Phi-3-small-8k-instruct-onnx-cuda
Microsoft基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗DISCOVER & COMPARE
Explore AI models 85003
Phi-3-vision-128k-instruct
Microsoft🎉 **Phi-3.5**: [[mini-instruct]](https://huggingface.co/microsoft/Phi-3.5-mini-instruct); [[MoE-instruct]](https://huggingface.co/microsoft/Phi-3.5-MoE-instruct) ; [[vision-instruct]](https://huggingface.co/microsoft/Phi-3.5-vision-instruct) The Phi-3-Vision-128K-Instruct is a lightweight, state-of-the-art open multimodal model built upon datasets which include - synthetic data and filtered publicly available websites - with a focus on very high-quality, reasoning dense data both on text and vision. The model belongs to the Phi-3 model family, and the multimodal version comes with 128K context length (in tokens) it can support. The model underwent a rigorous enhancement process, incorporating both supervised fine-tuning and direct preference optimization to ensure precise instruction adherence and robust safety measures. + Phi-3 Microsoft Blog + Phi-3 Technical Report + Phi-3 on Azure AI Studio + Phi-3 Cookbook
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Phi-3-vision-128k-instruct-onnx
Microsoft基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Phi-3-vision-128k-instruct-onnx-cpu
Microsoft基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Phi-3-vision-128k-instruct-onnx-cuda
Microsoft基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Phi 4
MicrosoftMicrosoft Research Phi-4 is designed to perform well in complex reasoning tasks and can operate efficiently in situations with limited memory or where quick responses are needed. At 14 billion...
Input Text→ Output Text
$0.07 / M input tokensOpenRouter · microsoft/phi-4
View details ↗phi-4-gguf
MicrosoftOur training data is an extension of the data used for Phi-3 and includes a wide variety of sources from: 1. Publicly available documents filtered rigorously for quality, selected high-quality educational data, and code. 2. Newly created synthetic, “textbook-like” data for the purpose of teaching math, coding, common sense reasoning, general knowledge of the world (science, daily activities, theory of mind, etc.).
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Phi-4 Mini Instruct
Microsoft基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Phi-4-mini-flash-reasoning
Microsoft基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Phi-4-mini-instruct
Microsoft🎉**Phi-4**: [mini-reasoning | reasoning] | [multimodal-instruct | onnx]; [mini-instruct | onnx] Phi-4-mini-instruct is a lightweight open model built upon synthetic data and filtered publicly available websites - with a focus on high-quality, reasoning dense data. The model belongs to the Phi-4 model family and supports 128K token context length. The model underwent an enhancement process, incorporating both supervised fine-tuning and direct preference optimization to support precise instruction adherence and robust safety measures. 📰 Phi-4-mini Microsoft Blog 📖 Phi-4-mini Technical Report 👩🍳 Phi Cookbook 🏡 Phi Portal 🖥️ Try It Azure, Huggingface
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Phi-4-mini-instruct-onnx
Microsoft基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Phi-4-mini-reasoning
MicrosoftPhi-4-mini-reasoning is a lightweight open model built upon synthetic data with a focus on high-quality, reasoning dense data further finetuned for more advanced math reasoning capabilities. The model belongs to the Phi-4 model family and supports 128K token context length. 📰 Phi-4-mini-reasoning Blog, and Developer Article 📖 Phi-4-mini-reasoning Technical Report | HF paper 👩🍳 Phi Cookbook 🏡 Phi Portal 🖥️ Try It Azure 🎉**Phi-4 models**: [Phi-4-reasoning] | [multimodal-instruct | onnx]; [mini-instruct | onnx]
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Phi-4-mini-reasoning-onnx
Microsoft基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Phi-4 Multimodal Instruct
Microsoft基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Phi-4-multimodal-instruct
Microsoft🎉**Phi-4**: [mini-reasoning | reasoning] | [multimodal-instruct | onnx]; [mini-instruct | onnx] Phi-4-multimodal-instruct is a lightweight open multimodal foundation model that leverages the language, vision, and speech research and datasets used for Phi-3.5 and 4.0 models. The model processes text, image, and audio inputs, generating text outputs, and comes with 128K token context length. The model underwent an enhancement process, incorporating both supervised fine-tuning, direct preference optimization and RLHF (Reinforcement Learning from Human Feedback) to support precise instruction adherence and safety measures. The languages that each modal supports are the following: - Text: Arabic, Chinese, Czech, Danish, Dutch, English, Finnish, French, German, Hebrew, Hungarian, Italian, Japanese, Korean, Norwegian, Polish, Portuguese, Russian, Spanish, Swedish, Thai, Turkish, Ukrainian - Vision: English - Audio: English, Chinese, German, French, Italian, Japanese, Spanish, Portuguese 📰 Phi-4-multimodal Microsoft Blog 📖 Phi-4-multimodal Technical Report 🏡 Phi Portal 👩🍳 Phi Cookbook 🖥️ Try It on Azure, GitHub, Nvidia, Huggingface playgrounds 📱Huggingface Spaces Thoughts Organizer, Stories Come Alive, Phine Speech Translator
