Model comparison

DeepSeek-V3.1-Terminus vs Llama 2-13B

DeepSeek-V3.1-Terminus is the stronger model overall, scoring 43.1 to 29.6 on the Noometry Index.

Last verified . 11 shared benchmarks.

DeepSeek-V3.1-Terminus DeepSeek

43.1

Rank #97 Confirmed

Llama 2-13B Meta

29.6

Rank #309 Confirmed

Summary

  • They share 11 benchmarks with published results for both. DeepSeek-V3.1-Terminus scores higher in 7 categories and Llama 2-13B in 0 categories; 7 gaps are clear of the uncertainty.
  • The widest gap is in writing & preference, where DeepSeek-V3.1-Terminus leads 61.0 to 29.8.
  • The biggest single-benchmark swing is DTBench: 81.3% for DeepSeek-V3.1-Terminus and 42.2% for Llama 2-13B.

Side by side

DeepSeek-V3.1-Terminus and Llama 2-13B specifications
DeepSeek-V3.1-TerminusLlama 2-13B
ProviderDeepSeekMeta
Noometry Index43.129.6
Released2025-09-222023-07-18
WeightsOpenOpen
Context window164K—
Max output147K—
Input $ / M tokens$0.27—
Output $ / M tokens$1—
Results tracked1632

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Category by category

Coding DeepSeek-V3.1-Terminus leads

DeepSeek-V3.1-Terminus: 42.0 (#113), Llama 2-13B: 30.9 (#291)

Coding benchmarks
BenchmarkDeepSeek-V3.1-TerminusLlama 2-13B
LMArena Coding14261062
SciCode40.6%—
ALE-Bench745.17—

Reasoning DeepSeek-V3.1-Terminus leads

DeepSeek-V3.1-Terminus: 26.4 (#133), Llama 2-13B: 12.8 (#337)

Reasoning benchmarks
BenchmarkDeepSeek-V3.1-TerminusLlama 2-13B
LMArena Hard Prompts14261051
DTBench81.3%42.2%
Kagi LLM Benchmark57.4%—
CritPt1.7%—
Chess Puzzles—0%
LMCA28.6%—
BIG-Bench Hard—58.2%
Epoch Capabilities Index—106.17
HellaSwag—80.7%
LAMBADA—76.5%
PIQA—80.8%
WinoGrande—72.8%

Math DeepSeek-V3.1-Terminus leads

DeepSeek-V3.1-Terminus: 38.5 (#137), Llama 2-13B: 31.1 (#229)

Math benchmarks
BenchmarkDeepSeek-V3.1-TerminusLlama 2-13B
LMArena Math14021065
GSM8K—36.9%

Knowledge Not comparable

DeepSeek-V3.1-Terminus: —, Llama 2-13B: 28.1 (#249)

Knowledge benchmarks
BenchmarkDeepSeek-V3.1-TerminusLlama 2-13B
LMArena Expert—1030
ARC (AI2) Challenge—60.3%
BoolQ—82.4%
MMLU—55.6%
OpenBookQA—57%
TriviaQA—79.6%

Multimodal Not comparable

DeepSeek-V3.1-Terminus: —, Llama 2-13B: —

Multimodal benchmarks
BenchmarkDeepSeek-V3.1-TerminusLlama 2-13B
ScienceQA—55.8%

Multilingual DeepSeek-V3.1-Terminus leads

DeepSeek-V3.1-Terminus: 52.1 (#92), Llama 2-13B: 26.5 (#279)

Multilingual benchmarks
BenchmarkDeepSeek-V3.1-TerminusLlama 2-13B
LMArena Non-English14071024
LMArena Russian14361055
LMArena Chinese—1001
LMArena French—1044
LMArena German—1009
LMArena Japanese—894
LMArena Korean—953
LMArena Spanish—1087

Instruction Following DeepSeek-V3.1-Terminus leads

DeepSeek-V3.1-Terminus: 74.0 (#106), Llama 2-13B: 53.3 (#287)

Instruction Following benchmarks
BenchmarkDeepSeek-V3.1-TerminusLlama 2-13B
LMArena Instruction Following14041045

Long Context DeepSeek-V3.1-Terminus leads

DeepSeek-V3.1-Terminus: 43.4 (#97), Llama 2-13B: 32.3 (#269)

Long Context benchmarks
BenchmarkDeepSeek-V3.1-TerminusLlama 2-13B
LMArena Longer Query14211064

Writing & Preference DeepSeek-V3.1-Terminus leads

DeepSeek-V3.1-Terminus: 61.0 (#92), Llama 2-13B: 29.8 (#289)

Writing & Preference benchmarks
BenchmarkDeepSeek-V3.1-TerminusLlama 2-13B
LMArena Text14191084
LMArena Creative Writing14031047
LMArena Multi-Turn14111050

Frequently asked questions

Is DeepSeek-V3.1-Terminus better than Llama 2-13B?

DeepSeek-V3.1-Terminus is the stronger model overall, scoring 43.1 to 29.6 on the Noometry Index.

Is DeepSeek-V3.1-Terminus or Llama 2-13B better for coding?

DeepSeek-V3.1-Terminus scores higher on coding benchmarks: 42.0 versus 30.9 in the Noometry coding category.

How many benchmarks do DeepSeek-V3.1-Terminus and Llama 2-13B share?

11 benchmarks have published results for both models. DeepSeek-V3.1-Terminus has 16 scored results on Noometry and Llama 2-13B has 32.

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