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查看详情 ↗DISCOVER & COMPARE
探索 AI 模型 20
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查看详情 ↗ERNIE-4.5-0.3B-PT
baidu> [!NOTE] > Note: "**-Paddle**" models use PaddlePaddle weights, while "**-PT**" models use Transformer-style PyTorch weights. The advanced capabilities of the ERNIE 4.5 models, particularly the MoE-based A47B and A3B series, are underpinned by several key technical innovations: 1. **Multimodal Heterogeneous MoE Pre-Training:** Our models are jointly trained on both textual and visual modalities to better capture the nuances of multimodal information and improve performance on tasks involving text understanding and generation, image understanding, and cross-modal reasoning. To achieve this without one modality hindering the learning of another, we designed a *heterogeneous MoE structure*, incorporated *modality-isolated routing*, and employed *router orthogonal loss* and *multimodal token-balanced loss*. These architectural choices ensure that both modalities are effectively represented, allowing for mutual reinforcement during training.
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查看详情 ↗ERNIE-4.5-21B-A3B-PT
baidu> [!NOTE] > Note: "**-Paddle**" models use PaddlePaddle weights, while "**-PT**" models use Transformer-style PyTorch weights. The advanced capabilities of the ERNIE 4.5 models, particularly the MoE-based A47B and A3B series, are underpinned by several key technical innovations: 1. **Multimodal Heterogeneous MoE Pre-Training:** Our models are jointly trained on both textual and visual modalities to better capture the nuances of multimodal information and improve performance on tasks involving text understanding and generation, image understanding, and cross-modal reasoning. To achieve this without one modality hindering the learning of another, we designed a *heterogeneous MoE structure*, incorporated *modality-isolated routing*, and employed *router orthogonal loss* and *multimodal token-balanced loss*. These architectural choices ensure that both modalities are effectively represented, allowing for mutual reinforcement during training.
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查看详情 ↗Over the past three months, we have continued to scale the **thinking capability** of ERNIE-4.5-21B-A3B, improving both the **quality and depth** of reasoning, thereby advancing the competitiveness of ERNIE **lightweight models** in complex reasoning tasks. We are pleased to introduce **ERNIE-4.5-21B-A3B-Thinking**, featuring the following key enhancements: * **Significantly improved performance** on reasoning tasks, including logical reasoning, mathematics, science, coding, text generation, and academic benchmarks that typically require human expertise. * **Efficient tool usage** capabilities. * **Enhanced 128K long-context understanding** capabilities. > [!NOTE] > Note: This version has an increased thinking length. We strongly recommend its use in highly complex reasoning tasks.
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查看详情 ↗> [!NOTE] > Note: "**-Paddle**" models use PaddlePaddle weights, while "**-PT**" models use Transformer-style PyTorch weights. The advanced capabilities of the ERNIE 4.5 models, particularly the MoE-based A47B and A3B series, are underpinned by several key technical innovations: 1. **Multimodal Heterogeneous MoE Pre-Training:** Our models are jointly trained on both textual and visual modalities to better capture the nuances of multimodal information and improve performance on tasks involving text understanding and generation, image understanding, and cross-modal reasoning. To achieve this without one modality hindering the learning of another, we designed a *heterogeneous MoE structure*, incorporated *modality-isolated routing*, and employed *router orthogonal loss* and *multimodal token-balanced loss*. These architectural choices ensure that both modalities are effectively represented, allowing for mutual reinforcement during training.
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查看详情 ↗ERNIE-Image
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查看详情 ↗ERNIE-Image-Aes
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查看详情 ↗ERNIE-Image-Turbo
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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 ↗Qianfan-OCR
baidu**Qianfan-OCR** is a **4B-parameter end-to-end document intelligence model** developed by the Baidu Qianfan Team. It unifies document parsing, layout analysis, and document understanding within a single vision-language architecture. Unlike traditional multi-stage OCR pipelines that chain separate layout detection, text recognition, and language comprehension modules, Qianfan-OCR performs **direct image-to-Markdown conversion** and supports a broad range of prompt-driven tasks — from structured document parsing and table extraction to chart understanding, document question answering, and key information extraction — all within one model. - 🏆 **#1 End-to-End Model on OmniDocBench v1.5** — Achieves **93.12** overall score, surpassing DeepSeek-OCR-v2 (91.09), Gemini-3 Pro (90.33), and all other end-to-end models - 🏆 **#1 End-to-End Model on OlmOCR Bench** — Scores **79.8** - 🏆 **#1 on Key Information Extraction** — Overall mean score of **87.9** across five public KIE benchmarks, surpassing Gemini-3.1-Pro, Gemini-3-Pro, Seed-2.0, and Qwen3-VL-235B-A22B - 🧠 **Layout-as-Thought** — An innovative optional thinking phase that recovers explicit layout analysis within the end-to-end paradigm via `⟨think⟩` tokens - 🌍 **192 Languages** — Multilingual OCR support across diverse scripts - ⚡ **Efficient Deployment** — Achieves **1.024 PPS** (pages per second) with W8A8 quantization on a single A100 GPU
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查看详情 ↗Unlimited-OCR
baidu- [2026/07/21] 🤝 Thanks to the ms-swift community for their support, our model now supports training with ms-swift. - [2026/07/03] 🤝 Thanks to the Baidu Cloud team for their support. Our model is now available on Baidu Cloud. - [2026/06/28] 🤝 Thanks to the vLLM community and Tianyu Guo for their support, our model now supports vLLM inference. - [2026/06/24] 🤝 Thanks to AK for creating a demo for us. It is now available at Hugging Face Spaces. - [2026/06/23] 📄 Our paper is now available on arXiv. - [2026/06/23] 🤝 Thanks to the ModelScope community for their support. Our model is now available at ModelScope. - [2026/06/22] 🚀 We present Unlimited-OCR, aiming to push Deepseek-OCR one step further. Inference using Huggingface transformers on NVIDIA GPUs. Requirements tested on python 3.12.3 + CUDA12.9： **Recipe:** https://recipes.vllm.ai/baidu/Unlimited-OCR
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查看详情 ↗Unlimited-OCR-2
baidu> Unlimited OCR 2 supports **Transformers** (native, for quick tests) and **SGLang** (recommended for serving). Tested with Python 3.10, `torch==2.13.0`, `transformers==4.52.1`, `huggingface-hub==0.36.0`, `tokenizers==0.21.4`, on an NVIDIA A800-80GB: **Multi-page inference** (`infer_multi`). All pages of a document are parsed in one request, so the model can use cross-page context. Pass the page images in page order:
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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。