ESMC-600M-step1500k
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查看详情 ↗DISCOVER & COMPARE
探索 AI 模型 84991
ESMC-600M-step250k
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查看详情 ↗ESMC-600M-step500k
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查看详情 ↗ESMC-600M-step750k
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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 ↗ESMC-6B
biohubESMC is a state-of-the-art protein language model that has learned the rules of protein biology from training on billions of protein sequences. ESMC provides representations of proteins enabling novel AI applications from therapeutic protein engineering to unlocking basic insights into protein biology across life. The ESMC 6B model has 6 billion parameters, with 80 layers and 2.37e23 training flops. We additionally release overtrained 300M and 600M parameter variants of ESMC for local inference and finetuning. The ESMFold2 structure prediction models are trained on top of a frozen ESMC 6B language model. ESMFold2 is a state-of-the-art model for protein structure prediction and design that defines a new frontier for speed and accuracy.
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查看详情 ↗esmc-6b-2024-12
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查看详情 ↗ESMC-6B-hf
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查看详情 ↗ESMC-6B-step1000k
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查看详情 ↗ESMC-6B-step250k
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查看详情 ↗ESMC-6B-step500k
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查看详情 ↗ESMC-6B-step750k
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查看详情 ↗ESMFold2
biohubESMFold2 is a state-of-the-art model for protein structure prediction and design that defines a new frontier for speed and accuracy. The model predicts high-resolution, all-atom 3D protein structures directly from amino acid sequences, with optional multiple sequence alignment (MSA) input for enhanced accuracy on challenging targets. The model outputs comprehensive structural information including all-atom coordinates (backbone and side chains), confidence metrics (pLDDT, pAE, pTM, iPTM), and optional distogram predictions for detailed analysis of predicted structures. Unlike ESMFold, ESMFold2 is able to predict structures for all biomolecules, including small molecules, DNA, RNA, and modified amino acids. ESMFold2 is capable of either single-sequence or MSA conditioned structure prediction for improved accuracy on difficult targets. The ESMFold2-Fast variant is an inference optimized single-sequence structure prediction model and is not MSA conditioned. To run this model with the Biohub Platform API, visit the Biohub Platform.
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查看详情 ↗ESMFold2-Experimental
biohubThe ESMFold2 Experimental Models are being released to support reproducibility of the findings in our paper, please refer to the paper and github for details. Please use ESMFold2 model for research work. ESMFold2 predicts high-resolution, all-atom 3D protein structures from either single-sequence or MSA conditioned structure prediction for improved accuracy on difficult targets.
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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 ↗The ESMFold2 Experimental Models are being released to support reproducibility of the findings in our paper, please refer to the paper and github for details. Please use ESMFold2 model for research work. ESMFold2 predicts high-resolution, all-atom 3D protein structures from either single-sequence or MSA conditioned structure prediction for improved accuracy on difficult targets.
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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 ↗The ESMFold2 Experimental Models are being released to support reproducibility of the findings in our paper, please refer to the paper and github for details. Please use ESMFold2 model for research work. ESMFold2 predicts high-resolution, all-atom 3D protein structures from either single-sequence or MSA conditioned structure prediction for improved accuracy on difficult targets.
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价格待核验多模态与渠道费用须分别核对
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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 ↗The ESMFold2 Experimental Models are being released to support reproducibility of the findings in our paper, please refer to the paper and github for details. Please use ESMFold2 model for research work. ESMFold2 predicts high-resolution, all-atom 3D protein structures from either single-sequence or MSA conditioned structure prediction for improved accuracy on difficult targets.
