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查看详情 ↗DISCOVER & COMPARE
探索 AI 模型 84999
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查看详情 ↗Hy3-FP8
腾讯- Model Introduction - Stronger Agent Capabilities - More Reliable Product Experiences - Benchmark Appendix - News - Model Links - Quickstart - Deployment - vLLM - SGLang - Finetuning - Quantization - License - Contact Us **Hy3** is a 295B-parameter Mixture-of-Experts (MoE) model with 21B active parameters and 3.8B MTP layer parameters, developed by the Tencent Hy Team. Following the Hy3 Preview launch in late April, we gathered feedback from 50+ products and scaled up post-training with higher quality data. Today, we introduce Hy3, which outperforms similar-size models and rivals flagship open-source models with 2-5x parameters. It also shows significant gains in utility across various products and productivity tasks. Building on Hy3 Preview, we further improved the quality and diversity of post-training data while scaling up RL training. Hy3 shows solid gains across reasoning, agentic, and long-context tasks, competitive with much larger flagship models.
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查看详情 ↗Hy4
腾讯The official release of Hy4 delivers comprehensive and substantial improvements over Hy4 Preview, with particularly strong gains in agentic capabilities and performance across a wide range of long-horizon, real-world productivity...
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查看详情 ↗- Model Introduction - A New Flagship Generation - Built for Productivity - Benchmark Appendix - Known Limitations - News - Model Links - Quickstart - Deployment - vLLM - SGLang - Finetuning - Quantization - License - Contact Us **Hy4 preview** is a new-generation Mixture-of-Experts (MoE) flagship model developed by the Tencent Hy Team. The model comprises 770B total parameters, of which 49B are activated per token. The backbone consists of 78 layers, where the first layer uses a standard dense FFN and the remaining 77 layers replace it with MoE, each containing 256 routed experts and 1 shared expert; every token activates the top-8 routed experts along with the shared expert. In addition to the backbone, 1 native MTP layer (10B total parameters, 0.7B activated) is built in for speculative decoding. On the architecture side, inspired by DeepSeek and GLM, the attention module employs Gated DeepSeek Sparse Attention (Gated DSA) with IndexCache for cross-layer sparse index reuse. The residual pathway uses iHC (identity Hyper-Connections) to expand inter-layer information flow.
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查看详情 ↗hybrid-32B
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查看详情 ↗Svene-14b
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查看详情 ↗> ### Teacher model > > This model is a **LoRA decision-distillation of > `convaiinnovations/laya`** > by **Convai Innovations**. laya supplies every training label — the > `should_compact` / `preserve` / `level` / `mode` decisions this model emits. > No laya weights are redistributed here; the teacher is credited as the source > of the supervision, which it is. > > If you want the same decisions without this model, go and use laya. A **Qwen3.8-27B Bonsai 2 (Heretic)** derivative carrying a small LoRA that turns a long working context into one structured compaction decision. **Text only.** No vision tower, no projector. If you feed it images it will not do anything useful with them.
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查看详情 ↗Ternary-Bonsai-2-27B-gguf
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查看详情 ↗Ternary-Bonsai-2-27B-gguf-plus
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查看详情 ↗This Text-to-Speech (TTS) model is provided solely for research, experimentation, and technology development purposes. Any audio content generated by the model does not represent the voice, identity, opinions, or endorsement of any real individual or organization. The authors and related parties assume no responsibility for any misuse, unlawful activities, violations of privacy, personality rights, intellectual property rights, or any direct or indirect damages arising from the use of this model. Users bear full responsibility and legal liability for the deployment, distribution, and use of the model. The use of this model for impersonation, voice cloning of individuals without lawful consent, creating misleading content, fraud, manipulation of public opinion, or any purpose that violates applicable laws is strictly prohibited. When using or sharing generated audio, it is strongly recommended to clearly disclose that the content is AI-generated and to comply fully with all applicable legal regulations, platform policies, and ethical standards. This model was trained entirely on **self-funded, personal hardware** — every GPU hour and electricity bill came out of my own pocket. If this work is useful to you, a small contribution helps cover the cost of the next training run and keeps future models free and open. Every coffee is hugely appreciated 🙏
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查看详情 ↗The **Vietnamese Speech-to-Text (ASR)** model is built on the **ZipFormer architecture** — an improved variant of the Conformer — featuring only **30 million parameters** yet achieving **exceptional performance** in both speed and accuracy. On CPU, the model can transcribe a **12-second audio clip in just 0.3 seconds**, significantly faster than most traditional ASR systems without requiring a GPU. This model was trained entirely on **self-funded, personal hardware** — every GPU hour and electricity bill came out of my own pocket. If this work is useful to you, a small contribution helps cover the cost of the next training run and keeps future models free and open. Every coffee is hugely appreciated 🙏 You can try the model directly here: 👉 https://huggingface.co/spaces/hynt/k2-automatic-speech-recognition-demo
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查看详情 ↗Hypa-Llama3.1-8b-SFT
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查看详情 ↗Hypa-Orpheus-3b-TTS-VC
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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。