Model comparison

Llama 3-70B vs Qwen3-Next 80B-A3B Instruct

Qwen3-Next 80B-A3B Instruct is the stronger model overall, scoring 43.0 to 28.8 on the Noometry Index.

Last verified . 18 shared benchmarks.

Llama 3-70B Meta

28.8

Rank #323 Confirmed

Summary

  • They share 18 benchmarks with published results for both. Llama 3-70B scores higher in 0 categories and Qwen3-Next 80B-A3B Instruct in 8 categories; 8 gaps are clear of the uncertainty.
  • The widest gap is in math, where Qwen3-Next 80B-A3B Instruct leads 38.8 to 12.8.
  • The biggest single-benchmark swing is Kagi LLM Benchmark: 35.1% for Llama 3-70B and 66.7% for Qwen3-Next 80B-A3B Instruct.

Side by side

Llama 3-70B and Qwen3-Next 80B-A3B Instruct specifications
Llama 3-70BQwen3-Next 80B-A3B Instruct
ProviderMetaAlibaba (Qwen)
Noometry Index28.843.0
Released2024-04-182025-09
WeightsOpenOpen
Context window—131K
Max output—33K
Input $ / M tokens—$0.50
Output $ / M tokens—$2
Results tracked3125

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Category by category

Coding Qwen3-Next 80B-A3B Instruct leads

Llama 3-70B: 35.8 (#218), Qwen3-Next 80B-A3B Instruct: 42.5 (#98)

Coding benchmarks
BenchmarkLlama 3-70BQwen3-Next 80B-A3B Instruct
LMArena Coding12061440
BigCodeBench Instruct43.6%—
BigCodeBench Complete54.5%—
HumanEval+72%—
MBPP+69%—

Agentic & Tool Use Not comparable

Llama 3-70B: 21.1 (#139), Qwen3-Next 80B-A3B Instruct: —

Agentic & Tool Use benchmarks
BenchmarkLlama 3-70BQwen3-Next 80B-A3B Instruct
Cybench5%—

Reasoning Qwen3-Next 80B-A3B Instruct leads

Llama 3-70B: 18.0 (#288), Qwen3-Next 80B-A3B Instruct: 31.1 (#81)

Reasoning benchmarks
BenchmarkLlama 3-70BQwen3-Next 80B-A3B Instruct
Kagi LLM Benchmark35.1%66.7%
LMArena Hard Prompts11951428
DTBench54.2%—
Epoch Capabilities Index122.93—
ForecastBench57.1—
WinoGrande83.5%—

Math Qwen3-Next 80B-A3B Instruct leads

Llama 3-70B: 12.8 (#305), Qwen3-Next 80B-A3B Instruct: 38.8 (#126)

Math benchmarks
BenchmarkLlama 3-70BQwen3-Next 80B-A3B Instruct
LMArena Math12181440
OTIS Mock AIME 2024-20254.3%—
Omni-MATH—46.7%
MATH Level 522.6%—

Knowledge Qwen3-Next 80B-A3B Instruct leads

Llama 3-70B: 20.8 (#277), Qwen3-Next 80B-A3B Instruct: 41.8 (#106)

Knowledge benchmarks
BenchmarkLlama 3-70BQwen3-Next 80B-A3B Instruct
LMArena Expert11491417
GPQA Diamond40.6%—
MMLU-Pro—78.6%
Vectara Hallucination Rate—9.3%
GPQA (HELM)—63%
MMLU79.3%—

Multilingual Qwen3-Next 80B-A3B Instruct leads

Llama 3-70B: 33.6 (#251), Qwen3-Next 80B-A3B Instruct: 52.1 (#93)

Multilingual benchmarks
BenchmarkLlama 3-70BQwen3-Next 80B-A3B Instruct
LMArena Non-English11421407
LMArena Chinese11141460
LMArena French12321413
LMArena German11691417
LMArena Japanese10171395
LMArena Korean10171364
LMArena Russian11591404
LMArena Spanish12411435

Instruction Following Qwen3-Next 80B-A3B Instruct leads

Llama 3-70B: 62.5 (#238), Qwen3-Next 80B-A3B Instruct: 70.8 (#159)

Instruction Following benchmarks
BenchmarkLlama 3-70BQwen3-Next 80B-A3B Instruct
LMArena Instruction Following11941389
IFEval—81%

Long Context Qwen3-Next 80B-A3B Instruct leads

Llama 3-70B: 35.6 (#240), Qwen3-Next 80B-A3B Instruct: 37.0 (#223)

Long Context benchmarks
BenchmarkLlama 3-70BQwen3-Next 80B-A3B Instruct
LMArena Longer Query11741403
Fiction.LiveBench—55.6%

Writing & Preference Qwen3-Next 80B-A3B Instruct leads

Llama 3-70B: 42.8 (#231), Qwen3-Next 80B-A3B Instruct: 58.0 (#121)

Writing & Preference benchmarks
BenchmarkLlama 3-70BQwen3-Next 80B-A3B Instruct
LMArena Text12211417
LMArena Creative Writing12101334
LMArena Multi-Turn12231416
WildBench—80.7%

Frequently asked questions

Is Llama 3-70B better than Qwen3-Next 80B-A3B Instruct?

Qwen3-Next 80B-A3B Instruct is the stronger model overall, scoring 43.0 to 28.8 on the Noometry Index.

Is Llama 3-70B or Qwen3-Next 80B-A3B Instruct better for coding?

Qwen3-Next 80B-A3B Instruct scores higher on coding benchmarks: 42.5 versus 35.8 in the Noometry coding category.

How many benchmarks do Llama 3-70B and Qwen3-Next 80B-A3B Instruct share?

18 benchmarks have published results for both models. Llama 3-70B has 31 scored results on Noometry and Qwen3-Next 80B-A3B Instruct has 25.

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