AI Tool Comparison
Gemma 3 vs Phi-4
A side-by-side breakdown to help you pick the right tool for your workflow.
Gemma 3
Superseded by Gemma 4 (April 2026), Gemini-3-derived reasoning and agentic capability in five open sizes from 2B to 31B, running on phones, laptops, or servers with a 256K context window.
Phi-4
Run vision and multi-step reasoning in a compact 15B model: read documents, ground UI elements, and solve math and science problems on modest hardware. Now Phi-4-reasoning-vision.
Bottom Line
Last reviewed: August 2026
Gemma 3 and Phi-4 both compete in Models, overlapping most directly on models. Gemma 3 carries the higher rating (4.5 vs 4.4), but a gap that size rarely overrides a real workflow fit on its own.
Choose Gemma 3 if…
Best for developers who need to self-host or fine-tune a capable open-weight model without cloud API dependency, and its edge is sizes from 1B to 27B parameters covering everything from on-device inference to serious workloads. A strong, well-documented open-weight option, expect real self-hosting expertise required and a gap versus frontier closed models. Lean toward Phi-4 instead if excellent quality-per-parameter that makes capable inference practical at 3.8B to 14B parameters matters more for your use case.
Choose Phi-4 if…
Best for developers who need strong reasoning on consumer hardware or edge devices, not a data center GPU, and its edge is excellent quality-per-parameter that makes capable inference practical at 3.8B to 14B parameters. The right choice for cost-constrained deployment, expect a real ML setup to actually deploy it. Lean toward Gemma 3 instead if sizes from 1B to 27B parameters covering everything from on-device inference to serious workloads matters more for your use case.
| Attribute | Gemma 3 | Phi-4 |
|---|---|---|
| Category | Models | Models |
| Pricing | free | free |
| Pricing Detail | Free and open-weight (Apache 2.0), now on Gemma 4 | Free and open-weight via Microsoft Foundry, Hugging Face |
| Rating |
Key Features
Gemma 3
- Open weights
- Multilingual and multimodal
- Multiple sizes
- Runs on single GPU
Phi-4
- Strong reasoning at small size
- Open weights
- Efficient inference
- Good for local/edge use
Pros
Gemma 3
- •High quality and open
- •Good documentation
- •Flexible sizes
Phi-4
- •Excellent quality-per-parameter
- •Free and open
- •Runs on modest hardware
Cons
Gemma 3
- Self-hosting expertise needed
- Behind frontier closed models
Phi-4
- Smaller knowledge breadth
- Needs ML setup to deploy