Noometry

Model comparison

Grok 4.20 Multi-Agent vs Llama 3.1-405B

Grok 4.20 Multi-Agent is the stronger model overall, scoring 45.5 to 30.4 on the Noometry Index.

Last verified . 17 shared benchmarks.

Grok 4.20 Multi-Agent xAI

45.5

Rank #62 Confirmed

Llama 3.1-405B Meta

30.4

Rank #279 Confirmed

Summary

  • They share 17 benchmarks with published results for both. Grok 4.20 Multi-Agent scores higher in 8 categories and Llama 3.1-405B in 0 categories; 8 gaps are clear of the uncertainty.
  • The widest gap is in reasoning, where Grok 4.20 Multi-Agent leads 42.4 to 16.4.
  • Llama 3.1-405B has downloadable open weights; the other is API-only.

Side by side

Grok 4.20 Multi-AgentLlama 3.1-405B
ProviderxAIMeta
Noometry Index45.530.4
Released2026-03-092024-07-23
WeightsProprietaryOpen
Context window1M—
Max output30K—
Input $ / M tokens$1.25—
Output $ / M tokens$2.50—
Results tracked2042

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Category by category

Coding Grok 4.20 Multi-Agent leads

Grok 4.20 Multi-Agent: 42.4 (#91), Llama 3.1-405B: 32.7 (#257)

BenchmarkGrok 4.20 Multi-AgentLlama 3.1-405B
LMArena Coding14581291
WeirdML—21.4%

Agentic & Tool Use Not comparable

Grok 4.20 Multi-Agent: —, Llama 3.1-405B: 20.9 (#131)

BenchmarkGrok 4.20 Multi-AgentLlama 3.1-405B
TheAgentCompany—7.4%
Cybench—7.5%
LMArena Search1204—

Reasoning Grok 4.20 Multi-Agent leads

Grok 4.20 Multi-Agent: 42.4 (#44), Llama 3.1-405B: 16.4 (#293)

BenchmarkGrok 4.20 Multi-AgentLlama 3.1-405B
LMArena Hard Prompts14481270
SimpleBench—23%
Kagi LLM Benchmark—45%
NYT Connections (extended)89.6%—
DTBench—61.4%
BIG-Bench Hard—82.9%
Epoch Capabilities Index—128.76
ForecastBench—59.9
HellaSwag—89.2%
PIQA—85.9%
WinoGrande—89.2%

Math Grok 4.20 Multi-Agent leads

Grok 4.20 Multi-Agent: 36.6 (#106), Llama 3.1-405B: 17.3 (#283)

BenchmarkGrok 4.20 Multi-AgentLlama 3.1-405B
LMArena Math14411281
OTIS Mock AIME 2024-2025—9.7%
Omni-MATH—24.9%
MATH Level 5—49.8%

Knowledge Grok 4.20 Multi-Agent leads

Grok 4.20 Multi-Agent: 39.7 (#116), Llama 3.1-405B: 30.4 (#221)

BenchmarkGrok 4.20 Multi-AgentLlama 3.1-405B
LMArena Expert14451243
GPQA Diamond—50.9%
MMLU-Pro—72.3%
Confabulations—17.6%
GPQA (HELM)—52.2%
ARC (AI2) Challenge—95.3%
MMLU—84.5%
TriviaQA—82.7%

Multimodal Not comparable

Grok 4.20 Multi-Agent: 39.8 (#44), Llama 3.1-405B: —

BenchmarkGrok 4.20 Multi-AgentLlama 3.1-405B
LMArena Vision1259—

Multilingual Grok 4.20 Multi-Agent leads

Grok 4.20 Multi-Agent: 54.6 (#40), Llama 3.1-405B: 40.9 (#209)

BenchmarkGrok 4.20 Multi-AgentLlama 3.1-405B
LMArena Non-English14401248
LMArena Chinese14751242
LMArena French14671278
LMArena German14561252
LMArena Japanese14051208
LMArena Korean14161184
LMArena Russian14571265
LMArena Spanish14471259

Instruction Following Grok 4.20 Multi-Agent leads

Grok 4.20 Multi-Agent: 75.0 (#80), Llama 3.1-405B: 66.1 (#209)

BenchmarkGrok 4.20 Multi-AgentLlama 3.1-405B
LMArena Instruction Following14201259
IFEval—81.1%

Long Context Grok 4.20 Multi-Agent leads

Grok 4.20 Multi-Agent: 43.7 (#84), Llama 3.1-405B: 38.4 (#192)

BenchmarkGrok 4.20 Multi-AgentLlama 3.1-405B
LMArena Longer Query14301266

Writing & Preference Grok 4.20 Multi-Agent leads

Grok 4.20 Multi-Agent: 64.1 (#57), Llama 3.1-405B: 39.1 (#246)

BenchmarkGrok 4.20 Multi-AgentLlama 3.1-405B
LMArena Text14501284
LMArena Creative Writing14351262
LMArena Multi-Turn14511297
EQ-Bench Creative Writing—870
WildBench—78.3%

Frequently asked questions

Is Grok 4.20 Multi-Agent better than Llama 3.1-405B?

Grok 4.20 Multi-Agent is the stronger model overall, scoring 45.5 to 30.4 on the Noometry Index.

Is Grok 4.20 Multi-Agent or Llama 3.1-405B better for coding?

Grok 4.20 Multi-Agent scores higher on coding benchmarks: 42.4 versus 32.7 in the Noometry coding category.

How many benchmarks do Grok 4.20 Multi-Agent and Llama 3.1-405B share?

17 benchmarks have published results for both models. Grok 4.20 Multi-Agent has 20 scored results on Noometry and Llama 3.1-405B has 42.

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