DeepSeek-V3.1-AWQ
QuantTrio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 30
DeepSeek-V3.2-AWQ
QuantTrio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 ↗gemma-4-31B-it-AWQ
QuantTrioBase model: google/gemma-4-31B-it This repo quantizes the model using data-free quantization (no calibration dataset required). Gemma is a family of open models built by Google DeepMind. Gemma 4 models are multimodal, handling text and image input (with audio supported on small models) and generating text output. This release includes open-weights models in both pre-trained and instruction-tuned variants. Gemma 4 features a context window of up to 256K tokens and maintains multilingual support in over 140 languages.
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查看详情 ↗gemma-4-31B-it-AWQ-6Bit
QuantTrio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 ↗GLM-4.1V-9B-Thinking-AWQ
QuantTrio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 ↗GLM-4.5-Air-AWQ-FP16Mix
QuantTrioNote: You must use `--enable-expert-parallel` to start this model, otherwise the expert tensor TP will not divide evenly. This is required even for 2 GPUs. The `awq_marlin` module in `vllm` misses checking the `modules_to_not_convert` parameter when loading AWQ-MoE modules, which causes mixed quantization of MoE to fail or report errors. Refer to: [[Issue #21888]](https://github.com/vllm-project/vllm/pull/21888) Before the PR is merged, temporarily replace `awq_marlin.py` in `vllm/model_executor/layers/quantization/awq_marlin.py`.
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查看详情 ↗GLM-4.5V-AWQ
QuantTrio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 ↗GLM-4.7-Flash-AWQ
QuantTrioBase model: zai-org/GLM-4.7-Flash GLM-4.7-Flash is a 30B-A3B MoE model. As the strongest model in the 30B class, GLM-4.7-Flash offers a new option for lightweight deployment that balances performance and efficiency. For local deployment, GLM-4.7-Flash supports inference frameworks including vLLM and SGLang. Comprehensive deployment instructions are available in the official Github repository.
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查看详情 ↗GLM-5.2-Int4-Int8Mix
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查看详情 ↗MiniMax-M2-AWQ
QuantTrio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 ↗Qwen3-235B-A22B-Instruct-2507-AWQ
QuantTrioNote: When launching with 8 GPUs, --enable-expert-parallel must be specified; otherwise, the expert tensors cannot be evenly split across tensor parallel ranks. This option is not required for 4-GPU setups. We introduce the updated version of the **Qwen3-235B-A22B non-thinking mode**, named **Qwen3-235B-A22B-Instruct-2507**, featuring the following key enhancements: **Qwen3-235B-A22B-Instruct-2507** has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Number of Parameters: 235B in total and 22B activated - Number of Paramaters (Non-Embedding): 234B - Number of Layers: 94 - Number of Attention Heads (GQA): 64 for Q and 4 for KV - Number of Experts: 128 - Number of Activated Experts: 8 - Context Length: **262,144 natively**.
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查看详情 ↗Qwen3-30B-A3B-Thinking-2507-AWQ
QuantTrioNote: You must use `--enable-expert-parallel` to start this model, otherwise the expert tensor TP will not divide evenly. This is required even for 2 GPUs. Over the past three months, we have continued to scale the **thinking capability** of Qwen3-30B-A3B, improving both the **quality and depth** of reasoning. We are pleased to introduce **Qwen3-30B-A3B-Thinking-2507**, featuring the following key enhancements: **NOTE**: This version has an increased thinking length. We strongly recommend its use in highly complex reasoning tasks.
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查看详情 ↗Qwen3.5-122B-A10B-AWQ
QuantTrio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 ↗Qwen3.5-27B-AWQ
QuantTrioThis repo quantizes the model using data-free quantization (no calibration dataset required). > [!Note] > This repository contains model weights and configuration files for the post-trained model in the Hugging Face Transformers format. > > These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, KTransformers, etc. Over recent months, we have intensified our focus on developing foundation models that deliver exceptional utility and performance. Qwen3.5 represents a significant leap forward, integrating breakthroughs in multimodal learning, architectural efficiency, reinforcement learning scale, and global accessibility to empower developers and enterprises with unprecedented capability and efficiency.
