Gemma 3 4B vs Phi-4 Mini 3.8B
Side-by-side comparison of hardware requirements, quantization options, and specifications to help you choose the right model for your device.
Specifications Comparison
| Spec | Gemma 3 4B | Phi-4 Mini 3.8B |
|---|---|---|
| Parameters | 4B | 3.8B |
| Architecture | gemma3 | phi4 |
| License | Gemma | MIT |
| Context Length | 32K tokens | 128K tokens |
| Category | Language Model | Language Model |
| Author | Microsoft | |
| HF Downloads | 1.9M | 1.6M |
| VRAM Range | 2.82 - 4.35 GB | 2.82 - 4.3 GB |
| Quantizations | 2 options | 2 options |
| Best Quality Score | 98% | 98% |
Quantization Options
Gemma 3 4B
Phi-4 Mini 3.8B
In-depth comparison
Phi-4 Mini 3.8B is the better choice for most users due to its larger context window, which is crucial for handling longer texts and maintaining coherence. However, Gemma 3 4B is more suitable for users with limited VRAM or those prioritizing strong reasoning capabilities on mobile devices.
When to choose Gemma 3 4B
Gemma 3 4B is the better pick for users who need a model that performs well on mobile devices like iPhones, thanks to its balanced design and strong reasoning capabilities. It is also ideal for applications where the context length of 32,768 tokens is sufficient, and you want a model that has been widely tested and trusted, as evidenced by its high number of downloads and likes.
When to choose Phi-4 Mini 3.8B
Phi-4 Mini 3.8B is the better choice for users who require a larger context window of 131,072 tokens, making it ideal for tasks that involve long documents or maintaining coherence over extended conversations. Its compact size and efficient architecture make it a drop-in upgrade from previous versions, and it is particularly useful for applications requiring extensive context, such as legal or technical document analysis.
Quality
Both models have a best quality score of 98%, indicating comparable output quality. However, Phi-4 Mini 3.8B's larger context window gives it an edge in maintaining coherence over longer texts, while Gemma 3 4B's strong reasoning capabilities make it slightly better for complex reasoning tasks within its context limit.
Performance & hardware fit
Both models require a minimum of 2.8GB VRAM, making them suitable for a wide range of hardware. However, Phi-4 Mini 3.8B's larger context window of 131,072 tokens may lead to slower processing times compared to Gemma 3 4B's 32,768 tokens, especially on lower-end GPUs.
Use-case fit
| coding | Phi-4 Mini 3.8B | Phi-4 Mini 3.8B's larger context window is beneficial for handling long code snippets and maintaining context in coding-related tasks. |
| creative writing | Phi-4 Mini 3.8B | The larger context window of Phi-4 Mini 3.8B helps maintain narrative coherence over longer pieces of creative writing. |
| RAG / retrieval | Phi-4 Mini 3.8B | Phi-4 Mini 3.8B's ability to handle longer contexts makes it more suitable for retrieval-augmented generation tasks involving extensive information. |
| agent / tool use | Gemma 3 4B | Gemma 3 4B's strong reasoning capabilities and efficiency make it better suited for agent and tool use, especially on mobile devices. |
| running on consumer GPU (8-12GB) | Tie | Both models fit well within the VRAM limits of consumer GPUs, making them equally viable options. |
| long context (16K+) | Phi-4 Mini 3.8B | Phi-4 Mini 3.8B's context window of 131,072 tokens far exceeds 16K, making it the clear winner for long-context tasks. |
Phi-4 Mini 3.8B wins for most users due to its larger context window and efficient handling of long texts. However, Gemma 3 4B is the better choice for mobile device use and tasks requiring strong reasoning within a smaller context window.