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Phi is Microsoft’s small open-weight language model family. Phi models punch well above their parameter count on reasoning benchmarks and are designed for edge and on-device inference.
See all models from MicrosoftModels in family
1
Open weight
1
API only
0
Avg score
65.8
Top benchmark
—
Total HF downloads
268.3K
Primary modality
Text
First release
Feb 2025
Latest release
Feb 2025
Every release in the Phi family, ranked by composite score across benchmarks, popularity, efficiency, and versatility.
| # | Model | Modality | Score | Params | Released |
|---|---|---|---|---|---|
| 1 | audio | BB65.8 | 5.6B | Feb 2025 |
When each release shipped, newest first. Useful for tracking version cadence.
Composite grades across this family. Higher is better, blending benchmarks, popularity, and efficiency.
Models with downloadable weights, ranked by composite score.
| # | Model | Modality | Score | Params | Released |
|---|---|---|---|---|---|
| 1 | audio | BB65.8 | 5.6B | Feb 2025 |
The Phi family is a series of AI models from Microsoft. This page lists every release in the family with its benchmark scores, parameter count, and hardware requirements.
By composite score, Phi-4-multimodal-instruct is currently the top model in the family. For local inference, match the parameter count to your VRAM budget. For quality, pick the highest scorer that fits.
See the open-weight section above for models you can run locally. The API-only section lists closed releases that must be accessed through the provider’s API.
Spin up an instance in the cloud, or pick local hardware that fits.
Advertising disclosure: we earn commissions when you shop through the links below.
Vast.ai
Decentralized GPU marketplace with the lowest hourly prices.
RunPod
Pay-per-second GPU rentals starting at $0.20/hr.
Digital Ocean
Spin up a GPU droplet in minutes, starting at $0.75/hr.
Vultr
GPU cloud with hourly and monthly plans, starting at $0.50/hr.
GPU Mart
Dedicated GPU servers and VPS billed monthly, starting at $0.50/hr.
PPQ.ai
Multi-model inference gateway for production workloads.
Find Local Hardware
See which GPU, Mac, or workstation can run Phi on-prem.
Local LLM Mini PCs
Compact machines that run Phi on your desk. Chosen for local inference.
