Project AURIONAI Use-Cases Research & Innovation
Microsoft AI
reasoning

Phi-4-mini-flash-reasoning

Microsoft 3.85B reasoning model whose card names lightweight deployment on edge or mobile systems as a possible use.

Figure from the Phi-4-mini-flash-reasoning model card.
Levelmodel
Classreasoning
Parameters3.85B
Context64,000
Released19 Jun 2025
Opennessopen weights permissive
Runs onmobile or NPU
Statusactive

From the model's own page; who made each picture and where it comes from are listed on the Image Credits page.

Benchmark Results

vendor-reported
vendor-reported
33.59
050100

Score in %. Higher is better.

vendor-reported
52.29
050100

Score in %. Higher is better.

GPQA Diamondknowledge and reasoning
vendor-reported
45.08
050100

Score in %. Higher is better.

vendor-reported
92.45
050100

Score in %. Higher is better.

Where this comes from
Every score names who ran it: vendor-reported, maintainer-run or independent. A rank is shown with the population it was ranked in (gap rule 3).

R&D Topics

Identity

Developer
Microsoft AI
Family
phi-4
Released
19 Jun 2025 sourcesregistry date
Weights published
–
Record checked
10 Oct 2026
Where this comes from
Developer and dates from the research notes. Release date is the vendor's public announcement; weights date is when open weights were published.

Architecture and Size

Architecture
hybrid SambaY decoder-hybrid-decoder: state-space layers, Gated Memory Unit and Differential Attention
Total parameters
3.85B sources
Active parameters
–
Checkpoint parameters
–
Context length
64,000 tokens
Input
text
Output
text
Where this comes from
Parameter counts as the developer states them. Where the checkpoint holds a different total, both are shown (register A16).

Openness and Access

Openness
open weights permissive
Licence
MIT License sources
Access routes
open weights download, API
Where this comes from
The licence file governs over registry tags and aggregator labels.

Deployment

Runs on
mobile or NPU
Footprint
Up to 10x decoding throughput over Phi-4-mini-reasoning (2K prompt, 32K generation), measured with vLLM on a single A100-80GB (not edge hardware). Quantized footprint and on-device latency unverified
Runtimes
not in the notes
Where this comes from
Deployment tiers and runtimes as named in the notes. Footprint is the notes' figure, not a measurement by AURION.

Indonesia

Indonesian support
not stated
Regional languages
not in the notes
Jakarta hosting
unknown
Languages
not in the notes
Where this comes from
Indonesia fields are carried on every model; unknown is an allowed value.

Lineage

The notes record no lineage for this model.

Industry Use Cases

No industry use case links this model.

Sources

  1. huggingface.co/microsoft/Phi-4-mini-flash-reasoning https://huggingface.co/microsoft/Phi-4-mini-flash-reasoning
  2. huggingface.co/api/models/microsoft/Phi-4-mini-flash-reasoning https://huggingface.co/api/models/microsoft/Phi-4-mini-flash-reasoning
  3. microsoft / phi-4-mini-flash-reasoning docs.api.nvidia.com https://docs.api.nvidia.com/nim/reference/microsoft-phi-4-mini-flash-reasoning

To cite this page: Project AURION, AI Use-Cases Research & Innovation, "Phi-4-Mini-Flash-Reasoning", https://aurion.semai.id/models/microsoft-phi-4-mini-flash-reasoning/, records of October 2026.