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84,991 款模型档案13202 家厂商 / 发布者45 个能力分类465 款有可比报价 · 30 款有公开评测
T文本生成▧图像生成▷视频生成⌘代码生成◖语音合成♫音乐生成⬡3D 建模▤文档分析
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探索 AI 模型 84991

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**Model details:** - Parameter count: 9B (source checkpoint) - Input support: text, image (with mmproj file) - details - Decision model: yes (clef) - details - Speculative decoding: no - imatrix: no (not possible for a decision model) - details This is a decision model: it reads a state (text or JSON) together with a schema of typed questions, and returns a probability for every allowed option of every question in a single forward pass. It does not generate text. There is no chat, no completion endpoint and no prompt format to follow. llama.cpp serves it through the `/v1/systemone` endpoint of `llama-server` (the TypeSafe System One API); the request and response shapes are documented in the llama-server README. For example:

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**Model details:** - Parameter count: 27B (source checkpoint) - Input support: text, image (with mmproj file) - details - Decision model: yes (clef) - details - Speculative decoding: no - imatrix: no (not possible for a decision model) - details This is a decision model, not a text generator: it reads a state (text or JSON) together with a schema of typed questions and hands back a probability for every allowed option of every question, all in a single forward pass. It never generates text, so there's no chat, no completion endpoint, and no prompt format to worry about. llama.cpp serves it through llama-server's `/v1/systemone` endpoint (the TypeSafe System One API). The full request and response shapes are in the llama-server README, but here's what a request looks like:

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If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run: The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have. If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.

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文本生成
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Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to. If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run: You can either specify a new local-dir (cognitivecomputations_Dolphin3.0-R1-Mistral-24B-Q8_0) or download them all in place (./)

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COST EXPLORER

好模型，也要算好成本。

费用分析

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

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

选型与常见问题
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。

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