Input Audio→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Phi-4-multimodal-instruct-onnx
Microsoft基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
Input Audio→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗phi-4-onnx
Microsoft基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Phi-4-reasoning
Microsoft> [!IMPORTANT] > To fully take advantage of the model's capabilities, inference must use `temperature=0.8`, `top_k=50`, `top_p=0.95`, and `do_sample=True`. For more complex queries, set `max_new_tokens=32768` to allow for longer chain-of-thought (CoT). Given the nature of the training data, **always use** ChatML template with the **following system prompt** for inference: *Phi-4-reasoning is also supported out-of-the-box by Ollama, llama.cpp, and any Phi-4 compatible framework.*
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Phi-4-reasoning-onnx
Microsoft基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Phi-4-reasoning-plus
Microsoft> [!IMPORTANT] > To fully take advantage of the model's capabilities, inference must use `temperature=0.8`, `top_k=50`, `top_p=0.95`, and `do_sample=True`. For more complex queries, set `max_new_tokens=32768` to allow for longer chain-of-thought (CoT). *Phi-4-reasoning-plus has shown strong performance on reasoning-intensive tasks. In our experiments, we extended its maximum number of tokens to 64k, and it handled longer sequences with promising results, maintaining coherence and logical consistency over extended inputs. This makes it a compelling option to explore for tasks that require deep, multi-step reasoning or extensive context.* Given the nature of the training data, **always use** ChatML template with the **following system prompt** for inference:
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Phi-4-reasoning-plus-onnx
Microsoft基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Phi-4-reasoning-vision-15B
Microsoft**Developer:** Microsoft Corporation **Authorized Representative:** Microsoft Ireland Operations Limited, 70 Sir John Rogerson's Quay, Dublin 2, D02 R296, Ireland **Release Date:** March 4, 2026 **License:** MIT **Parameters:** 15B **Context Length:** 16,384 tokens **Inputs:** Text and Images **Outputs:** Text **Training GPUs:** 240 B200s **Training Time:** 4 days **Training Dates:** February 3, 2025 – February 21, 2026 **Model Dependencies:** Phi-4-Reasoning Phi-4-Reasoning-Vision-15B is a compact open-weight multimodal reasoning model built on the Phi-4-Reasoning language model backbone and the SigLIP-2 vision encoder, using a mid-fusion architecture. In this architecture, the vision encoder first converts images into visual tokens, which are then projected into the language model's embedding space and injected into the pretrained language model. This approach leverages the strengths of both pretrained components while keeping training and inference costs manageable. The model employs a dynamic resolution vision encoder with up to 3,600 visual tokens, enabling high-resolution image understanding critical for tasks such as GUI grounding and fine-grained document analysis. Bidirectional attention is applied within images (intra-image) to improve spatial reasoning without the overfitting risks observed with broader bidirectional schemes. Phi-4-Reasoning-Vision-15B is trained with Supervised Fine-Tuning (SFT) on a carefully curated mixture of reasoning and non-reasoning data. Rather than training separate models for each mode, the model operates as a single system that can invoke extended chain-of-thought reasoning (using ` ... ` blocks) for tasks like mathematical and scientific reasoning, or default to direct inference (tagged with ` `) for perception-focused tasks such as captioning, object detection, and grounding. The training data consists primarily of meticulously filtered and improved open-source vision-language datasets, supplemented by high-quality domain-specific data from internal Microsoft teams and targeted data acquisitions. This data-centric approach, combined with moderate training compute requirements (240 NVIDIA B200 GPUs for 4 days), distinguis
Input Text · Image→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Phi-Ground
Microsoft基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗No matching models
Adjust provider, category or input/output filters. New models are added continuously.
COST EXPLORER
Great models. Clear costs.
Adjust monthly token usage to compare estimated API costs.
1 M = 1 million tokens · USD
Compares base text-token rates within the same channel
Compares base text-token rates within the same channel
Uses collected, comparable USD-per-million-token rates. Snapshots exclude cache discounts, tiers, extra reasoning output and taxes. Check current source prices.
MODEL HANDBOOK
Understand before you choose
How should API prices be compared?+
Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.
Do open weights mean free commercial use?+
Not necessarily. Check each version’s license, commercial restrictions and policies. Self-hosting also incurs compute and maintenance costs.
Is the top-ranked model the best for me?+
Rankings depend on tasks, versions and methods. Compare accuracy, latency, reliability and costs on your own workload.