Phi-4 Mini 3.8BvsGemma 3 4B
Complete comparison between Phi-4 Mini 3.8B (3.8B parameters, Microsoft) and Gemma 3 4B (4B, Google). VRAM required by quantization, tokens/second measured on 4 reference GPUs, verdict by use case, license, installation commands. All figures are calculated from the catalog data—no copy-pasting between pages.
In brief
| Feature | Phi-4 Mini 3.8B | Gemma 3 4B |
|---|---|---|
| Settings | 3.8B | 4B |
| Family | Phi | Gemma |
| Author | Microsoft | |
| Origin | US | US |
| License | MIT | Gemma |
| Context | 128,000 tokens | 128,000 tokens |
| Output | February 2025 | March 2025 |
Memory footprint
Approximate VRAM required for inference with an average context window. The winner (in green) is the model that uses less — smaller models have the edge.
| Quantization | Phi-4 Mini 3.8B | Gemma 3 4B |
|---|---|---|
| Q4_K_M (lightweight) | 3 GB | 3.5 GB |
| Q5_K_M (balanced) | 3.5 GB | 4 GB |
| Q8 (near-lossless) | 5 GB | 5 GB |
| FP16 (max quality) | 8 GB | 9 GB |
| CPU-only RAM | 6 GB | 6 GB |
Estimated speed (tokens/second)
Estimates based on the best quantization that can fit on each GPU. Actual figures depend on the prompt, context, and engine (llama.cpp, vLLM, MLX). Methodology.
| Reference GPU | Phi-4 Mini 3.8B | Gemma 3 4B |
|---|---|---|
| RTX 4090 (24 GB) | 150 tok/s · FP16 | 150 tok/s · FP16 |
| RTX 4080 (16 GB) | 65 tok/s · FP16 | 65 tok/s · FP16 |
| RTX 3060 12GB (12 GB) | 22 tok/s · FP16 | 22 tok/s · FP16 |
| Apple M4 Pro (48 GB) (36 GB) | 65 tok/s · FP16 | 65 tok/s · FP16 |
Verdict by use case
For each common use case, we indicate which of the two is better suited based on its tags, size, and specialization.
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Strengths and weaknesses
Phi-4 Mini 3.8B
3.8B MIT with function calling. MMLU 67.3, HumanEval 74.4. 200k vocabulary, LongRoPE.
- Native function calling
- 128k ctx
- MIT License
- Anglais-first
Installation
Gemma 3 4B
Compact multimodal model. 140+ languages, 128k ctx, vision, sliding-window attention.
- Compact multimodal
- 140 languages
- 128k ctx
- Gemma license (not Apache)
Installation
Frequently asked questions
Which model runs best on RTX 4090 (24 GB): Phi-4 Mini 3.8B or Gemma 3 4B?
On an RTX 4090, Phi-4 Mini 3.8B runs at FP16 (~150 tok/s), Gemma 3 4B in FP16 (~150 tok/s). In pure throughput, Gemma 3 4B wins. See the configurator to test your exact GPU.
Which uses the least VRAM?
In Q4_K_M, Phi-4 Mini 3.8B fits in 3 GB versus 3.5 GB for its rival—a 0.5 GB gap, significant if you're targeting an RTX 3060 or an 8 GB 4060 Ti.
Can these models be used in commercial production?
Phi-4 Mini 3.8B is under the MIT license — free for commercial use. Gemma 3 4B is under Gemma — also check the terms. For SaaS, prefer Apache 2.0 or MIT.
Which one should you choose in 2026?
Depends on your primary constraint. Smaller / faster: Phi-4 Mini 3.8B (3.8B). More capable: Gemma 3 4B (4B). If you're unsure, run the configurator with your GPU and use case — it will decide based on both.
Alternatives to consider
If neither works for you, here are some neighboring models you could explore.