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言語から画像、オープンウェイトからAPIまで。能力と実際の費用を比較。
次のアイデアに合うAIを見つけましょう。
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The NVIDIA Qwen2.5-VL-7B-Instruct-FP4 model is the quantized version of Alibaba's Qwen2.5-VL-7B-Instruct model, which is an auto-regressive language model that uses an optimized transformer architecture. For more information, please check here. The NVIDIA Qwen2.5-VL-7B-Instruct-FP4 model is quantized with TensorRT Model Optimizer. This model is not owned or developed by NVIDIA. It was developed and built to a third party’s requirements for this application and use case. See the Non-NVIDIA (Qwen2.5-VL-7B-Instruct) Model Card. Use of this model is governed by nvidia-open-model-license ADDITIONAL INFORMATION: Apache 2.0.
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The NVIDIA Qwen3-14B FP8 model is the quantized version of Alibaba's Qwen3-14B model, which is an auto-regressive language model that uses an optimized transformer architecture. For more information, please check here. The NVIDIA Qwen3-14B FP8 model is quantized with TensorRT Model Optimizer. This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case; see link to Non-NVIDIA (Qwen3-14B) Model Card. Use of this model is governed by Apache license 2.0
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The NVIDIA Qwen3-14B FP4 model is the quantized version of Alibaba's Qwen3-14B model, which is an auto-regressive language model that uses an optimized transformer architecture. For more information, please check here. The NVIDIA Qwen3-14B FP4 model is quantized with TensorRT Model Optimizer. This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case; see link to Non-NVIDIA (Qwen3-14B) Model Card. Developers looking to take off the shelf pre-quantized models for deployment in AI Agent systems, chatbots, RAG systems, and other AI-powered applications.
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The NVIDIA Qwen3-235B-A22B FP4 model is the quantized version of Alibaba's Qwen3-235B-A22B model, which is an auto-regressive language model that uses an optimized transformer architecture. For more information, please check here. The NVIDIA Qwen3-235B-A22B FP4 model is quantized with TensorRT Model Optimizer. This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case; see link to Non-NVIDIA (Qwen3-235B-A22B) Model Card. Developers looking to take off the shelf pre-quantized models for deployment in AI Agent systems, chatbots, RAG systems, and other AI-powered applications.
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The NVIDIA Qwen3-30B-A3B FP4 model is the quantized version of Alibaba's Qwen3-30B-A3B model, which is an auto-regressive language model that uses an optimized transformer architecture. For more information, please check here. The NVIDIA Qwen3-30B-A3B FP4 model is quantized with TensorRT Model Optimizer. This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case; see link to Non-NVIDIA (Qwen3-30B-A3B) Model Card. Developers looking to take off the shelf pre-quantized models for deployment in AI Agent systems, chatbots, RAG systems, and other AI-powered applications.
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The NVIDIA Qwen3-32B FP4 model is the quantized version of Alibaba's Qwen3-32B model, which is an auto-regressive language model that uses an optimized transformer architecture. For more information, please check here. The NVIDIA Qwen3-32B FP4 model is quantized with TensorRT Model Optimizer. This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case; see link to Non-NVIDIA (Qwen3-32B) Model Card. Developers looking to take off the shelf pre-quantized models for deployment in AI Agent systems, chatbots, RAG systems, and other AI-powered applications.
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The NVIDIA Qwen3.5-122B-A10B-NVFP4 model is the quantized version of Alibaba's Qwen3.5-122B-A10B model, which is an auto-regressive language model that uses an optimized transformer architecture. For more information, please check here. The NVIDIA Qwen3.5-122B-A10B NVFP4 model is quantized with Model Optimizer. This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case; see link to Non-NVIDIA (Qwen3.5-122B-A10B) Model Card from Alibaba. Developers looking to take off-the-shelf, pre-quantized models for deployment in AI Agent systems, chatbots, RAG systems, and other AI-powered applications.
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The NVIDIA Qwen3.5-397B-A17B NVFP4 model is the quantized version of Alibaba's Qwen3.5-397B-A17B model, which is an auto-regressive language model that uses an optimized transformer architecture. For more information, please check here. The NVIDIA Qwen3.5-397B-A17B NVFP4 model is quantized with Model Optimizer. This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case; see link to Non-NVIDIA (Qwen3.5-397B-A17B) Model Card. Developers looking to take off-the-shelf, pre-quantized models for deployment in AI Agent systems, chatbots, RAG systems, and other AI-powered applications.
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The NVIDIA Qwen3.5-397B-A17B NVFP4 V2 model is the quantized version of Alibaba's Qwen3.5-397B-A17B model, which is an auto-regressive language model that uses an optimized transformer architecture. For more information, please check here. The NVIDIA Qwen3.5-397B-A17B NVFP4 V2 model is quantized with Model Optimizer. Developers looking to take off-the-shelf, pre-quantized models for deployment in AI Agent systems, chatbots, RAG systems, and other AI-powered applications. Hugging Face 06/29/2026 via https://huggingface.co/nvidia/Qwen3.5-397B-A17B-NVFP4-V2
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提供元・分類・入出力の条件を変更してください。新しいモデルを随時追加しています。
月間トークン数を調整してAPI費用を比較。
収集済みの比較可能な100万トークン当たり米ドル料金です。キャッシュ割引、段階料金、追加推論出力、税金は含みません。最新料金は出典で確認してください。
入力・出力・キャッシュ料金を分け、同じ用途と使用量で総費用を比較します。画像・音声・動画は枚数、時間、解像度で課金される場合があります。
必ずしもそうではありません。バージョンごとのライセンス、商用制限、ポリシーを確認してください。自前運用には計算資源と保守費用も必要です。
順位はテスト、バージョン、評価方法によります。実際の業務データで精度、遅延、安定性、費用を比較してください。