Phi-3 Mini vs Gemma 2 9B: Which Small LLM Is Better?
Microsoft's efficient Phi-3 Mini punches above its weight against Google's Gemma 2 9B. We compare benchmarks, speed, on-device performance, and real-world use cases for the best small language models.
Phi-3 Mini (3.8B) wins on efficiency, speed, coding, and math reasoning. Runs on laptops and phones — 2x faster with better code generation despite fewer parameters.
Gemma 2 9B wins on general knowledge, output quality, and coherence. Better for open-ended generation and tasks requiring nuanced understanding.
Choose Phi-3 Mini for edge deployment and speed. Choose Gemma 2 9B for higher quality output when resources allow.
Model Overview
| Developer | Microsoft |
| Parameters | 3.8 billion |
| Context | 128K tokens |
| License | MIT |
| Release | April 2024 |
| Memory (FP16) | ~8 GB |
| Phone Compatible | Yes (Q4) |
| Developer | Google DeepMind |
| Parameters | 9 billion |
| Context | 8K tokens |
| License | Gemma Terms |
| Release | June 2024 |
| Memory (FP16) | ~18 GB |
| Phone Compatible | Q4 only |
Benchmark Comparison
| Benchmark | 🔬 Phi-3 Mini | 💎 Gemma 2 9B | Winner |
|---|---|---|---|
| MMLU | 68.8% | 71.3% | 💎 Gemma |
| HumanEval | 58.5% | 40.2% | 🔬 Phi-3 |
| GSM8K | 75.6% | 68.1% | 🔬 Phi-3 |
| ARC-Challenge | 78.5% | 72.3% | 🔬 Phi-3 |
| HellaSwag | 76.8% | 80.0% | 💎 Gemma |
| TruthfulQA | 52.0% | 48.7% | 🔬 Phi-3 |
| MT-Bench | 7.2/10 | 6.8/10 | 🔬 Phi-3 |
| Model Size | 3.8B | 9B | 🔬 Phi-3 |
| Memory (FP16) | ~8 GB | ~18 GB | 🔬 Phi-3 |
| Speed (4-bit) | ~80 t/s | ~45 t/s | 🔬 Phi-3 |
Detailed Analysis
Phi-3 Mini's 3.8B parameters outperform many models 2-3x its size. Microsoft achieved this through careful training on "textbook-quality" data — high-quality educational content that teaches reasoning rather than memorizing patterns.
The result: a model that can run on a smartphone while matching or beating 7B-class models on coding and math. This makes Phi-3 Mini the ideal choice for edge AI, mobile apps, and on-device inference where GPU memory is limited.
Google's Gemma 2 9B leverages knowledge distillation from Gemini models, resulting in higher quality open-ended generation. Its 9B parameters give it more capacity for nuanced understanding, creative writing, and complex reasoning that requires world knowledge.
With Google's knowledge about Gemma 2 9B, it uses grouped-query attention and sliding window attention for efficient inference, but still requires ~18GB of memory in FP16 — more than a typical laptop GPU.
Phi-3 Mini: Runs on a laptop RTX 4060 (8GB) at ~80 tokens/sec. Cloud inference costs ~$0.05/1M tokens. Can run on CPU at ~15 tokens/sec.
Gemma 2 9B: Needs an RTX 4090 (24GB) for comfortable inference. Cloud inference costs ~$0.10/1M tokens. CPU inference is slow (~5 tokens/sec).
For high-volume applications, Phi-3 Mini is roughly 2x cheaper to run while also being faster. The tradeoff is lower quality on open-ended tasks.
Category-by-Category Verdict
Coding & Code Generation
Winner: Phi-3 Mini
Phi-3 Mini scores significantly higher on HumanEval (58.5% vs 40.2%) despite being smaller. Microsoft's code-heavy training pays off.
General Knowledge
Winner: Gemma 2 9B
Gemma 2 9B leads on MMLU (71.3% vs 68.8%) and HellaSwag thanks to its larger parameter count and Google's training data.
Math & Reasoning
Winner: Phi-3 Mini
Phi-3 Mini outperforms on GSM8K (75.6% vs 68.1%) and ARC-Challenge, showing superior mathematical reasoning for its size.
On-Device Efficiency
Winner: Phi-3 Mini
At 3.8B parameters, Phi-3 Mini runs on phones, laptops, and edge devices. Gemma 2 9B needs more powerful hardware.
Speed & Latency
Winner: Phi-3 Mini
Phi-3 Mini generates ~80 tokens/sec on a laptop GPU vs Gemma 2 9B's ~45 tokens/sec. Nearly 2x faster.
Output Quality
Winner: Gemma 2 9B
Gemma 2 9B produces more coherent, nuanced responses for open-ended generation thanks to its larger capacity.
Instruction Following
Winner: Phi-3 Mini
Phi-3 Mini's MT-Bench score of 7.2 edges out Gemma 2 9B's 6.8, suggesting better instruction adherence.
Licensing
Tie
Both use permissive licenses: MIT (Phi-3) and Gemma Terms (Google). Both allow commercial use with minimal restrictions.
When to Use Which Model
- Deploying AI on mobile or edge devices
- Speed and low latency are critical
- Running on consumer hardware (8GB VRAM)
- Code generation is a primary task
- Mathematical reasoning matters
- You need the most efficient model per parameter
- Output quality matters more than speed
- You have 18GB+ VRAM available
- Open-ended generation and creative writing
- Nuanced understanding of complex topics
- General-purpose chatbot applications
- Quality of reasoning trumps raw efficiency
Frequently Asked Questions
Can Phi-3 Mini run on a smartphone?
Yes. The Q4 quantized version of Phi-3 Mini (~2GB) can run on modern smartphones using frameworks like MLX (iOS) or MLC LLM (Android). Expect 10-20 tokens/sec on flagship phones.
Is Gemma 2 9B really better for creative writing?
Yes. Its 9B parameters give it more capacity for nuanced, creative output. In blind tests, Gemma 2 9B is preferred for open-ended text generation, storytelling, and content that requires depth.
Which model is better for RAG applications?
For RAG, Phi-3 Mini's 128K context window is a significant advantage over Gemma 2 9B's 8K. You can feed more retrieved context into Phi-3 Mini. However, Gemma 2 9B may better synthesize the retrieved information.
Can I fine-tune both models?
Yes. Both support fine-tuning with LoRA/QLoRA. Phi-3 Mini's smaller size makes fine-tuning faster and cheaper (~2x less GPU memory). Both have active communities on HuggingFace.