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What is Phi-4-mini-reasoning?

Phi-4-mini-reasoning is an advanced transformer-based language model that boasts 3.8 billion parameters, tailored specifically for superior performance in mathematical reasoning and systematic problem-solving, especially in scenarios with limited computational resources and low latency. The model's optimization is achieved through fine-tuning with synthetic data generated by the DeepSeek-R1 model, which effectively balances performance and intricate reasoning skills. Having been trained on a diverse set of over one million math problems that vary from middle school level to Ph.D. complexity, Phi-4-mini-reasoning outperforms its foundational model by generating extensive sentences across numerous evaluations and surpasses larger models like OpenThinker-7B, Llama-3.2-3B-instruct, and DeepSeek-R1 in various tasks. Additionally, it features a 128K-token context window and supports function calling, which ensures smooth integration with different external tools and APIs. This model can also be quantized using the Microsoft Olive or Apple MLX Framework, making it deployable on a wide range of edge devices such as IoT devices, laptops, and smartphones. Furthermore, its design not only enhances accessibility for users but also opens up new avenues for innovative applications in the realm of mathematics, potentially revolutionizing how such problems are approached and solved.

What is Inkling-Small?

Inkling-Small is an efficient multimodal AI model built to deliver strong reasoning and coding performance at a fraction of Inkling’s size. It is a Mixture-of-Experts transformer with 276 billion total parameters and 12 billion active parameters. The model was trained on NVIDIA GB300 NVL72 systems and is designed to combine high capability with more efficient inference. Inkling-Small supports native reasoning across text, images, and audio, allowing it to work across multimodal tasks without relying on separate encoders. Its context window supports up to one million tokens, making it useful for long-form reasoning, large-scale code understanding, document analysis, and agentic workflows. Users can adjust reasoning effort from minimal to extra high depending on whether they need faster responses or deeper computation. The model’s training process includes improved pre-training data, post-training with on-policy distillation from Inkling, and extended agentic coding reinforcement learning. These techniques helped Inkling-Small outperform its larger counterpart on reasoning and coding benchmarks. The model performs well in coding and tool-use harnesses and exceeds 80% on SWE-bench Verified. Its encoder-free architecture processes audio as dMel spectrograms and images as 40-by-40-pixel patches alongside text tokens. By combining efficient MoE design, one-million-token context, adjustable reasoning effort, multimodal processing, coding strength, and tool-use performance, Inkling-Small is designed for developers and teams that need capable AI with lower active compute requirements.

Media

Media

Integrations Supported

Hugging Face
Microsoft Azure
Microsoft Foundry
Microsoft Foundry Models
Model Context Protocol (MCP)
Tinker

Integrations Supported

Hugging Face
Microsoft Azure
Microsoft Foundry
Microsoft Foundry Models
Model Context Protocol (MCP)
Tinker

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided
Free Version
Free Trial Offered?

Pricing Information

$0.30 per million input tokens
$0.30 per million input tokens and $1.20 per million output tokens
Free Version
Free Trial Offered?

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Microsoft

Date Founded

1975

Company Location

United States

Company Website

azure.microsoft.com/en-us/blog/one-year-of-phi-small-language-models-making-big-leaps-in-ai/

Company Facts

Organization Name

Thinking Machines Lab

Date Founded

2025

Company Location

United States

Company Website

thinkingmachines.ai/news/inkling-small/

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