AniReal
Hosioka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。
Basic profile indexed · More details to follow
Input Text→ Output Image
Price not verifiedCheck multimodal and channel pricing separately
View details ↗DISCOVER & COMPARE
Explore AI models 84999
keural-14.8b-sft
hossain0909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。
Basic profile indexed · More details to follow
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗keural-dpo-3500
hossain0909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。
Basic profile indexed · More details to follow
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗keural-sft-18k
hossain0909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。
Basic profile indexed · More details to follow
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗clinical-t5
hossboll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。
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗whisper-large-v3-turbo-uzbek-ct2
hostmepanda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。
Basic profile indexed · More details to follow
Input Audio→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗bekko-embedding-v1-a25m
hotchpotchbekko-embedding-v1-a25m is an ultra-compact multilingual text embedding model. It has just 25M active parameters — light enough to run comfortably on modest CPUs — yet its retrieval quality is comparable to models with 3–10x more active parameters. You can also try bekko right in your browser: the bekko-embedding-web demo runs the model fully client-side with Transformers.js — no server involved. > [!NOTE] > For a guided overview of the models, training recipe, and results, read Bekko Embedding: how small can a multilingual retrieval model be?.
Input Text→ Output Embedding
Price not verifiedCheck multimodal and channel pricing separately
View details ↗bekko-embedding-v1-a8m
hotchpotchbekko-embedding-v1-a8m is an ultra-compact multilingual text embedding model. It has just 8M active parameters — light enough to run comfortably even on low-spec CPUs — yet its retrieval quality is comparable to models with 3–10x more active parameters. You can also try bekko right in your browser: the bekko-embedding-web demo runs the model fully client-side with Transformers.js — no server involved. > [!NOTE] > For a guided overview of the models, training recipe, and results, read Bekko Embedding: how small can a multilingual retrieval model be?.
Input Text→ Output Embedding
Price not verifiedCheck multimodal and channel pricing separately
View details ↗japanese-bge-reranker-v2-m3-v1
hotchpotch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。
Basic profile indexed · More details to follow
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗japanese-reranker-base-v2
hotchpotch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。
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗japanese-reranker-cross-encoder-base-v1
hotchpotch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。
Basic profile indexed · More details to follow
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗japanese-reranker-cross-encoder-large-v1
hotchpotch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。
Basic profile indexed · More details to follow
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗japanese-reranker-cross-encoder-small-v1
hotchpotch- 日本語最高性能のRerankerをリリース / そもそも Reranker とは? - 日本語 Reranker 作成のテクニカルレポート
Translation queued · Showing source text
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗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。
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗japanese-reranker-small-v2
hotchpotchjapanese-reranker-tiny-v2、japanese-reranker-xsmall-v2、japanese-reranker-small-v2、japanese-reranker-base-v2は、以下の特徴を持つ小型リランカーモデルです： 1. CPUやAppleシリコン環境でも実用的な速度で動作 2. 高価なGPUリソースなしでもRAGシステムの精度向上が可能 3. エッジデバイスでの展開や低レイテンシが要求される本番環境で活用可能 4. ModernBertベースのruri-v3-pt-30m（tiny/xsmall）、ruri-v3-pt-70m（small）、ruri-v3-pt-130m（base）を利用 以下は約15万ペアをリランキングした際の推論速度結果（トークナイズ時間を除く純粋なモデル推論時間）です。MPS(Appleシリコン)とCPU測定にはM4 Max、GPUにはRTX5090を使用しています。GPU処理では flash-attention2 を使用しています。
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗japanese-reranker-tiny-v2
hotchpotch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。
Basic profile indexed · More details to follow
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗japanese-reranker-xsmall-v2
hotchpotch- とても小さく速く実用的な日本語リランカー japanese-reranker-tiny,xsmall v2 を公開 - 日本語最高性能のRerankerをリリース / そもそも Reranker とは? - 日本語 Reranker 作成のテクニカルレポート japanese-reranker-tiny-v2、japanese-reranker-xsmall-v2、japanese-reranker-small-v2、japanese-reranker-base-v2は、以下の特徴を持つ小型リランカーモデルです： 1. CPUやAppleシリコン環境でも実用的な速度で動作 2. 高価なGPUリソースなしでもRAGシステムの精度向上が可能 3. エッジデバイスでの展開や低レイテンシが要求される本番環境で活用可能 4. ModernBertベースのruri-v3-pt-30m（tiny/xsmall）、ruri-v3-pt-70m（small）、ruri-v3-pt-130m（base）を利用
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗japanese-splade-v2
hotchpotch高性能な日本語 SPLADE (Sparse Lexical and Expansion Model) モデルです。テキストからスパースベクトルへの変換デモで、どのようなスパースベクトルに変換できるか、WebUI から気軽にお試しいただけます。 japanese-splade-v2 は JMTEB をスパースベクトルで評価できるように変更したコードでの評価となっています。 なお、japanese-splade-v2 は JMTEB タスクである jaqket(や派生のjaqra), mrtydi(と派生のmiracl), jagovfaqs, nlp_jornal のデータセットのtrain,dev, testなどのデータは **学習に利用していません**。
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗mMiniLMv2-L6-H384
hotchpotch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。
Basic profile indexed · More details to follow
Input Text→ Output Embedding
Price not verifiedCheck multimodal and channel pricing separately
View details ↗saihai-v1-8b-gguf
hotchpotch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。
Basic profile indexed · More details to follow
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗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。
Basic profile indexed · More details to follow
Input Text · Image→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗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。
Basic profile indexed · More details to follow
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗frankenmoe
hotdogs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。
Basic profile indexed · More details to follow
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗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。
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗No matching models
Adjust provider, category or input/output filters. New models are added continuously.
COST EXPLORER
Great models. Clear costs.
Adjust monthly token usage to compare estimated API costs.
1 M = 1 million tokens · USD
Compares base text-token rates within the same channel
Compares base text-token rates within the same channel
Uses collected, comparable USD-per-million-token rates. Snapshots exclude cache discounts, tiers, extra reasoning output and taxes. Check current source prices.
MODEL HANDBOOK
Understand before you choose
How should API prices be compared?+
Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.
Do open weights mean free commercial use?+
Not necessarily. Check each version’s license, commercial restrictions and policies. Self-hosting also incurs compute and maintenance costs.
Is the top-ranked model the best for me?+
Rankings depend on tasks, versions and methods. Compare accuracy, latency, reliability and costs on your own workload.