Gemini vs Gemma
Head-to-Head Performance Audit
Gemini
Google DeepMindGoogle's multimodal AI leading on reasoning and ARC-AGI-2 benchmarks
Full Audit →
Intelligence Fingerprint
Gemini 3.1 Pro Preview
Gemini 3.1 Pro Preview by Google. Optimized for high intelligence.
Gemma 4 31B (Reasoning)
Gemma 4 31B (Reasoning) by Google. Optimized for efficiency.
Competitive Edge
Gemini Verdict
Key Strengths
- #1 on ARC-AGI-2 (77.1%)
- Best GPQA Diamond score (94.3%)
- Native multimodal from ground up
- Real-time Google Search integration
Limitations
- Workspace integration required for full features
- Some features US-only
- Less coding focus than Claude
Gemma Verdict
Key Strengths
- Apache 2.0 license (commercial use)
- #3 Open Model on Arena AI
- Phone-to-Workstation scalability
- Native Gemini 3 research inside
Limitations
- Smaller context than proprietary Gemini
- Resource heavy for 31B on mobile
Where to Choose Which?
Select Gemini for:
- Research tasks
- Multimodal workflows
- Google Workspace users
- Benchmark-critical applications
Select Gemma for:
- Open-source developers
- Local RAG implementations
- Edge device AI
- Academic research
Frequently Asked Questions
Is Gemini better than Gemma?
Based on our benchmark analysis, Gemini scores higher on average across key metrics (SWE-Bench, GPQA Diamond, ARC-AGI-2) with a composite average of 84.0% vs 65.3%. However, Gemma may still be the better choice depending on your specific use case and budget.
Which is better for coding, Gemini or Gemma?
Gemini scores 80.6% on SWE-Bench Verified compared to Gemma's 72.1%. SWE-Bench measures real-world GitHub issue resolution, making it the most reliable coding benchmark. Gemini is the stronger choice for developers.
How does Gemini pricing compare to Gemma?
Gemini starts at Free (freemium) while Gemma starts at Free (open-source). Gemma offers a completely free tier.
When should I choose Gemini over Gemma?
Choose Gemini when you need Research tasks or Multimodal workflows. Choose Gemma when your priority is Open-source developers or Local RAG implementations. Both tools serve different strengths depending on your workflow.