Qwen (Alibaba) vs Nemotron (Nvidia)

Which one should you pick? Here's the full breakdown.

Our Pick

Qwen (Alibaba)

A
8.8/10

Alibaba's open-weights family -- Qwen3.5, Qwen3-Coder-Next, Qwen3-VL, Qwen3-Max. Apache 2.0 flagship sizes.

Nemotron (Nvidia)

B
7.8/10

Nvidia's open-weights family -- hybrid Mamba-Transformer MoE architecture, optimized for efficient reasoning on Nvidia hardware

CategoryQwen (Alibaba)Nemotron (Nvidia)
Ease of Use7.06.5
Output Quality9.08.0
Value10.08.0
Features9.08.5
Overall8.87.8

Pricing Comparison

FeatureQwen (Alibaba)Nemotron (Nvidia)
Free TierYesYes
Starting Price$0$0

Benchmark Head-to-Head

Qwen3.5-397B MoE vs Nemotron 3 Ultra (253B)

BenchmarkQwen (Alibaba)Nemotron (Nvidia)
MMLU-Pro83.5%79.8%
GPQA Diamond78.2%70.5%
AIME 202587%84.5%
HumanEval92.5%89.6%

Which Should You Pick?

Pick Qwen (Alibaba) if...

  • Higher output quality (9 vs 8)
  • Better value for money (10/10)
  • Stronger on graduate-level science questions (+7.7% on GPQA Diamond)

Developers who want frontier-tier open weights with Apache 2.0 licensing. Qwen3-Coder-Next is arguably the best local coding model; Qwen3.5-397B is a top-3 open generalist.

Visit Qwen (Alibaba)

Pick Nemotron (Nvidia) if...

Teams running on Nvidia hardware (TensorRT-LLM, NIM) who need efficient long-context reasoning. Nemotron 3 Super is a standout for its 8 GB VRAM footprint with strong reasoning.

Visit Nemotron (Nvidia)

Our Verdict

Qwen (Alibaba) is the clear winner here with 8.8/10 vs 7.8/10. Nemotron (Nvidia) isn't bad, but Qwen (Alibaba) outperforms it across the board. Pick Nemotron (Nvidia) only if teams running on nvidia hardware (tensorrt-llm, nim) who need efficient long-context reasoning.