Gemma 4 (Google) vs Nemotron (Nvidia)

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

Our Pick

Gemma 4 (Google)

A
8.3/10

Google DeepMind's open-weights model family -- multimodal, 256K context, runs on edge devices

Nemotron (Nvidia)

B
7.8/10

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

CategoryGemma 4 (Google)Nemotron (Nvidia)
Ease of Use7.06.5
Output Quality8.08.0
Value10.08.0
Features8.08.5
Overall8.37.8

Pricing Comparison

FeatureGemma 4 (Google)Nemotron (Nvidia)
Free TierYesYes
Starting Price$0$0

Benchmark Head-to-Head

Gemma 4 31B vs Nemotron 3 Ultra (253B)

BenchmarkGemma 4 (Google)Nemotron (Nvidia)
GPQA Diamond84.3%70.5%
HumanEval85%89.6%

Which Should You Pick?

Pick Gemma 4 (Google) if...

  • Better value for money (10/10)
  • Stronger on graduate-level science questions (+13.8% on GPQA Diamond)

Developers and businesses who need a permissively licensed multimodal LLM they can self-host or fine-tune. Especially good for multilingual use cases and on-device deployment.

Visit Gemma 4 (Google)

Pick Nemotron (Nvidia) if...

  • Stronger on python code generation (+4.6% on HumanEval)

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

Gemma 4 (Google) edges out Nemotron (Nvidia) with a 8.3 vs 7.8 overall score. Both are solid picks, but Gemma 4 (Google) has the advantage in value.