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查看详情 ↗DISCOVER & COMPARE
探索 AI 模型 84999
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查看详情 ↗> **Update:** > This model has been **further enhanced with additional reasoning data distilled from Qwen3.5-27B**. > > The new training data introduces higher-quality reasoning trajectories across domains such as **science, instruction-following, and mathematics**. > > Part of the data comes from **Jackrong/Qwen3.5-reasoning-700x**, a curated dataset designed to improve **structured step-by-step reasoning** and **reasoning diversity**. **Qwen3.5-2B-Claude-4.6-Opus-Reasoning-Distilled** is a highly capable reasoning model fine-tuned on top of the Qwen3.5-2B dense architecture. The model's core directive is to leverage state-of-the-art Chain-of-Thought (CoT) distillation primarily sourced from Claude-4.6 Opus interactions. Through Supervised Fine-Tuning (SFT) focusing specifically on structured reasoning logic, this model excels in breaking down complex user problems, planning step-by-step methodologies within strictly formatted ` ` tags, and ultimately delivering precise, nuanced solutions.
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查看详情 ↗🔥 **Update (April 5):** I’ve released the complete training notebook, codebase, and a comprehensive PDF guide to help beginners and enthusiasts understand and reproduce this model's fine-tuning process. > ❤️ Special thanks to the **Unsloth** open-source library and @KyleHessling1 for their support. 👉 **GitHub Repository: Jackrong-llm-finetuning-guide** Visit the repo to dive into the codebase and reproduce the results locally or on Colab.
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查看详情 ↗🔥 **Update (April 5):** I’ve released the complete training notebook, codebase, and a comprehensive PDF guide to help beginners and enthusiasts understand and reproduce this model's fine-tuning process. 👉 **GitHub Repository: Jackrong-llm-finetuning-guide** Visit the repo to dive into the codebase and reproduce the results locally or on Colab. **🔗 Qwopus3.5-27b Complete Fine-Tuning Guide (PDF)** * **The Full Pipeline:** A step-by-step walkthrough—from downloading the base model and unifying heterogeneous data, to configuring trainer hyperparameters and publishing to Hugging Face. * **Beginner Friendly:** Includes an introductory guide to getting started with Google Colab and Unsloth. * *Feedback welcome! If you spot any areas for improvement, please let me know and I will update it promptly.*
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查看详情 ↗Qwen3.5-4B-Neo-GGUF
Jackrong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。
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查看详情 ↗🔥 **Update (April 5):** I’ve released the complete training notebook, codebase, and a comprehensive PDF guide to help beginners and enthusiasts understand and reproduce this model's fine-tuning process. 👉 **GitHub Repository: Jackrong-llm-finetuning-guide** Visit the repo to dive into the codebase and reproduce the results locally or on Colab. **🔗 Qwopus3.5-27b Complete Fine-Tuning Guide (PDF)** * **The Full Pipeline:** A step-by-step walkthrough—from downloading the base model and unifying heterogeneous data, to configuring trainer hyperparameters and publishing to Hugging Face. * **Beginner Friendly:** Includes an introductory guide to getting started with Google Colab and Unsloth. * *Feedback welcome! If you spot any areas for improvement, please let me know and I will update it promptly.*
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查看详情 ↗🔥 **Update (April 5):** I’ve released the complete training notebook, codebase, and a comprehensive PDF guide to help beginners and enthusiasts understand and reproduce this model's fine-tuning process. > ❤️ Special thanks to the **Unsloth** open-source library and @KyleHessling1 for their support. 👉 **GitHub Repository: Jackrong-llm-finetuning-guide** Visit the repo to dive into the codebase and reproduce the results locally or on Colab.
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查看详情 ↗🔥 **Update (April 5):** I’ve released the complete training notebook, codebase, and a comprehensive PDF guide to help beginners and enthusiasts understand and reproduce this model's fine-tuning process. > ❤️ Special thanks to the **Unsloth** open-source library and @KyleHessling1 for their support. 👉 **GitHub Repository: Jackrong-llm-finetuning-guide** Visit the repo to dive into the codebase and reproduce the results locally or on Colab.
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查看详情 ↗Qwen3.5-9B-DeepSeek-V4-Flash
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查看详情 ↗> [!NOTE] > **Qwen3.5-9B-DeepSeek-V4-Flash** is an efficient reasoning model distilled using high-quality data from **DeepSeek-V4**. - By leveraging the dataset **Jackrong/DeepSeek-V4-Distill-8000x**, this model successfully transfers the advanced structured reasoning and multi-step problem-solving capabilities of the DeepSeek-V4 architecture into the highly efficient **Qwen3.5-9B** parameter space. - This model was trained in an **Unsloth** environment, prioritizing stable gradient propagation and rigorous data curation to ensure the distillation process avoids merely learning "hollow chain-of-thought" and instead captures genuine logical generalization.
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查看详情 ↗**Qwen3.5-9B-Gemini-3.1-Pro-Reasoning-Distill** is a reasoning model fine-tuned on top of **Qwen3.5-9B**. The model is primarily optimized through high-density reasoning distillation sourced from **Gemini 3.1**, while also incorporating additional reasoning traces distilled from **Qwen3.5-27B** and a broader **Gemini 3.0 Pro** reasoning corpus. Through Supervised Fine-Tuning focused on structured analytical behavior, this model aims to reshape the base model’s reasoning style into a more coherent, better-organized, and higher-density Chain-of-Thought (CoT) pattern. It is especially designed to improve decomposition, planning, abstraction, and response cleanliness on complex multi-step tasks. This model inherits a more structured reasoning style influenced by **Gemini 3.1-style analytical planning**. Compared with more loosely exploratory reasoning patterns, this model tends to organize the problem before answering:
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查看详情 ↗Qwen3.5-9B-GLM5.1-Distill-v1
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查看详情 ↗**Model Name:** `Jackrong/Qwen3.5-9B-GLM5.1-Distill-v1` **Base Model:** Qwen3.5-9B **Training Type:** Supervised Fine-Tuning (SFT, Distillation) **Parameter Scale:** 9B **Training Framework:** Unsloth This model is a distilled variant of **Qwen3.5-9B**, trained on high-quality reasoning data derived from **GLM-5.1**. > [!IMPORTANT] > Training used **`Jackrong/GLM-5.1-Reasoning-1M-Cleaned`**, a cleaned derivative of **`Kassadin88/GLM-5.1-1000000x`**. Special thanks to **Kassadin88 ❤️** for the original dataset. Please support the original author with a follow and a like. > Only a **quality-filtered subset** was used for distillation, rather than the full original dataset.
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查看详情 ↗Qwen3.5-9B-Neo-GGUF
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查看详情 ↗Qwen3.6-27B-GGUF
Jackrong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。
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查看详情 ↗Qwen3.8-27B-MTP-GGUF
JackrongThis release bundles the MTP head directly into the model, enabling speculative decoding without loading a separate draft model. > 📌 Results are from an internal benchmark using identical runtime, GPU, > Flash Attention, and sampling settings. Actual performance may vary > depending on prompt length, reasoning depth, output length, and hardware. > [!Note] > This repository contains model weights and configuration files for the post-trained model in the Hugging Face Transformers format. > > These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, TokenSpeed, etc.
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查看详情 ↗Qwopus-GLM-18B-Merged-GGUF
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查看详情 ↗Qwopus3.5-27B-v3-GGUF
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价格待核验多模态与渠道费用须分别核对
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。