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查看详情 ↗> **v2 Update:** > - **Accuracy preserved:** Matches base model on HumanEval (**96.91% pass@1**) > > - **Shorter reasoning:** ~**24% reduction** in chain-of-thought length > > - **Higher efficiency:** **+31.6% more correct solutions per token** > > - ⚠️**Trade-off:** −1.24% on HumanEval+ −7.2% on MMLU-Pro (Indicating reduced general knowledge reasoning performance) > > *⚠️Note: Due to the scope of SFT data and training focus, the model may underperform the base model on certain tasks requiring long-context understanding or more complex multi-step reasoning. The efficiency and accuracy results reported here are based solely on the HumanEval and HumanEval+ benchmarks. Thank you for your understanding.* **Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-v2** is the second iteration of this reasoning-focused Qwen3.5-27B fine-tune, built to drastically improve the *efficiency* of chain-of-thought generation, unlocking highly substantial gains in reasoning speed and cost-reduction while actually increasing absolute accuracy. Compared with the earlier version, **v2 was trained with 14,000 Claude 4.6 Opus-style general reasoning samples**, with a stronger emphasis on transferring concise, reusable reasoning patterns rather than only maximizing raw benchmark scores. The goal of v2 is not simply to make the model "think more," but to help it **think more economically**: reducing unnecessarily long internal chains, avoiding verbose over-analysis on easy problems, and massively improving the reasoning-cost-to-quality ratio while beating the baseline's benchmark correctness.
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查看详情 ↗Qwen3.5-2B-AWQ
QuantTrio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 ↗Qwen3.5-35B-A3B-AWQ
QuantTrioThis repo quantizes the model using data-free quantization (no calibration dataset required). Note: When launching with TP=8, include `--enable-expert-parallel`; otherwise the expert tensors wouldn’t be evenly sharded across GPU devices. > [!Note] > This repository contains model weights and configuration files for the post-trained model in the Hugging Face Transformers format. > > These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, KTransformers, etc.
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查看详情 ↗Qwen3.5-397B-A17B-AWQ
QuantTrio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 ↗Qwen3.5-4B-AWQ
QuantTrio> [!Note] > This repository contains model weights and configuration files for the post-trained model in the Hugging Face Transformers format. > > These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, KTransformers, etc. Over recent months, we have intensified our focus on developing foundation models that deliver exceptional utility and performance. Qwen3.5 represents a significant leap forward, integrating breakthroughs in multimodal learning, architectural efficiency, reinforcement learning scale, and global accessibility to empower developers and enterprises with unprecedented capability and efficiency. - **Unified Vision-Language Foundation**: Early fusion training on multimodal tokens achieves cross-generational parity with Qwen3 and outperforms Qwen3-VL models across reasoning, coding, agents, and visual understanding benchmarks.
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查看详情 ↗Qwen3.5-9B-AWQ
QuantTrio> [!Note] > This repository contains model weights and configuration files for the post-trained model in the Hugging Face Transformers format. > > These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, KTransformers, etc. Over recent months, we have intensified our focus on developing foundation models that deliver exceptional utility and performance. Qwen3.5 represents a significant leap forward, integrating breakthroughs in multimodal learning, architectural efficiency, reinforcement learning scale, and global accessibility to empower developers and enterprises with unprecedented capability and efficiency. - **Unified Vision-Language Foundation**: Early fusion training on multimodal tokens achieves cross-generational parity with Qwen3 and outperforms Qwen3-VL models across reasoning, coding, agents, and visual understanding benchmarks.
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查看详情 ↗Qwen3.6-27B-AWQ
QuantTrioBase model: Qwen/Qwen3.6-27B Note: When launching with TP=8, include `--enable-expert-parallel`; otherwise the expert tensors wouldn’t be evenly sharded across GPU devices. > [!Note] > This repository contains model weights and configuration files for the post-trained model in the Hugging Face Transformers format. > > These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, KTransformers, etc.
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查看详情 ↗Qwen3.6-35B-A3B-AWQ
QuantTrioBase model: Qwen/Qwen3.6-35B-A3B Note: When launching with TP=8, include `--enable-expert-parallel`; otherwise the expert tensors wouldn’t be evenly sharded across GPU devices. > [!Note] > This repository contains model weights and configuration files for the post-trained model in the Hugging Face Transformers format. > > These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, KTransformers, etc.
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查看详情 ↗Qwen3-Coder-30B-A3B-Instruct-AWQ
QuantTrioBase model Qwen3-Coder-30B-A3B-Instruct This model suffers from significant loss under 4-bit quantization, please use with caution. Note: You must use `--enable-expert-parallel` to start this model, otherwise the expert tensor TP will not divide evenly. This is required even for 2 GPUs.
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查看详情 ↗Base model:Qwen3-Coder-30B-A3B-Instruct Note: When using 4 GPUs, you must include `--enable-expert-parallel` because expert tensor TP must be evenly divisible; for 2 GPUs this is not necessary. **Qwen3-Coder** is available in multiple sizes. Today, we're excited to introduce **Qwen3-Coder-30B-A3B-Instruct**. This streamlined model maintains impressive performance and efficiency, featuring the following key enhancements:
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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。