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AIモデルを探す 1038
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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詳細を見る ↗gemma-4-12b
unslothGemma 4 12B can now be run and fine-tuned in Unsloth Studio . Read our guide . See all versions of Gemma 4 (GGUF, 16-bit etc.) in our collection . Example of Gemma 4 E4B (4-bit GGUF) running in Unsloth Studio with tool-calling: > [!Note] > This model card is for the Gemma 4 12B Unified model, which is part of the Gemma 4 family of open models. Built with the same multimodal functionality as Gemma 4 E2B and E4B (text, audio, image, and video inputs), it brings native audio and vision understanding directly to local environments without the need for separate encoders. This unified approach to multimodality makes the model encoder-free, offering a deployment size that is perfect for consumer devices and streamlined local execution. 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 E2B, E4B, and 12B) 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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詳細を見る ↗gemma-4-12b-it
unslothGemma 4 12B can now be run and fine-tuned in Unsloth Studio . Read our guide . See all versions of Gemma 4 (GGUF, 16-bit etc.) in our collection . Example of Gemma 4 E4B (4-bit GGUF) running in Unsloth Studio with tool-calling: > [!Note] > This model card is for the Gemma 4 12B Unified model, which is part of the Gemma 4 family of open models. Built with the same multimodal functionality as Gemma 4 E2B and E4B (text, audio, image, and video inputs), it brings native audio and vision understanding directly to local environments without the need for separate encoders. This unified approach to multimodality makes the model encoder-free, offering a deployment size that is perfect for consumer devices and streamlined local execution. 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 E2B, E4B, and 12B) 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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詳細を見る ↗gemma-4-12b-it-GGUF
unslothJun 9 Update: Added MTP support. See our MTP Guide . Gemma 4 12B can now be run and fine-tuned in Unsloth Studio . Read our guide . See all versions of Gemma 4 (GGUF, 16-bit etc.) in our collection . Example of Gemma 4 E4B (4-bit GGUF) running in Unsloth Studio with tool-calling: > [!Note] > This model card is for the Gemma 4 12B Unified model, which is part of the Gemma 4 family of open models. Built with the same multimodal functionality as Gemma 4 E2B and E4B (text, audio, image, and video inputs), it brings native audio and vision understanding directly to local environments without the need for separate encoders. This unified approach to multimodality makes the model encoder-free, offering a deployment size that is perfect for consumer devices and streamlined local execution. 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 E2B, E4B, and 12B) 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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詳細を見る ↗gemma-4-12b-it-NVFP4
unslothGemma 4 12B can now be run and fine-tuned in Unsloth Studio . Read our guide . See all versions of Gemma 4 (GGUF, 16-bit etc.) in our collection . Example of Gemma 4 E4B (4-bit GGUF) running in Unsloth Studio with tool-calling: > [!Note] > This model card is for the Gemma 4 12B Unified model, which is part of the Gemma 4 family of open models. Built with the same multimodal functionality as Gemma 4 E2B and E4B (text, audio, image, and video inputs), it brings native audio and vision understanding directly to local environments without the need for separate encoders. This unified approach to multimodality makes the model encoder-free, offering a deployment size that is perfect for consumer devices and streamlined local execution. 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 E2B, E4B, and 12B) 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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詳細を見る ↗gemma-4-12B-it-qat-GGUF
unslothJun 9 Update: Added MTP support. See our MTP Guide . Gemma 4 can now be run and fine-tuned in Unsloth Studio . Read our guide . See all versions of Gemma 4 QAT (GGUF, 16-bit etc.) in our collection . Example of Gemma 4 E4B (4-bit GGUF) running in Unsloth Studio with tool-calling: This model ships a Multi-Token Prediction drafter at the repo root (`mtp-gemma-4-12B-it.gguf`, a near-lossless smart Q4_0). A recent llama.cpp auto-discovers it from `-hf`, so you do not pass `--model-draft`: The drafter shares the target's KV cache and does not change the output (the target verifies every drafted token). See the `MTP/` folder for the other precisions and explicit usage.
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詳細を見る ↗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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詳細を見る ↗gemma-4-12B-it-qat-w4a16
unsloth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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詳細を見る ↗gemma-4-26B-A4B
unsloth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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詳細を見る ↗gemma-4-26B-A4B-it
unslothGemma 4 can now be run and fine-tuned in Unsloth Studio. Read our guide. See all versions of Gemma 4 (GGUF, 16-bit etc.) in our collection. 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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詳細を見る ↗gemma-4-26B-A4B-it-GGUF
unslothJun 9 Update: Added MTP support. See our MTP Guide . Apr 11 Update: Re-download for Google's latest chat template and llama.cpp fixes. Gemma 4 can now be run and fine-tuned in Unsloth Studio . Read our guide . See all versions of Gemma 4 (GGUF, 16-bit etc.) in our collection . Example of Gemma 4 E4B (4-bit GGUF) running in Unsloth Studio with tool-calling: 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. Featuring both Dense and Mixture-of-Experts (MoE) architectures, Gemma 4 is well-suited for tasks like text generation, coding, and reasoning. The models are available in four distinct sizes: **E2B**, **E4B**, **26B A4B**, and **31B**. Their diverse sizes make them deployable in environments ranging from high-end phones to laptops and servers, democratizing access to state-of-the-art AI.
