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。
GGUF quantizations of apodex/Apodex-1.1-mini, a 35.95B-parameter Qwen3.5 MoE model for research, data, files, code, and tool-driven work. All model files retain the checkpoint's native MTP/NextN head. For image input, use the included F16 vision projector. *Results reported by Apodex AI from evaluations of the original Apodex-1.1 model using FrontierAgent, its agent harness and evaluation suite.*
翻訳待ち・原文を表示
GGUF quantizations of Cloudflare/clef, a 27B multimodal decision model post-trained from Qwen3.8-27B. For image or video input, use the included multimodal projector (`clef-mmproj-BF16.gguf`). Tested with `torch` 2.11 and `transformers` 5.10.2 on a single H200. Image and video inputs also need `pillow`. `systemone` takes a Jev/SystemOne `POST /v1/systemone` request body and returns the same response body: `model`, `answers` keyed by question ID, and `usage`. A `choice` answer has `choice`, `confidence`, and `probabilities`; a `score` answer has the expected `score`, `confidence`, `legend`, and `probabilities`; a `noul` answer has the probability of true. `instructions` is optional, and `images` and `videos` may be added to the request.
翻訳待ち・原文を表示
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
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
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
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
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
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
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
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
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
Mixed-precision **GSQ-RCO** GGUF quantizations of Nanbeige/Nanbeige4.3-33B, optimized for efficient, high-fidelity local inference in `llama.cpp`. **64k Long-Context (Low-VRAM)** Using 4-bit KV cache (`-ctk q4_0 -ctv q4_0`) allows 65k context to run within **< 12 GB total VRAM** on consumer 12 GB GPUs (e.g. RTX 3060 12GB, RTX 4070):
翻訳待ち・原文を表示
Mixed-precision **GSQ-RCO** GGUF quantizations of nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-BF16, optimized for efficient, high-fidelity local inference in `llama.cpp`. The model supports a context length of up to 65,536 tokens (64K). Standard quantization severely degrades hybrid sliding-attention models with shared Key-Value caches, multimodal projections, and speculative draft targets. **GSQ-RCO** solves this through two targeted techniques: - **RCO:** Preserves all 54 shared `k_proj`, `v_proj`, and `norm` tensors across layers 24–41 in high precision (`Q5_K`/`Q6_K`), maintaining structural attention geometry, visual projection alignment, and DFlash draft compatibility. - **GSQ:** Optimizes per-layer precision guided by Hessian sensitivity calibration.
翻訳待ち・原文を表示
Mixed-precision **GSQ-RCO** GGUF quantizations of TokenRhythm/NeoHorse-1-9B, optimized for efficient, high-fidelity local inference in `llama.cpp`. The model supports a context length of up to 262,144 tokens (262K). Standard quantization severely degrades hybrid SSM-Transformer models (e.g. stock low-bit quantizations cause recurrent state drift and degradation in reasoning coherence). **GSQ-RCO** solves this through two targeted techniques: - **RCO:** Keeps all 48 critical SSM parameters in unquantized **BF16**. - **GSQ:** Optimizes per-layer precision guided by Hessian sensitivity calibration.
翻訳待ち・原文を表示
GGUF files for Nex-N2.5-mini, a long-context agentic model for coding, tool use, computer use, and multimodal workloads. The source checkpoint supports a native context length of **262,144 tokens (256K)**. *Benchmark results reported by Nex-AGI for the original Nex-N2.5 family. These figures are not measurements of this GGUF conversion.* The projector is optional for text-only use. Use it with a current llama.cpp build that supports the model's multimodal path.
翻訳待ち・原文を表示
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
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。
Mixed-precision **GSQ-RCO** GGUF quantizations of ornith-ai/Ornith-1.5-9B, optimized for efficient, high-fidelity local inference in `llama.cpp`. The model supports a context length of up to 262,144 tokens (262K). Standard quantization severely degrades hybrid SSM-Transformer models (e.g. stock IQ3_M perplexity drops to ~75.2). **GSQ-RCO** solves this through two targeted techniques: - **RCO:** Keeps all 48 critical SSM parameters in unquantized **BF16**. - **GSQ:** Optimizes per-layer precision guided by Hessian sensitivity calibration.
翻訳待ち・原文を表示
GGUF, Safetensors, and MLX quantized formats of Qwen/Qwen-Image-2.1-Turbo for local image generation using the official turbo base weights. **Q4_K_M** is recommended for the best balance of size and quality for GGUF. **INT8 ConvRot** and **NVFP4** offer high performance on supported GPUs, while **MLX** formats are optimized for Apple Silicon. All required companion files (GGUF transformer, text encoder, and VAE) are hosted directly in this repository.
翻訳待ち・原文を表示
GGUF, Safetensors, and MLX quantized formats of Qwen/Qwen-Image-2.1 for local image generation using the original upstream base weights. **Q4_K_M** is recommended for the best balance of size and quality for GGUF. **INT8 ConvRot** and **NVFP4** offer high performance on supported GPUs, while **MLX** formats are optimized for Apple Silicon. Use the model with ComfyUI and ComfyUI-GGUF.
翻訳待ち・原文を表示
> [!IMPORTANT] > **Compatibility:** These GGUF files require `llama.cpp` **b10828** or later, which includes official support for the Spark-X2.5 (`spark2_5`) architecture. Applications with a bundled runtime must use an equivalent or newer build. llama.cpp support GGUF quantizations of XHToken/Spark-X2.5-4B, a 4B general-purpose language model for reasoning, coding, tool use, and agentic workflows. Native context: **1,048,576 tokens (1M)**. Includes the upstream chat_template.jinja. Checksums: SHA256SUMS.txt.
翻訳待ち・原文を表示
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
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。
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。
