Phi-4
Microsoft's Synthetic-Data Small Model
Microsoft's December 2024 small model — only 14 billion parameters but matching GPT-4o on reasoning benchmarks. The trick: Phi-4 was trained mostly on SYNTHETIC data — examples generated by a larger model and filtered for quality. It's a proof point that data quality matters more than raw web-scraped quantity.
Intelligence
Below avg
Speed
Slow
38 tok/s output
Cost
Low
$0.13 in / $0.50 out
Context
16K
Up to 16,384 tokens
How are Intelligence, Speed & Cost bucketed?
Intelligence and Speed buckets are percentile ranks on
Artificial Analysis. Cost buckets are fixed dollar
thresholds keyed off output-token price ($/M out).
Intelligence
- Top 1%≤ 1%
- Top 5%≤ 5%
- Top 10%≤ 10%
- Good≤ 25%
- Medium≤ 50%
- Below avg> 50%
Speed
- Top 1%≥ 345 tok/s
- Top 5%≥ 237 tok/s
- Top 10%≥ 196 tok/s
- Good≥ 146 tok/s
- Medium≥ 90 tok/s
- Slow< 90 tok/s
Cost
- Freeopen weights · self-host
- Low< $1 / M out
- Moderate$1–5 / M out
- High≥ $5 / M out
Why it matters
Validated synthetic-data training at scale. Showed that closed labs' web-scraping advantage matters less than once thought — a small model + great data can beat a huge model + average data.
Core Capabilities
Long Documents
Handles entire codebases, books, and multi-doc RAG.
Generative
Produces images, video, audio, or other media.
Research
Foundational paper or scientific contribution.
Context Window
16k tokens
≈ short doc
4k Chat 聊天
32k Long docs 长文档
128k Books 整本书
400k Multi-doc 多文档
1M Codebase 整个代码库
10M
16k
Availability
API
Not available
Product / App
Not available
Open Source
Released
Enterprise
—
Pricing Model
Free / self-host
Open weights — pay only for compute.
Self-host Capability / Performance
Where this model sits relative to the middle 60% of models in the tree. All scores are 0–10 (higher is better).
Lower 20% Upper 80% This model
Context / memory
Context window size · log-scaled
6.0
9.0
3.0
Lower 20% 20th percentile — 20% of models score below this This model Where the current model lands Upper 80% 80th percentile — only 20% of models score above this
Percentile boundaries are computed across every model in the tree that reports the underlying benchmark for each capability.
What it feels like
- 14B parameters that outperforms models 5x larger on math and reasoning — synthetic-data scaling laws in action
- MMLU 84.8% (up from Phi-3's 77.9%); MATH 56.1% (up from 42.5%) — large quality jumps without size jump
- HumanEval 82.6% — competitive with much larger frontier-tier models on Python code generation
- Phi-4-reasoning variants beat o1-mini and DeepSeek-R1-Distill-Llama-70B on most reasoning benchmarks
- Phi-4-reasoning-plus surpasses full DeepSeek-R1 on AIME 2025 — 14B beating 671B is the headline
- Trained on 9.8T tokens over three weeks, mixing GPT-4o-generated synthetic data with curated web text
Best use cases
- On-device reasoning where larger models can't fit
- Math, science, and code benchmarks at SLM cost
- Self-hosted production where 14B inference is affordable on a single GPU
- Distillation experiments and SLM research baselines
Tools to try
Not ideal for
- Broad-knowledge factual QA — small models still have limited recall
- Multimodal tasks (Phi-4 base is text-only; vision lives in Phi-4-multimodal)
- Workloads needing 100K+ context (limited window vs frontier models)
Model Evolution
phi is Microsoft's language model family.