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詳細を見る ↗gemma-4-26b-a4b-it-MLX-8bit
unsloth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。
基本情報を収録・詳細は順次追加
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詳細を見る ↗gemma-4-26B-A4B-it-NVFP4
unslothGemma 4 can now be run and fine-tuned in Unsloth Studio. Read our guide. See all versions of Gemma 4 (GGUF, 16-bit etc.) in our collection. Do not use the Marlin backend (around 2x slower); let vLLM auto-select the NVFP4 kernel.
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詳細を見る ↗gemma-4-26B-A4B-it-qat-GGUF
unslothJun 9 Update: Added MTP support. See our MTP Guide . Gemma 4 can now be run and fine-tuned in Unsloth Studio . Read our guide . See all versions of Gemma 4 QAT (GGUF, 16-bit etc.) in our collection . Example of Gemma 4 E4B (4-bit GGUF) running in Unsloth Studio with tool-calling: This model ships a Multi-Token Prediction drafter at the repo root (`mtp-gemma-4-26B-A4B-it.gguf`, a near-lossless smart Q4_0). A recent llama.cpp auto-discovers it from `-hf`, so you do not pass `--model-draft`: The drafter shares the target's KV cache and does not change the output (the target verifies every drafted token). See the `MTP/` folder for the other precisions and explicit usage.
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詳細を見る ↗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。
基本情報を収録・詳細は順次追加
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詳細を見る ↗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。
基本情報を収録・詳細は順次追加
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詳細を見る ↗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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詳細を見る ↗gemma-4-31B
unsloth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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詳細を見る ↗gemma-4-31B-it
unslothGemma 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. Featuring both Dense and Mixture-of-Experts (MoE) architectures, Gemma 4 is well-suited for tasks like text generation, coding, and reasoning. The models are available in four distinct sizes: **E2B**, **E4B**, **26B A4B**, and **31B**. Their diverse sizes make them deployable in environments ranging from high-end phones to laptops and servers, democratizing access to state-of-the-art AI. Gemma 4 models are designed to deliver frontier-level performance at each size, targeting deployment scenarios from mobile and edge devices (E2B, E4B) to consumer GPUs and workstations (26B A4B, 31B). They are well-suited for reasoning, agentic workflows, coding, and multimodal understanding.
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詳細を見る ↗gemma-4-31B-it-GGUF
unslothJun 9 Update: Added MTP support. See our MTP Guide . Apr 11 Update: Re-download for Google's latest chat template and llama.cpp fixes. Gemma 4 can now be run and fine-tuned in Unsloth Studio . Read our guide . See all versions of Gemma 4 (GGUF, 16-bit etc.) in our collection . Example of Gemma 4 E4B (4-bit GGUF) running in Unsloth Studio with tool-calling: 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. Featuring both Dense and Mixture-of-Experts (MoE) architectures, Gemma 4 is well-suited for tasks like text generation, coding, and reasoning. The models are available in four distinct sizes: **E2B**, **E4B**, **26B A4B**, and **31B**. Their diverse sizes make them deployable in environments ranging from high-end phones to laptops and servers, democratizing access to state-of-the-art AI.
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詳細を見る ↗gemma-4-31b-it-MLX-8bit
unsloth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。
基本情報を収録・詳細は順次追加
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料金未確認マルチモーダル・チャネル料金は個別に確認
詳細を見る ↗gemma-4-31B-it-NVFP4
unslothGemma 4 can now be run and fine-tuned in Unsloth Studio. Read our guide. See all versions of Gemma 4 (GGUF, 16-bit etc.) in our collection. Do not use the Marlin backend (around 2x slower); let vLLM auto-select the NVFP4 kernel.
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詳細を見る ↗gemma-4-31B-it-qat-GGUF
unslothJun 9 Update: Added MTP support. See our MTP Guide . Gemma 4 can now be run and fine-tuned in Unsloth Studio . Read our guide . See all versions of Gemma 4 QAT (GGUF, 16-bit etc.) in our collection . Example of Gemma 4 E4B (4-bit GGUF) running in Unsloth Studio with tool-calling: This model ships a Multi-Token Prediction drafter at the repo root (`mtp-gemma-4-31B-it.gguf`, a near-lossless smart Q4_0). A recent llama.cpp auto-discovers it from `-hf`, so you do not pass `--model-draft`: The drafter shares the target's KV cache and does not change the output (the target verifies every drafted token). See the `MTP/` folder for the other precisions and explicit usage.
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1 M = 100万トークン・米ドル
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同一チャネルの基本テキスト料金を比較
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。
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API料金はどう比較しますか？+
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。
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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。