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
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。
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
Original model: a Task Arithmetic merge of Mistral-Nemo-Instruct-2407, Lumimaid-v0.2-12B, and absolute-heresy — tuned for uncensored roleplay and creative writing. 12B params, Mistral architecture. Quantized with llama.cpp (release b9890). Run these in LM Studio, Ollama, KoboldCpp, SillyTavern, or `llama.cpp` directly. Most front-ends (SillyTavern, LM Studio) apply this automatically when you select a **Mistral / Mistral-Nemo** template. The Lumimaid component was also trained with ChatML, so ChatML works too — but Mistral format is the safe default.
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
> **~3 GB of RAM is all you need.** Laptop, old GPU, Raspberry-Pi-class boxes with swap — the Q4 build runs > anywhere. One command and you have a private, offline reasoning model on your machine: > > v2 is a full retrain: **13× more genuine Fable 5 trace data** (11,574 sessions, 16.8M tokens — corpus published) and a rebuilt recipe (completion-masked loss, replay mixing, benchmark-gated checkpoints, seed-averaged weights). Clean answers, structured reasoning, agent instincts — and the transcript artifacts that leaked into v1's replies are gone. *One trade, made on purpose: raw HumanEval-style function synthesis stays the base model's turf (81.7 vs 70.1) — v2 spends that capacity on agent behavior instead, and spends half as much as v1 did.* Measurement notes below. 👇
翻訳待ち・原文を表示
> **~6 GB of RAM is all you need.** The Q4 build fits comfortably on an ordinary laptop or a mid-range GPU. > One command and you have a private, offline reasoning model on your machine: > > Parable-Granite-4.1-8B is an ibm-granite/granite-4.1-8b fine-tune trained on real multi-step agent sessions: planning, tool use, and ` ` reasoning captured from actual Claude Fable 5 and GPT-5.5 agent work, not synthetic Q&A. It is the largest release in the Parable series. **🔮 v2 is coming.** The 3B just got the v2 treatment (13× corpus, rebuilt recipe) — the same upgrade lands here next. Same links, in-place.
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
**A 4B local coding model with agent instincts.** Planning, tool habits and terminal reasoning distilled from real Claude Fable 5 agent sessions, not synthetic Q&A. Runs on ~2.5 GB of RAM. Retrained on **corpus v3.1**: the v2 agent traces plus 1,807 execution-verified solutions generated by the previous build and kept only where the code actually ran against its tests. Two seeds souped, merged at the v2.1 scale. The result **matches or beats base Qwen3-4B on all four execution benchmarks**, where the previous build trailed it on three. If you pulled this model before 11 August 2026, re-pull.
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
> **~6 GB of RAM is all you need.** Laptop, mid-range GPU, yesterday's desktop — the Q4 build runs > anywhere with that much headroom. One command and you have a private, offline reasoning model on your machine: > > **🔮 v2 is coming.** The 3B just got the v2 treatment (13× corpus, rebuilt recipe) — the same upgrade lands here next. Same links, in-place. **📦 Full family.** This 8B is the largest Parable, alongside Parable-Qwen3-4B — browse the full collection for every size, quant, and eval report.
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
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。