从语言到影像，从开源到 API。读懂模型能力、比较真实成本，
为你的下一个想法，找到合适的智能引擎。
Curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations My appreciation for the sponsors of Dolphin 2.9: - Crusoe Cloud - provided excellent on-demand 10xL40S node Dolphin-2.9 has a variety of instruction, conversational, and coding skills. It also has initial agentic abilities and supports function calling.
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Website: https://dphn.ai Twitter: https://x.com/dphnAI Web Chat: https://chat.dphn.ai Telegram bot: https://t.me/DolphinAI_bot Dolphin Mistral 24B Venice Edition is a collaborative project we undertook with Venice.ai with the goal of creating the most uncensored version of Mistral 24B for use within the Venice ecosystem. Dolphin Mistral 24B Venice Edition is now live on https://venice.ai/ as “Venice Uncensored,” the new default model for all Venice users.
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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。
Dolphin X1 8B is a result of our effort to directly uncensor Llama's 3.1 8B instruct while also keeping the same abilities or improving on them with finetuning. Dolphin aims to be a general purpose model, similar to the models behind ChatGPT, Claude, Gemini. But these models present problems for businesses seeking to include AI in their products. 1) They maintain control of the system prompt, deprecating and changing things as they wish, often causing software to break. 2) They maintain control of the model versions, sometimes changing things silently, or deprecating older models that your business relies on. 3) They maintain control of the alignment, and in particular the alignment is one-size-fits all, not tailored to the application. 4) They can see all your queries and they can potentially use that data in ways you wouldn't want. Dolphin, in contrast, is steerable and gives control to the system owner. You set the system prompt. You decide the alignment. You have control of your data. Dolphin does not impose its ethics or guidelines on you. You are the one who decides the guidelines. Dolphin belongs to YOU, it is your tool, an extension of your will. Just as you are personally responsible for what you do with a knife, gun, fire, car, or the internet, you are the creator and originator of any content you generate with Dolphin.
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Dolphin X1 Trinity Nano directly decensors Arcee AI's Trinity Nano model while maintaining instruction-following capabilities. This model is the first trained entirely using our new RL environments aimed to de-align models and achieves lower refusal rates than other models in its class without compromising performance. Dolphin aims to be a general purpose model, similar to the models behind ChatGPT, Claude, Gemini. But these models present problems for businesses seeking to include AI in their products. 1) They maintain control of the system prompt, deprecating and changing things as they wish, often causing software to break. 2) They maintain control of the model versions, sometimes changing things silently, or deprecating older models that your business relies on. 3) They maintain control of the alignment, and in particular the alignment is one-size-fits all, not tailored to the application. 4) They can see all your queries and they can potentially use that data in ways you wouldn't want. Dolphin, in contrast, is steerable and gives control to the system owner. You set the system prompt. You decide the alignment. You have control of your data. Dolphin does not impose its ethics or guidelines on you. You are the one who decides the guidelines.
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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。
基础资料已收录 · 缺失资料后续补充
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。
Part of the Dolphin 3.0 Collection Curated and trained by Eric Hartford, Ben Gitter, BlouseJury and Cognitive Computations Our appreciation for the generous sponsors of Dolphin 3.0: - Crusoe Cloud - provided 16x L40s for training and evals - Akash - provided on-demand 8x H100 for training - Lazarus - provided 16x H100 for training - Cerebras - provided excellent and fast inference services for data labeling - Andreessen Horowitz - provided a grant that make Dolphin 1.0 possible and enabled me to bootstrap my homelab
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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。
基础资料已收录 · 缺失资料后续补充
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。
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。
基础资料已收录 · 缺失资料后续补充
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。
基础资料已收录 · 缺失资料后续补充
可以调整厂商、分类或输入输出条件；新模型正持续收录。
调整月度 Token 用量，直观比较不同模型的预估 API 开支。
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。
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。
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。
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。
