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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 ↗DISCOVER & COMPARE
探索 AI 模型 9
gemma-2-9b-it
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查看详情 ↗* [Gemma 3 Technical Report][g3-tech-report] * [Responsible Generative AI Toolkit][rai-toolkit] * [Gemma on Kaggle][kaggle-gemma] * [Gemma on Vertex Model Garden][vertex-mg-gemma3] Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. Gemma 3 models are multimodal, handling text and image input and generating text output, with open weights for both pre-trained variants and instruction-tuned variants. Gemma 3 has a large, 128K context window, multilingual support in over 140 languages, and is available in more sizes than previous versions. Gemma 3 models are well-suited for a variety of text generation and image understanding tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as laptops, desktops or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone. - **Input:** - Text string, such as a question, a prompt, or a document to be summarized - Images, normalized to 896 x 896 resolution and encoded to 256 tokens each - Total input context of 128K tokens for the 4B, 12B, and 27B sizes, and 32K tokens for the 1B size
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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 ↗See our collection for all versions of Gemma 3 including GGUF, 4-bit & 16-bit formats. - Fine-tune Gemma 3 (12B) for free using our Google Colab notebook here! - Read our Blog about Gemma 3 support: unsloth.ai/blog/gemma3 - View the rest of our notebooks in our docs here. - Export your fine-tuned model to GGUF, Ollama, llama.cpp or 🤗HF. * [Gemma 3 Technical Report][g3-tech-report] * [Responsible Generative AI Toolkit][rai-toolkit] * [Gemma on Kaggle][kaggle-gemma] * [Gemma on Vertex Model Garden][vertex-mg-gemma3]
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查看详情 ↗GLM-5.2-E2M1-Furnace
m8thanComplete experimental conversion of zai-org/GLM-5.2-FP8 for Furnace. **142 safetensors shards, 118,629 tensors, approximately 388 GB.** The source checkpoint is unchanged. This is a full target model, not a pilot or draft. Routed experts are E2M1 (4-bit, block-128 scales), as before. Attention, dense and shared-expert projections are now **FP8 block-128, passed through unchanged from the source** (`dense_weight_format: fp8_block128` in `quantization_config`), instead of BF16. Expert weights and their reconstruction error are identical to the previous revision. The previous BF16-dense revision is `16c3f5ca406349306ffe69de7726d7156b09b91b`. This needs a Furnace build that understands `dense_weight_format` (the commit linked above or later). FP8 dense weights let Furnace's `FURNACE_MEGAKERNEL=1` decode path qualify on this checkpoint.
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查看详情 ↗GLM-5.3-E2M1-Furnace
m8than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 ↗KimiK3-GG
m8thanThis checkpoint is derived from Kimi K3 by Moonshot AI. The original LICENSE and any accompanying notices govern use and are retained. This is a converted, additionally quantized checkpoint, not the original model. It requires a compatible runtime supporting the quantization format specified in config.json. That required metadata is public and must be retained; standard model loaders are not necessarily compatible with this checkpoint. Conversion can change model behavior. Full-model accuracy, perplexity, and generation quality are not certified. Evaluate quality and compatibility for your intended use before deployment.
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查看详情 ↗Llama-2-7b-chat-hf
m8than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 ↗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。