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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 ↗ESMFold2-Fast
biohubESMFold2 is a state-of-the-art model for protein structure prediction and design that defines a new frontier for speed and accuracy. The model predicts high-resolution, all-atom 3D protein structures directly from amino acid sequences, with optional multiple sequence alignment (MSA) input for enhanced accuracy on challenging targets. The model outputs comprehensive structural information including all-atom coordinates (backbone and side chains), confidence metrics (pLDDT, pAE, pTM, iPTM), and optional distogram predictions for detailed analysis of predicted structures. Unlike ESMFold, ESMFold2 is able to predict structures for all biomolecules, including small molecules, DNA, RNA, and modified amino acids. ESMFold2 is capable of either single-sequence or MSA conditioned structure prediction for improved accuracy on difficult targets. The ESMFold2-Fast variant is an inference optimized single-sequence structure prediction model and is not MSA conditioned. To run this model with the Biohub Platform API, visit the Biohub Platform.
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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 ↗Qwen3.5-4B-heretic-gguf
Biomanticus> [!TIP] > **This model is reproducible!** > > See the README in the `reproduce` directory for more information. GPT-OSS-120B GPT-OSS-20B Qwen3-Next-80B-A3B-Thinking Qwen3-30BA3B-Thinking-2507 Qwen3.5-9B Qwen3.5-4B > [!Important] > Qwen3.5 models operate in thinking mode by default, generating thinking content signified by ` \n... \n\n` before producing the final responses. > To disable thinking content and obtain direct response, refer to the examples here. For streamlined integration, we recommend using Qwen3.5 via APIs. Below is a guide to use Qwen3.5 via OpenAI-compatible API. Qwen3.5 can be served via APIs with popular inference frameworks. In the following, we show example commands to launch OpenAI-Compatible API servers for Qwen3.5 models. > [!Important] > Inference efficiency and throughput vary significantly across frameworks. > We recommend using the latest framework versions to ensure optimal performance and compatibility. > For production workloads or high-throughput scenarios, dedicated serving engines such as SGLang, KTransformers or vLLM are strongly recommended. > [!Important] > The model has a default context length of 262,144 tokens. > If you encounter out-of-memory (OOM) errors, consider reducing the context window. > However, because Qwen3.5 leverages extended context for complex tasks, we advise maintaining a context length of at least 128K tokens to preserve thinking capabilities. SGLang is a fast serving framework for large language models and vision language models. SGLang from the main branch of the open-source repository is required for Qwen3.5, which can be installed using the following command in a fresh environment:
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查看详情 ↗proteinglm-1b-mlm
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查看详情 ↗BioMistral-7B
BioMistralLarge Language Models (LLMs) have demonstrated remarkable versatility in recent years, offering potential applications across specialized domains such as healthcare and medicine. Despite the availability of various open-source LLMs tailored for health contexts, adapting general-purpose LLMs to the medical domain presents significant challenges. In this paper, we introduce BioMistral, an open-source LLM tailored for the biomedical domain, utilizing Mistral as its foundation model and further pre-trained on PubMed Central. We conduct a comprehensive evaluation of BioMistral on a benchmark comprising 10 established medical question-answering (QA) tasks in English. We also explore lightweight models obtained through quantization and model merging approaches. Our results demonstrate BioMistral's superior performance compared to existing open-source medical models and its competitive edge against proprietary counterparts. Finally, to address the limited availability of data beyond English and to assess the multilingual generalization of medical LLMs, we automatically translated and evaluated this benchmark into 7 other languages. This marks the first large-scale multilingual evaluation of LLMs in the medical domain. Datasets, multilingual evaluation benchmarks, scripts, and all the models obtained during our experiments are freely released. **Advisory Notice!** Although BioMistral is intended to encapsulate medical knowledge sourced from high-quality evidence, it hasn't been tailored to effectively, safely, or suitably convey this knowledge within professional parameters for action. We advise refraining from utilizing BioMistral in medical contexts unless it undergoes thorough alignment with specific use cases and undergoes further testing, notably including randomized controlled trials in real-world medical environments. BioMistral 7B may possess inherent risks and biases that have not yet been thoroughly assessed. Additionally, the model's performance has not been evaluated in real-world clinical settings. Consequently, we recommend using BioMistral 7B strictly as a research tool and advise against deploying it in production environments for natural language generat
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查看详情 ↗BioMistral-7B-DARE
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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。