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Phi-3 MinivsPhi-4

Microsoft vs Microsoft — Side-by-side model comparison

Phi-4 leads 4/5 categories

Head-to-Head Comparison

MetricPhi-3 MiniPhi-4
Provider
Arena Rank
#28
Context Window
128K
16K
Input Pricing
Free (open)/1M tokens
Free/1M tokens
Output Pricing
Free (open)/1M tokens
Free/1M tokens
Parameters
3.8B
14B
Open Source
Yes
Yes
Best For
Edge deployment, mobile, on-device AI
Small model research, edge deployment, reasoning
Release Date
Apr 23, 2024
Dec 12, 2024

Phi-3 Mini

Phi-3 Mini is Microsoft's compact 3.8 billion parameter model that delivers surprisingly strong performance for its size, rivaling models many times larger on reasoning and coding benchmarks. It features a 128K context window despite its small size, making it ideal for on-device deployment in mobile phones, laptops, and edge devices where computational resources are severely constrained.

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Phi-4

Phi-4 is Microsoft's small language model that demonstrates remarkable capability relative to its size, embodying the 'small but mighty' approach to AI. Through innovative training on high-quality synthetic and curated data, Phi-4 achieves performance comparable to much larger models on reasoning, coding, and STEM tasks. As an open-source model, it's ideal for on-device deployment, edge computing, and applications requiring local AI processing without cloud connectivity. Phi-4 has been influential in proving that model quality depends more on data quality and training methodology than raw parameter count.

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Key Differences: Phi-3 Mini vs Phi-4

1

Phi-3 Mini supports a larger context window (128K), allowing it to process longer documents in a single request.

2

Phi-3 Mini has 3.8B parameters vs Phi-4's 14B, which affects inference speed and capability.

P

When to use Phi-3 Mini

  • +You need to process long documents (128K context)
  • +Your use case involves edge deployment, mobile, on-device ai
View full Phi-3 Mini specs →
P

When to use Phi-4

  • +Your use case involves small model research, edge deployment, reasoning
View full Phi-4 specs →

The Verdict

Phi-4 wins our head-to-head comparison with 4 out of 5 category wins. It's the stronger choice for small model research, edge deployment, reasoning, though Phi-3 Mini holds an edge in edge deployment, mobile, on-device ai.

Last compared: March 2026 · Data sourced from public benchmarks and official pricing pages

Frequently Asked Questions

Which is better, Phi-3 Mini or Phi-4?
In our head-to-head comparison, Phi-4 leads in 4 out of 5 categories (arena rank, context window, input pricing, output pricing, and parameters). Phi-4 excels at small model research, edge deployment, reasoning, while Phi-3 Mini is better suited for edge deployment, mobile, on-device ai. The best choice depends on your specific requirements, budget, and use case.
How does Phi-3 Mini pricing compare to Phi-4?
Phi-3 Mini charges Free (open) per 1M input tokens and Free (open) per 1M output tokens. Phi-4 charges Free per 1M input tokens and Free per 1M output tokens. For high-volume production workloads, the pricing difference can significantly impact total cost of ownership.
What is the context window difference between Phi-3 Mini and Phi-4?
Phi-3 Mini supports a 128K token context window, while Phi-4 supports 16K tokens. Phi-3 Mini can process longer documents, codebases, and conversations in a single request. Context window size matters most for tasks involving long documents, large codebases, or extended conversations.
Can I use Phi-3 Mini or Phi-4 for free?
Phi-3 Mini is a paid API model starting at Free (open) per 1M input tokens. Phi-4 is available for free (open-source). Open-source models can be self-hosted for free but require your own GPU infrastructure.
Which model has better benchmarks, Phi-3 Mini or Phi-4?
Phi-3 Mini's arena rank is not yet available, while Phi-4 holds rank #28. Note that benchmarks don't capture every use case — we recommend testing both models on your specific tasks.
Is Phi-3 Mini or Phi-4 better for coding?
Phi-3 Mini's primary strength is edge deployment, mobile, on-device ai. Phi-4's primary strength is small model research, edge deployment, reasoning. For coding specifically, arena rank and code-specific benchmarks are the best indicators of performance.