Phi-3-small-8k-instruct-onnx-cuda
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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
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查看详情 ↗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...
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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.).
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查看详情 ↗Phi-4 Mini Instruct
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查看详情 ↗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
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查看详情 ↗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]
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查看详情 ↗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
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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.*
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查看详情 ↗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:
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查看详情 ↗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
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查看详情 ↗暂未找到匹配模型
可以调整厂商、分类或输入输出条件；新模型正持续收录。
COST EXPLORER
好模型，也要算好成本。
调整月度 Token 用量，直观比较不同模型的预估 API 开支。
1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价
仅比较同一渠道的基础文本 Token 报价
使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。
MODEL HANDBOOK
读懂模型，再做选择
API 价格应该怎么比较？+
先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。
开放权重，就代表可以免费商用吗？+
不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。
排行榜第一，就是最适合我的模型吗？+
榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。