Phi 4

Phi-4

Quick answer: Phi-4 is Microsoft's December 2024 14B-parameter language model, scoring 70.4% on MMLU-Pro, 75.4% on Arena Hard, 82.6% on HumanEval, and 27.4% on BigCodeBench. Under Apache 2.0 license with open weights, it delivers GPT-4-class knowledge at a 14B parameter scale. The model uses a 16K-token context window.

At a Glance

Where Phi-4 leads

• 70.4% MMLU-Pro — competitive with models 3-5× its parameter count
• 75.4% Arena Hard — strong general instruction following
• 82.6% HumanEval — solid function-level code completion
• Apache 2.0 — unrestricted commercial use
• 14B parameters — highly efficient for self-hosting
• Trained on synthetic high-quality data — disproportionately capable for its size

Where it lags

• 16K context window — significantly smaller than Qwen3 32B (131K) or Llama 4 models
• 27.4% BigCodeBench — moderate performance on complex code generation
• Text-only: no vision capability
• 14B dense — no MoE efficiency benefits

Best for: Edge deployment, resource-constrained inference, self-hosting on consumer GPUs, and tasks requiring GPT-4-class knowledge at 14B scale.

What Phi-4 Is

Phi-4 is Microsoft's December 2024 small language model, the fourth generation of the Phi series. Microsoft's Phi research focuses on demonstrating that high-quality training data — rather than raw parameter count — can yield outsized capability in small models. Phi-4 at 14B achieves MMLU-Pro scores competitive with much larger open-weight models.

The model is trained primarily on synthetic data: carefully curated textbook-quality content, code, and reasoning problems. This approach produces a model that excels at the academic knowledge and reasoning tasks covered by MMLU-Pro and Arena Hard, with 70.4% and 75.4% scores respectively placing it well above other models in its size class.

Phi-4's 16K context window is its most significant limitation relative to peers: Qwen3 32B offers 131K context at similar size, and Llama 4 Scout offers 10M. For tasks requiring long context, alternative open-weight models are recommended. For tasks fitting within 16K tokens, Phi-4 offers exceptional capability at 14B.

Specifications

Pricing

Phi-4 is available as open weights under Apache 2.0 — free to self-host. Available via Azure AI Foundry and third-party providers.

Context Window

Phi-4 has a 16,384-token context window — approximately 12 pages of text. This is significantly smaller than most 2025-era models; for tasks requiring longer context, consider Qwen3 32B (131K) or Llama 4 Maverick (1M).

Public Benchmark Scores

Phi-4 vs Alternatives

Phi-4's 70.4% MMLU-Pro at 14B is a standout result — Llama 4 Scout at 74.3% requires 17B active MoE parameters with a much larger infrastructure footprint. For tasks within 16K context, Phi-4 is the highest-knowledge-per-parameter open-weight model in its class.

Run Phi-4

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "microsoft/phi-4"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto")

inputs = tokenizer("Explain Newton's third law:", return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Related Models & Benchmarks

• Compare: Qwen3 32B · Llama 4 Scout · Hermes 3 70B
• Benchmarks: MMLU-Pro · Arena Hard · HumanEval

Frequently Asked Questions

Specs from Microsoft's official Phi-4 release (December 2024) and Benchgen evaluations. Last updated 2026-07-24.