Phi-4 Mini Instruct
Phi-4 Mini Instruct is an API model from Microsoft. It’s positioned for general text tasks—work that benefits from iteration, not just one-shot answers.
Intelligence
Below avg
Speed
Slow
44 tok/s output
Cost
Low
$0.08 in / $0.35 out
Context
128K
Up to 128,000 tokens
How are Intelligence, Speed & Cost bucketed?
Intelligence and Speed buckets are percentile ranks on
Artificial Analysis. Cost buckets are fixed dollar
thresholds keyed off output-token price ($/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
Core Capabilities
Long Documents
Handles entire codebases, books, and multi-doc RAG.
Context Window
128k tokens
≈ 98 pages
4k Chat 聊天
32k Long docs 长文档
128k This model 本模型
400k Multi-doc 多文档
1M Codebase 整个代码库
10M
Availability
API
Available
Product / App
Not available
Open Source
Not released
Enterprise
Contact sales
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
6.0
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
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
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)
Model Evolution
phi is Microsoft's language model family.