Phi-4
Intelligence benchmarks
Artificial Analysis indexes - compared with the best open and proprietary models
Intelligence
10.4
AA Index
Coding
11.2
AA Index
Agentic
0.0
AA Index
Math
18.0
AA Index
Intelligence Index - Phi-4 vs. the field
Best open-weight models (you can run locally) and leading proprietary models for context.
Coding Index comparison
Agentic Index comparison
Math Index comparison
Benchmark data from Artificial Analysis · updated 2026-06-07.
Standard benchmarks
Performance across standard evaluations
| Benchmark | Score |
|---|---|
| MMLU | 84.8 |
| HumanEval | 82.6 |
| MT-Bench | 8.6 |
| GSM8K | 91.8 |
| MMLUPRO | 71.4 |
| GPQA | 57.5 |
| AIME | 14.3 |
Will it run on your hardware?
Pick your GPU memory - see which quantizations fit, and the cheapest card for the rest
Need an exact figure for your context length? Use the VRAM calculator.
Run it locally
Copy-paste - running in under a minute
ollama run phi-4:14bvllm serve microsoft/phi-4New to this? Start with Ollama · serve to many users with vLLM.
Deep dive
Notes, sources, and the full write-up
Phi-4
Phi-4 is Microsoft's 14-billion-parameter model that achieves GPT-4-level performance on coding and math benchmarks. It was trained on high-quality synthetic data rather than massive web crawl, making it exceptionally efficient for its size.
Key features
- Synthetic data training - 10x more efficient than traditional training
- 84.8 MMLU - highest of any 14B model
- 82.6 HumanEval - beats models 5x its size
- MIT license - fully open for commercial use
- 91.8 GSM8K - top-tier math reasoning
VRAM math
| Quant | VRAM | Recommended Hardware |
|---|---|---|
| Q4_K_M | ~8.5 GB | RTX 3090 |
| Q5_K_M | ~10.5 GB | RTX 4090 |
| Q8_0 | ~16 GB | RTX 4090 |
| FP16 | ~28 GB | RTX 5090 |
How to run
ollama run phi-4:14bWhat the community says
"Phi-4 is the best coding model you can run on a single 12GB card. It beats 70B models on HumanEval."
- r/LocalLLaMA, 289 upvotes
Frequently asked
Quick answers to common questions
How much VRAM does Phi-4 need?
Phi-4 with 14B parameters needs approximately 8.5 GB at Q4_K_M quantization. Use our VRAM calculator for an exact estimate.
Is Phi-4 better than other Phi models?
Phi-4 scores 84.8 on MMLU and 82.6 on HumanEval. It has 14B parameters with 16,384 context - a strong choice for coding, math, reasoning.
What license is Phi-4 under?
Phi-4 is released under the MIT license, making it suitable for most commercial and personal projects.
What hardware runs Phi-4 well?
With 14B parameters, Phi-4 requires adequate VRAM. High-end GPUs like the RTX 4090 (24GB), RTX 5090 (32GB), or Mac Studio with unified memory are good options. Check our hardware directory for specific recommendations.
What is the best quantization for Phi-4?
Q4_K_M is the recommended sweet spot - ~98% of FP16 quality at ~27% of the size. Q5_K_M (~10.5 GB) is an option if you have spare VRAM. Use our VRAM calculator to compare.
What models compete with Phi-4?
Phi-4 competes with other models in its class. Browse our model directory for comparisons, benchmarks, and community reviews to find the best fit.
Compare & pair with
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