32.2 (#102)

BenchmarkDeepseek Coder v2Llama 4 Scout
LMArena Vision—1118
SpatialViz-Bench—34.2%

Multilingual Llama 4 Scout leads

Deepseek Coder v2: 36.3 (#240), Llama 4 Scout: 41.0 (#212)

BenchmarkDeepseek Coder v2Llama 4 Scout
LMArena Non-English11821252
LMArena Chinese12011255
LMArena French11851282
LMArena German11641272
LMArena Japanese11261206
LMArena Korean11041207
LMArena Russian11881263
LMArena Spanish11531278

Instruction Following Llama 4 Scout leads

Deepseek Coder v2: 61.7 (#242), Llama 4 Scout: 65.8 (#217)

BenchmarkDeepseek Coder v2Llama 4 Scout
LMArena Instruction Following11801248
IFEval—81.8%

Long Context Deepseek Coder v2 leads

Deepseek Coder v2: 37.0 (#224), Llama 4 Scout: 27.5 (#294)

BenchmarkDeepseek Coder v2Llama 4 Scout
LMArena Longer Query12191265
Fiction.LiveBench—36%

Writing & Preference Deepseek Coder v2 leads

Deepseek Coder v2: 38.2 (#253), Llama 4 Scout: 37.0 (#261)

BenchmarkDeepseek Coder v2Llama 4 Scout
LMArena Text11911279
LMArena Creative Writing11201249
LMArena Multi-Turn11771280
EQ-Bench Creative Writing—783
WildBench—78%

Frequently asked questions

Is Deepseek Coder v2 better than Llama 4 Scout?

Deepseek Coder v2 is the stronger model overall, scoring 35.9 to 27.7 on the Noometry Index.

Is Deepseek Coder v2 or Llama 4 Scout better for coding?

Deepseek Coder v2 scores higher on coding benchmarks: 38.1 versus 20.2 in the Noometry coding category.

How many benchmarks do Deepseek Coder v2 and Llama 4 Scout share?

18 benchmarks have published results for both models. Deepseek Coder v2 has 24 scored results on Noometry and Llama 4 Scout has 43.

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