Llama-2-70b-instruct
upstage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。
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查看详情 ↗DISCOVER & COMPARE
探索 AI 模型 13
llama-30b-instruct-2048
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查看详情 ↗llama-65b-instruct
upstage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。
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查看详情 ↗SOLAR-0-70b-16bit
upstageSolar, a new bot created by Upstage, is now available on **Poe**. As a top-ranked model on the HuggingFace Open LLM leaderboard, and a fine tune of Llama 2, Solar is a great example of the progress enabled by open source. Try now at https://poe.com/Solar-0-70b
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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 ↗SOLAR-10.7B-Instruct-v1.0
upstage**(This model is upstage/SOLAR-10.7B-v1.0 fine-tuned version for single-turn conversation.)** We introduce SOLAR-10.7B, an advanced large language model (LLM) with 10.7 billion parameters, demonstrating superior performance in various natural language processing (NLP) tasks. It's compact, yet remarkably powerful, and demonstrates unparalleled state-of-the-art performance in models with parameters under 30B. We present a methodology for scaling LLMs called depth up-scaling (DUS) , which encompasses architectural modifications and continued pretraining. In other words, we integrated Mistral 7B weights into the upscaled layers, and finally, continued pre-training for the entire model.
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查看详情 ↗SOLAR-10.7B-v1.0
upstageWe introduce SOLAR-10.7B, an advanced large language model (LLM) with 10.7 billion parameters, demonstrating superior performance in various natural language processing (NLP) tasks. It's compact, yet remarkably powerful, and demonstrates unparalleled state-of-the-art performance in models with parameters under 30B. We present a methodology for scaling LLMs called depth up-scaling (DUS) , which encompasses architectural modifications and continued pretraining. In other words, we integrated Mistral 7B weights into the upscaled layers, and finally, continued pre-training for the entire model. SOLAR-10.7B has remarkable performance. It outperforms models with up to 30B parameters, even surpassing the recent Mixtral 8X7B model. For detailed information, please refer to the experimental table. Solar 10.7B is an ideal choice for fine-tuning. SOLAR-10.7B offers robustness and adaptability for your fine-tuning needs. Our simple instruction fine-tuning using the SOLAR-10.7B pre-trained model yields significant performance improvements (SOLAR-10.7B-Instruct-v1.0).
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查看详情 ↗Solar Mini 4
upstageSolar Mini 4 是 Upstage 推出的一款紧凑且经济高效的语言模型，拥有 350 亿参数的专家混合模型，其中活跃参数为 30 亿，上下文窗口长达 52.4 万 token。它专为代理型应用场景而设计，在这些场景中，响应……
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$0.05 / M 输入 TokenOpenRouter · upstage/solar-mini4
查看详情 ↗Solar-Open-100B
upstage**Solar Open** is Upstage's flagship **102B-parameter** large language model, trained **entirely from scratch** and released under the **Upstage Solar License** (see LICENSE for details). As a **Mixture-of-Experts (MoE)** architecture, it delivers enterprise-grade performance in reasoning, instruction-following, and agentic capabilities—all while prioritizing transparency and customization for the open-source community. 1. MODEL WEIGHTS (*.safetensors) Licensed under **Upstage Solar License** See: https://huggingface.co/upstage/Solar-Open-100B/blob/main/LICENSE 2. CODE (*.py, *.json, *.jinja files) Licensed under **Apache License 2.0** See: https://www.apache.org/licenses/LICENSE-2.0
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查看详情 ↗Solar-Open2-250B
upstageSolar Open 2 is Upstage’s 250B-A15B open-weight large language model, built for agentic use cases such as office productivity, document-intensive work, and coding. Its Hybrid-Attention Mixture-of-Experts (MoE) architecture with linear attention delivers highly efficient inference even in long-context settings. The examples below assume 8 GPUs with at least 141 GB of memory each, such as NVIDIA H200 or B200 GPUs. Actual memory requirements depend on the context length and serving settings. Use the Upstage Transformers branch with native Solar Open 2 support for local experimentation. For production serving, we recommend vLLM.
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查看详情 ↗Solar Pro 3
upstageSolar Pro 3 is Upstage's powerful Mixture-of-Experts (MoE) language model. With 102B total parameters and 12B active parameters per forward pass, it delivers exceptional performance while maintaining computational efficiency. Optimized...
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$0.15 / M 输入 TokenOpenRouter · upstage/solar-pro-3
查看详情 ↗solar-pro-preview-instruct
upstageWe introduce **Solar Pro Preview**, an advanced large language model (LLM) with 22 billion parameters designed to fit into a single GPU. Solar Pro Preview shows superior performance compared to LLMs with less than 30 billion parameters and delivers performance comparable to models over three times its size, such as Llama 3.1 with 70 billion parameters. Solar Pro Preview is developed using an enhanced version of our previous depth up-scaling method, which scales a Phi-3-medium model with 14 billion parameters to 22 billion parameters, intended to run on a GPU with 80GB of VRAM. Our carefully curated training strategy and dataset have significantly enhanced performance from Phi-3-medium, particularly on the MMLU-Pro and IFEval benchmarks, both respected for evaluating a model’s knowledge and instruction-following abilities. Solar Pro Preview is a pre-release version of the official Solar Pro, with limitations on language coverage and a maximum context length of 4K. However, we believe Solar Pro Preview not only stands out as a highly efficient and capable model, but has the potential to be further extended to cover more languages and capabilities. The official version of Solar Pro will be released this November 2024 with expanded language support beyond English and longer context windows. To stay informed about the latest updates, please sign up for our mailing list. If you have any feedback or questions about the model, please visit our model discussion board and connect with us directly.
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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 ↗Solar Pro 4
upstageSolar Pro 4 是 Upstage 的一款高性价比大型语言模型，拥有 52.4 万词的上下文窗口。它专为长周期任务和智能体工作流而设计，在办公效率、文档密集型...等方面表现出色。
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$0.15 / M 输入 TokenOpenRouter · upstage/solar-pro4
查看详情 ↗TinySolar-248m-4k
upstage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。
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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。