o1-pro-all
OpenAI官方 Chatgpt 的最高级推理模型 o1-pro 模型
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View details ↗DISCOVER & COMPARE
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o3-mini-all
OpenAI官方 Chatgpt 的高级推理模型 o3-mini 模型
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View details ↗o3-mini-high-all
OpenAI官方 Chatgpt 的高级推理模型 o3-mini-high 模型
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View details ↗search-gpts
OpenAI搜索gpts接口，返回json原始数据 点击查看接入文档 （模型名称：search-gpts）
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View details ↗search-gpts-chat
OpenAI聊天格式的 gpts 搜索接口，返回 Markdown 排版后的数据。
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View details ↗privacy-filter
OpenAIOpenAI Privacy Filter is a bidirectional token-classification model for personally identifiable information (PII) detection and masking in text. It is intended for high-throughput data sanitization workflows where teams need a model that they can run on-premises that is fast, context-aware, and tunable. OpenAI Privacy Filter is pretrained autoregressively to arrive at a checkpoint with similar architecture to gpt-oss, albeit of a smaller size. We then converted that checkpoint into a bidirectional token classifier over a privacy label taxonomy, and post-trained with a supervised classification loss. (For architecture details about gpt-oss, please see the gpt-oss model card.) Instead of generating text token-by-token, this model labels an input sequence in a single forward pass, then decodes coherent spans with a constrained Viterbi procedure. For each input token, the model predicts a probability distribution over the label taxonomy which consists of 8 output categories described below. - Permissive Apache 2.0 license: ideal for experimentation, customization, and commercial deployment. - Small size: Runs in a web browser or on a laptop – 1.5B parameters total and 50M active parameters. - Fine-tunable: Adapt the model to specific data distributions through easy and data efficient finetuning. - Long-context: 128,000-token context window enables processing long text with high throughput and no chunking. - Runtime control: configure precision/recall tradeoffs and detected span lengths through preset operating points.
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View details ↗shap-e
OpenAIShap-E introduces a diffusion process that can generate a 3D image from a text prompt. It was introduced in Shap-E: Generating Conditional 3D Implicit Functions by Heewoo Jun and Alex Nichol from OpenAI. *We present Shap-E, a conditional generative model for 3D assets. Unlike recent work on 3D generative models which produce a single output representation, Shap-E directly generates the parameters of implicit functions that can be rendered as both textured meshes and neural radiance fields. We train Shap-E in two stages: first, we train an encoder that deterministically maps 3D assets into the parameters of an implicit function; second, we train a conditional diffusion model on outputs of the encoder. When trained on a large dataset of paired 3D and text data, our resulting models are capable of generating complex and diverse 3D assets in a matter of seconds. When compared to Point-E, an explicit generative model over point clouds, Shap-E converges faster and reaches comparable or better sample quality despite modeling a higher-dimensional, multi-representation output space. We release model weights, inference code, and samples at this https URL.*
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View details ↗shap-e-img2img
OpenAIShap-E introduces a diffusion process that can generate a 3D image from a text prompt. It was introduced in Shap-E: Generating Conditional 3D Implicit Functions by Heewoo Jun and Alex Nichol from OpenAI. *We present Shap-E, a conditional generative model for 3D assets. Unlike recent work on 3D generative models which produce a single output representation, Shap-E directly generates the parameters of implicit functions that can be rendered as both textured meshes and neural radiance fields. We train Shap-E in two stages: first, we train an encoder that deterministically maps 3D assets into the parameters of an implicit function; second, we train a conditional diffusion model on outputs of the encoder. When trained on a large dataset of paired 3D and text data, our resulting models are capable of generating complex and diverse 3D assets in a matter of seconds. When compared to Point-E, an explicit generative model over point clouds, Shap-E converges faster and reaches comparable or better sample quality despite modeling a higher-dimensional, multi-representation output space. We release model weights, inference code, and samples at this https URL.*
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View details ↗sora-2
OpenAIOpenAI 最新发布的 Sora 2 视频模型，比以前的系统在物理上更准确、更逼真、更可控。它还具有同步对话和音效功能 支持对话、异步任务格式，支持设置参考图； 如果使用 Chat 格式，在提示词中添加要求即可，如提示词以(竖屏、横屏,15s,高清）开头 文档：https://gpt-best.apifox.cn/api-358024351 default 分组是逆向，是按次计费；支持秒数 10、15，支持角色客串功能 openai优质、origin 分组是官方api，是按秒计费；支持秒数 4、8、12
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View details ↗sora-2-pro
OpenAISora 2 pro，支持 HD default 分组是逆向，是按次计费；支持秒数 15、25，支持角色客串功能 openai优质、origin 分组是官方api，是按秒计费；支持秒数 4、8、12
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View details ↗text-ada-001
OpenAI-
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View details ↗text-babbage-001
OpenAI-
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View details ↗text-curie-001
OpenAI-
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View details ↗text-davinci-edit-001
OpenAI-
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View details ↗text-embedding-3-large
OpenAI-
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View details ↗text-embedding-3-small
OpenAI-
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View details ↗text-embedding-ada-002
OpenAI-
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View details ↗text-moderation-latest
OpenAI-
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View details ↗text-search-ada-doc-001
OpenAI-
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View details ↗tts-1
OpenAI文本转语音模型TTS，支持设置音色，标准 tts-1 模型提供最低的延迟，但质量低于 tts-1-hd 模型 点击查看接入文档
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View details ↗tts-1-1106
OpenAI文本转语音模型TTS，支持设置音色，标准 tts-1 模型提供最低的延迟，但质量低于 tts-1-hd 模型。 点击查看接入文档
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View details ↗tts-1-hd
OpenAI高清文本转语音模型 TTS，支持高质量音色设置。
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View details ↗tts-1-hd-1106
OpenAI高清文本转语音模型 TTS，支持高质量音色设置。
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View details ↗whisper-1
OpenAIWhisper can transcribe speech to text and translate multiple languages into English.
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