Noometry

Model comparison

Grok 4.20 Multi-Agent vs Llama 2-13B

Grok 4.20 Multi-Agent is the stronger model overall, scoring 46.2 to 29.6 on the Noometry Index.

Last verified . 17 shared benchmarks.

Grok 4.20 Multi-Agent xAI

46.2

Rank #65 Confirmed

Llama 2-13B Meta

29.6

Rank #309 Confirmed

Summary

  • They share 17 benchmarks with published results for both. Grok 4.20 Multi-Agent scores higher in 8 categories and Llama 2-13B in 0 categories; 8 gaps are clear of the uncertainty.
  • The widest gap is in writing & preference, where Grok 4.20 Multi-Agent leads 64.0 to 29.8.
  • Llama 2-13B has downloadable open weights; the other is API-only.

Side by side

Grok 4.20 Multi-AgentLlama 2-13B
ProviderxAIMeta
Noometry Index46.229.6
Released2026-03-092023-07-18
WeightsProprietaryOpen
Context window1M—
Max output30K—
Input $ / M tokens$1.25—
Output $ / M tokens$2.50—
Results tracked2032

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Category by category

Coding Grok 4.20 Multi-Agent leads

Grok 4.20 Multi-Agent: 43.0 (#92), Llama 2-13B: 30.9 (#291)

BenchmarkGrok 4.20 Multi-AgentLlama 2-13B
LMArena Coding14571062

Agentic & Tool Use Not comparable

Grok 4.20 Multi-Agent: —, Llama 2-13B: —

BenchmarkGrok 4.20 Multi-AgentLlama 2-13B
LMArena Search1204—

Reasoning Grok 4.20 Multi-Agent leads

Grok 4.20 Multi-Agent: 43.9 (#48), Llama 2-13B: 12.8 (#337)

BenchmarkGrok 4.20 Multi-AgentLlama 2-13B
LMArena Hard Prompts14481051
NYT Connections (extended)89.6%—
Chess Puzzles—0%
DTBench—42.2%
BIG-Bench Hard—58.2%
Epoch Capabilities Index—106.17
HellaSwag—80.7%
LAMBADA—76.5%
PIQA—80.8%
WinoGrande—72.8%

Math Grok 4.20 Multi-Agent leads

Grok 4.20 Multi-Agent: 39.4 (#104), Llama 2-13B: 31.1 (#229)

BenchmarkGrok 4.20 Multi-AgentLlama 2-13B
LMArena Math14421065
GSM8K—36.9%

Knowledge Grok 4.20 Multi-Agent leads

Grok 4.20 Multi-Agent: 40.4 (#119), Llama 2-13B: 28.1 (#249)

BenchmarkGrok 4.20 Multi-AgentLlama 2-13B
LMArena Expert14451030
ARC (AI2) Challenge—60.3%
BoolQ—82.4%
MMLU—55.6%
OpenBookQA—57%
TriviaQA—79.6%

Multimodal Not comparable

Grok 4.20 Multi-Agent: 40.5 (#48), Llama 2-13B: —

BenchmarkGrok 4.20 Multi-AgentLlama 2-13B
LMArena Vision1259—
ScienceQA—55.8%

Multilingual Grok 4.20 Multi-Agent leads

Grok 4.20 Multi-Agent: 54.4 (#43), Llama 2-13B: 26.5 (#279)

BenchmarkGrok 4.20 Multi-AgentLlama 2-13B
LMArena Non-English14401024
LMArena Chinese14751001
LMArena French14661044
LMArena German14561009
LMArena Japanese1405894
LMArena Korean1416953
LMArena Russian14571055
LMArena Spanish14471087

Instruction Following Grok 4.20 Multi-Agent leads

Grok 4.20 Multi-Agent: 74.8 (#84), Llama 2-13B: 53.3 (#287)

BenchmarkGrok 4.20 Multi-AgentLlama 2-13B
LMArena Instruction Following14201045

Long Context Grok 4.20 Multi-Agent leads

Grok 4.20 Multi-Agent: 43.7 (#88), Llama 2-13B: 32.3 (#269)

BenchmarkGrok 4.20 Multi-AgentLlama 2-13B
LMArena Longer Query14311064

Writing & Preference Grok 4.20 Multi-Agent leads

Grok 4.20 Multi-Agent: 64.0 (#59), Llama 2-13B: 29.8 (#289)

BenchmarkGrok 4.20 Multi-AgentLlama 2-13B
LMArena Text14501084
LMArena Creative Writing14361047
LMArena Multi-Turn14521050

Frequently asked questions

Is Grok 4.20 Multi-Agent better than Llama 2-13B?

Grok 4.20 Multi-Agent is the stronger model overall, scoring 46.2 to 29.6 on the Noometry Index.

Is Grok 4.20 Multi-Agent or Llama 2-13B better for coding?

Grok 4.20 Multi-Agent scores higher on coding benchmarks: 43.0 versus 30.9 in the Noometry coding category.

How many benchmarks do Grok 4.20 Multi-Agent and Llama 2-13B share?

17 benchmarks have published results for both models. Grok 4.20 Multi-Agent has 20 scored results on Noometry and Llama 2-13B has 32.

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