Phi-4
Q4 GGUF download: microsoft/phi-4-gguf - 8.44 GB on disk, read 2026-10-22. VRAM figures above are estimates; this is the actual file size.
Intelligence benchmarks
Artificial Analysis indexes - compared with the best open and proprietary models
Intelligence
5.9
AA Index
Intelligence Index - Phi-4 vs. the field
Best open-weight models (you can run locally) and leading proprietary models for context.
Benchmark data from Artificial Analysis · updated 2026-10-22.
Hosted API cost & speed
What it costs to rent this model instead of running it locally
| Metric | Value |
|---|---|
| Input (per 1M tokens) | $0.13 |
| Output (per 1M tokens) | $0.50 |
| Median speed (hosted API) | 40 tok/s |
Median across hosted providers, measured by Artificial Analysis - updated 2026-10-22. Local llama.cpp speed depends on your hardware — see the hardware pages for estimates.
Arena rating (LMArena)
Human preference wins from head-to-head chat battles
| Metric | Value |
|---|---|
| Overall arena rating | 1216.7 |
| Rank (overall) | #315 |
| Battle votes | 24,126 |
Overall rating published by LMArena on 2026-10-20 - a pairwise human-preference Elo across live chat battles; higher is better. Fetched 2026-10-22. Open-weights models compete head-to-head with proprietary ones here — no benchmark prompts, just what people pick.
Standard benchmarks
Performance across standard evaluations
| Benchmark | Score |
|---|---|
| MMLU | 84.8 |
| HumanEval | 82.6 |
| MT-Bench | 8.6 |
| GSM8K | 91.8 |
| MMLU-Pro | 71.4 |
| GPQA Diamond | 57.5 |
| AIME | 18 |
| HLE | 3.8 |
| LiveCodeBench | 23.1 |
| IFBench | 23.5 |
| Terminal-Bench | 3.8 |
Source: Artificial Analysis component evals - updated 2026-10-22. Other scores are carried over from the site's initial dataset - no source on record, not re-verified.
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-4:14bFrom the community
What users are saying
“It's easy to argue that Llama-3.3 8B performs better than GPT-3.5. Compare their benchmarks, and try the two side-by-side. Phi-4 is yet another step towards a small, open, GPT-4 level model. I think we're getting quite close.”
“Related Phi-4: Microsoft's Newest Small Language Model Specializing in Complex Reasoning (439 points, 24 days ago, 144 comments) https://news.ycombinator.com/item?id=42405323 Also on hugging face https://huggingface.co/microsoft/phi-4”
“Hey HN family! I found a few bugs for Phi-4 - Microsoft's latest MIT licensed LLM to be on par with GPT-4o mini 1. End of sentence should be <|im_end|> not <|endoftext|> 2. Chat template should not auto add an assistant prompt 3. Padding token should not be EOS but <|dummy_87|> I also converted Phi-4 to Llama-arch.”
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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Nearby options
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