Phi-4 Mini 3.8B vs Gemma 3 4B
Side-by-side specs, benchmarks, and a verdict by use case.
Updated 2026-07-13
| Spec | Phi-4 Mini 3.8B | Gemma 3 4B |
|---|---|---|
| Parameters | 3.8B | 4B |
| Author | Microsoft | |
| License | MIT | Gemma |
| Context window | 0k | 0k |
| VRAM at Q4 | 10 GB | 10 GB |
| VRAM at Q5 | 12 GB | 12 GB |
| VRAM at Q8 | 18 GB | 18 GB |
| VRAM at FP16 | 33 GB | 33 GB |
| Use cases | chat, general, small | chat, general, vision, multilingual, small |
Verdict
Both models sit in a similar size class. The pick depends on tags, license, and benchmarks rather than raw parameter count.
For unambiguous commercial use, Phi-4 Mini 3.8B has the safer license (MIT) compared to Gemma.
The two models at a glance
About Phi-4 Mini 3.8B
Microsoft's 3.8B Phi-4 Mini under MIT with native function calling, 128k context via LongRoPE, and a 200k vocab. MMLU 67.3 and HumanEval 74.4. Strengths: Native function calling at 3.8B, 128k context via LongRoPE, MIT license, 200k vocabulary improves tokenization efficiency.
About Gemma 3 4B
Google's compact multimodal 4B with 128K context, vision input, and 140+ language coverage. The smallest Gemma 3 with the full feature set intact. Strengths: Multimodal in a 4B footprint, 140+ language coverage, 128K context, Sliding-window attention keeps memory in check.
How they compare
Phi-4 Mini 3.8B comes from Microsoft and Gemma 3 4B from Google, they belong to the Phi and Gemma families respectively. This comparison is built entirely from structured specs — parameter count, VRAM by quantization, context window, license, and published benchmark scores — so the verdict below reflects measurable differences rather than marketing claims.
At 3.8B vs 4B parameters, Gemma 3 4B is the larger of the two. Both need about 10 GB of VRAM at a Q4 quantization, so they fit the same GPU tier.
The two models target different sweet spots: Phi-4 Mini 3.8B is tuned for chat, general, small, while Gemma 3 4B leans toward chat, general, vision, multilingual, small. Match the model to your dominant workload rather than to raw size.
The smaller model, Phi-4 Mini 3.8B, will generally generate tokens faster on the same hardware.
Memory, quantization & throughput
Across quantization levels, Phi-4 Mini 3.8B requires Q4 ≈ 10 GB, Q5 ≈ 12 GB, Q8 ≈ 18 GB, FP16 ≈ 33 GB, while Gemma 3 4B requires Q4 ≈ 10 GB, Q5 ≈ 12 GB, Q8 ≈ 18 GB, FP16 ≈ 33 GB. In practice Phi-4 Mini 3.8B needs a 12 GB card at Q4, so plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity of Q8 or FP16.
Without a GPU, Phi-4 Mini 3.8B needs roughly 12 GB of system RAM to run on CPU and Gemma 3 4B about 12 GB — workable for offline use but far slower than GPU inference. On a mid-range GPU you can expect on the order of 40 tokens/sec from Phi-4 Mini 3.8B and 40 from Gemma 3 4B, scaling up to 100 and 100 tokens/sec on high-end hardware.
Which fits your GPU
Here is the highest-quality quantization of each model that fits common GPU memory budgets, so you can match Phi-4 Mini 3.8B or Gemma 3 4B to the card you actually own:
- On a 12 GB GPU: Phi-4 Mini 3.8B runs at Q5 (12 GB); Gemma 3 4B runs at Q5 (12 GB).
- On a 16 GB GPU: Phi-4 Mini 3.8B runs at Q5 (12 GB); Gemma 3 4B runs at Q5 (12 GB).
- On a 24 GB GPU: Phi-4 Mini 3.8B runs at Q8 (18 GB); Gemma 3 4B runs at Q8 (18 GB).
Benchmark scores
Reported benchmarks for Phi-4 Mini 3.8B: MMLU 67.3, HumanEval 74.4, MATH 71.5.
Bottom line: which should you pick?
- Pick Phi-4 Mini 3.8B if you need a permissive (MIT) license for commercial deployment.
- Pick Phi-4 Mini 3.8B for lower VRAM and faster inference; pick Gemma 3 4B for maximum headline quality.
- Pick Gemma 3 4B if your workload is multilingual, vision.
Which GPU should you buy to run Phi-4 Mini 3.8B?
To run Phi-4 Mini 3.8B locally at Q4, you need ~10 GB of VRAM. The best value for this is a RTX 5070 (12 GB VRAM).
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Frequently asked questions
What is the difference between Phi-4 Mini 3.8B and Gemma 3 4B?
The headline differences: Phi-4 Mini 3.8B is a 3.8B model and Gemma 3 4B is 4B; they ship under different licenses (MIT vs Gemma). Below we break down VRAM by quantization, benchmark scores, and a use-case verdict so you can pick the right one.
Can Phi-4 Mini 3.8B and Gemma 3 4B run on a 24 GB GPU?
At a Q4 quantization, Phi-4 Mini 3.8B needs about 10 GB of VRAM and fits comfortably on a 24 GB GPU; Gemma 3 4B needs about 10 GB and fits comfortably on a 24 GB GPU. Both have the same Q4 footprint.
Which is faster, Phi-4 Mini 3.8B or Gemma 3 4B?
Phi-4 Mini 3.8B is the smaller model (3.8B vs 4B), so on the same hardware it runs faster and uses less memory. The larger model trades speed for headline quality.
Which license is safer for commercial use, Phi-4 Mini 3.8B or Gemma 3 4B?
Phi-4 Mini 3.8B ships under MIT, a permissive license with no usage restrictions, whereas the other is under Gemma — check its terms before commercial deployment.