Phi-4 Mini vs Phi-3 Medium: Microsoft's Compact LLM Compared

Phi-4 Mini vs Phi-3 Medium: Microsoft's Compact LLM Compared

Microsoft's Phi series has consistently punched above its weight class. Phi-4 Mini (3.8B) outperforms models twice its size on reasoning tasks. Here's the full comparison and local setup guide.

Phi-4 Mini vs Phi-3 Medium: Key Differences

ModelParamsContextMMLUHumanEval
Phi-4 Mini3.8B128K70.982.1
Phi-3 Medium14B128K78.070.2
Phi-3 Mini3.8B128K68.858.1

Why Phi-4 Mini Outperforms on Coding

Microsoft trained Phi-4 Mini on a heavily curated dataset weighted toward coding, math, and reasoning-dense content. The HumanEval score of 82.1 at 3.8B parameters is extraordinary — comparable to GPT-3.5 Turbo and beating most 7B models.

This makes Phi-4 Mini an ideal on-device model. At Q4 quantization (~2.3 GB), it runs at 80–120 tokens/sec on most modern CPUs and 200+ tokens/sec on an RTX 3060. Hardware speed comparisons are available at LLMHardware.io.

Running Phi-4 Mini Locally

# Via Ollama
ollama pull phi4-mini
ollama run phi4-mini "Write a merge sort in Python"

# Via llama.cpp
./llama-cli   -m phi-4-mini-Q4_K_M.gguf   -p "You are a helpful coding assistant.

User: Write a binary search tree
Assistant:"   -n 512 --repeat-penalty 1.1

Best Use Cases for Phi-4 Mini

  • Code completion and explanation on CPU-only machines
  • Embedded AI in applications with strict memory constraints
  • Raspberry Pi 5 and Apple Silicon MacBook inference
  • Batch processing tasks where speed matters more than quality

For tasks requiring deep reasoning or long-form generation, the larger Phi-3 Medium (14B) still wins. But for the 3.8B tier, Phi-4 Mini is unambiguously the best option available in 2026.

Comments

Post a Comment

Popular posts from this blog

AMD RX 9070 XT for Local LLMs: ROCm Performance Guide (2026)

The AMD RX 9070 XT launched in early 2026 at $599 with 16GB GDDR6 and 648 GB/s memory bandwidth. For local LLM users who want a mid-range GPU without NVIDIA prices, this changes the calculus significantly. Here is what it actually delivers. RX 9070 XT Specs vs Competition GPU VRAM Bandwidth Price LLM Score RX 9070 XT 16 GB 648 GB/s $599 ~60 tok/s RTX 4070 Ti Super 16 GB 672 GB/s $799 ~62 tok/s RTX 4080 Super 16 GB 736 GB/s $999 ~68 tok/s RTX 4090 24 GB 1,008 GB/s ~$1,100 ~90 tok/s At $599 with 16GB VRAM, the RX 9070 XT is the best value GPU for local LLMs in the mid-range tier in 2026. See the full comparison at LLMHardware.io's AMD GPU guide . ROCm Setup on Linux (2026) # Ubuntu 22.04/24.04 sudo apt install rocm-hip-libraries hip-base # or use the AMD installer: wget https://repo.radeon.com/amdgpu-install/6.3/ubuntu/jammy/amdgpu-install_6.3.60300-1_all.deb sudo dpkg -i amdgpu-install*.deb sudo amdgpu-install --usecase=rocm,lrt,openclsdk # Verify ROCm sees your GPU r...
Read more

llama.cpp flash attention: how to enable and benchmark

Image
Flash attention computes attention with far less memory traffic. The math is identical; the memory movement is not. In llama.cpp it matters most in two situations: long context windows and tight video memory (VRAM). With flash attention enabled, the key-value cache shrinks substantially. A 32k-token context window becomes realistic on a 16GB card, and the prompt-processing phase speeds up, so long documents get ingested faster. The catch is compatibility: not every model and quantization combination supports it, and on some setups the speedup is modest. If you run short chats on a strong GPU, you may barely notice. Where it shines is retrieval-augmented generation (RAG) and long-document work on mid-range cards. Enable it, compare your tokens per second, and keep it if your workload benefits. For the common case of pasting a long PDF and asking questions, it is close to free performance. See how context length changes VRAM needs .
Read more

SillyTavern with Local LLMs: Complete Setup for AI Companions

Image
SillyTavern is the most feature-rich frontend for character-based AI conversations. What began as an AI roleplay tool has evolved into a powerful platform with persona management, memory systems, extensive prompt engineering tools, and connections to every major local inference backend. Here's how to set it up with local models. Quick connect: SillyTavern connects to Ollama, KoboldCpp, llama.cpp server, and TabbyAPI (ExLlamaV2) out of the box. Any of the hardware setups at LLMHardware.io provides a solid backend for SillyTavern. Installation Install via git: git clone https://github.com/SillyTavern/SillyTavern && cd SillyTavern && npm install then node server.js . Access at localhost:8000. For persistent installation, use the included start.sh script. SillyTavern uses Node.js and runs on any platform. The companion app (STMP) enables mobile access to your SillyTavern instance via Tailscale or local network. Character Cards and the TavernAI Format Chara...
Read more