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About

Phi-4-mini-reasoning is a 3.8-billion parameter transformer-based language model optimized for mathematical reasoning and step-by-step problem solving in environments with constrained computing or latency. Fine-tuned with synthetic data generated by the DeepSeek-R1 model, it balances efficiency with advanced reasoning ability. Trained on over one million diverse math problems spanning multiple levels of difficulty from middle school to Ph.D. level, Phi-4-mini-reasoning outperforms its base model on long sentence generation across various evaluations and surpasses larger models like OpenThinker-7B, Llama-3.2-3B-instruct, and DeepSeek-R1. It features a 128K-token context window and supports function calling, enabling integration with external tools and APIs. Phi-4-mini-reasoning can be quantized using Microsoft Olive or Apple MLX Framework for deployment on edge devices such as IoT, laptops, and mobile devices.

About

Qwen2.5-VL-32B is a state-of-the-art AI model designed for multimodal tasks, offering advanced capabilities in both text and image reasoning. It builds upon the earlier Qwen2.5-VL series, improving response quality with more human-like, formatted answers. The model excels in mathematical reasoning, fine-grained image understanding, and complex, multi-step reasoning tasks, such as those found in MathVista and MMMU benchmarks. Its superior performance has been demonstrated in comparison to other models, outperforming the larger Qwen2-VL-72B in certain areas. With improved image parsing and visual logic deduction, Qwen2.5-VL-32B provides a detailed, accurate analysis of images and can generate responses based on complex visual inputs. It has been optimized for both text and image tasks, making it ideal for applications requiring sophisticated reasoning and understanding across different media.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

Developers wanting an AI model for educational tools and embedded tutoring

Audience

Developers and researchers in AI, machine learning, and data science needing a model for complex reasoning and image-text integration

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Pricing

No information available.
Free Version
Free Trial

Pricing

No information available.
Free Version
Free Trial

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

Microsoft
Founded: 1975
United States
azure.microsoft.com/en-us/blog/one-year-of-phi-small-language-models-making-big-leaps-in-ai/

Company Information

Alibaba
Founded: 1999
China
qwenlm.github.io/blog/qwen2.5-vl-32b/

Alternatives

Phi-4-reasoning

Phi-4-reasoning

Microsoft

Alternatives

Qwen2-VL

Qwen2-VL

Alibaba
Qwen3-VL

Qwen3-VL

Alibaba
DeepSeek R1

DeepSeek R1

DeepSeek
Qwen

Qwen

Alibaba
Qwen3.5

Qwen3.5

Alibaba
DeepCoder

DeepCoder

Agentica Project
Qwen3.5-Plus

Qwen3.5-Plus

Alibaba

Categories

Categories

Integrations

Hugging Face
Microsoft Azure
Microsoft Foundry
Microsoft Foundry Models

Integrations

Hugging Face
Microsoft Azure
Microsoft Foundry
Microsoft Foundry Models
Claim Phi-4-mini-reasoning and update features and information
Claim Phi-4-mini-reasoning and update features and information
Claim Qwen2.5-VL-32B and update features and information
Claim Qwen2.5-VL-32B and update features and information