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为你发现值得关注的模型 /* Reusable benchmark table architecture. Inline styles remain as a fallback for HF rendering. */ .vl-table { width: 100%; min-width: 100%; border-collapse: collapse; table-layout: fixed; font-size: 15px; } .vl-table th { font-size: 15px !important; line-height: 1.2; color: #c2410c; background: rgba(249,115,22,.10); } .vl-table td:not(.benchmark-cell):not([colspan]) { font-size: 15px; line-height: 1.2; vertical-align: middle; } .vl-table .benchmark-cell { padding: 12px 10px 12px 18px !important; vertical-align: middle; } .vl-table .benchmark-capability { font-size: 15px; font-weight: 600; line-height: 1.22; color: #c2410c; } .vl-table .benchmark-name { margin-top: 4px; font-size: 11px; font-weight: 400; line-height: 1.2; color: inherit; } .vl-table .metric-stack { display: flex; flex-direction: column; gap: 7px; padding: 3px 0; } .vl-table .metric-label { font-size: 10px; font-weight: 400; line-height: 1.1; color: inherit; } .vl-table .metric-value { margin-top: 2px; font-size: 15px; line-height: 1.15; color: inherit; } .model-table td:first-child { width: 34%; font-weight: 600; } /* HF's theme toggle sets the dark class on an ancestor. */ .dark .vl-table th, .dark .vl-table .benchmark-capability { color: #fdba74 !important; } NeoHorse-1-4B is a 4B causal language model and an initial prototype on the path toward **recursive self-improvement (RSI)**. It is post-trained from Qwen3.5-4B for text-based agent harnesses, tool use, coding, and instruction following. Derived from Qwen/Qwen3.5-4B and fine-tuned by TokenRhythm. This release contains **language-model weights only** and is repackaged for text-only inference. Vision weights are not included. Repackaging changes configuration and tensor key names, without changing the fine-tuned tensor values.
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NeoHorse-1-4B-GGUF Towards Recursive Self-Improvement via Agentic Post-Training with Routing Harness. /* Reusable benchmark table architecture. Inline styles remain as a fallback for HF rendering. */ .vl-table { width: 100%; min-width: 100%; border-collapse: collapse; table-layout: fixed; font-size: 15px; } .vl-table th { font-size: 15px !important; line-height: 1.2; color: #c2410c; background: rgba(249,115,22,.10); } .vl-table td:not(.benchmark-cell):not([colspan]) { font-size: 15px; line-height: 1.2; vertical-align: middle; } .vl-table .benchmark-cell { padding: 12px 10px 12px 18px !important; vertical-align: middle; } .vl-table .benchmark-capability { font-size: 15px; font-weight: 600; line-height: 1.22; color: #c2410c; } .vl-table .benchmark-name { margin-top: 4px; font-size: 11px; font-weight: 400; line-height: 1.2; color: inherit; } .vl-table .metric-stack { display: flex; flex-direction: column; gap: 7px; padding: 3px 0; } .vl-table .metric-label { font-size: 10px; font-weight: 400; line-height: 1.1; color: inherit; } .vl-table .metric-value { margin-top: 2px; font-size: 15px; line-height: 1.15; color: inherit; } .model-table td:first-child { width: 34%; font-weight: 600; } /* HF's theme toggle sets the dark class on an ancestor. */ .dark .vl-table th, .dark .vl-table .benchmark-capability { color: #fdba74 !important; } NeoHorse-1-4B is a 4B causal language model and an initial prototype on the path toward **recursive self-improvement (RSI)**. It is post-trained from Qwen3.5-4B for text-based agent harnesses, tool use, coding, and instruction following.
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NeoHorse-1-9B-GGUF Towards Recursive Self-Improvement via Agentic Post-Training with Routing Harness. /* Reusable benchmark table architecture. Inline styles remain as a fallback for HF rendering. */ .vl-table { width: 100%; min-width: 100%; border-collapse: collapse; table-layout: fixed; font-size: 15px; } .vl-table th { font-size: 15px !important; line-height: 1.2; color: #c2410c; background: rgba(249,115,22,.10); } .vl-table td:not(.benchmark-cell):not([colspan]) { font-size: 15px; line-height: 1.2; vertical-align: middle; } .vl-table .benchmark-cell { padding: 12px 10px 12px 18px !important; vertical-align: middle; } .vl-table .benchmark-capability { font-size: 15px; font-weight: 600; line-height: 1.22; color: #c2410c; } .vl-table .benchmark-name { margin-top: 4px; font-size: 11px; font-weight: 400; line-height: 1.2; color: inherit; } .vl-table .metric-stack { display: flex; flex-direction: column; gap: 7px; padding: 3px 0; } .vl-table .metric-label { font-size: 10px; font-weight: 400; line-height: 1.1; color: inherit; } .vl-table .metric-value { margin-top: 2px; font-size: 15px; line-height: 1.15; color: inherit; } .model-table td:first-child { width: 34%; font-weight: 600; } /* HF's theme toggle sets the dark class on an ancestor. */ .dark .vl-table th, .dark .vl-table .benchmark-capability { color: #fdba74 !important; } NeoHorse-1-9B is a 9B causal language model and an initial prototype on the path toward **recursive self-improvement (RSI)**. It is post-trained from Qwen3.5-9B for text-based agent harnesses, tool use, coding, and instruction following.
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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。
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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
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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。
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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。
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。
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。
