Qwen3.8-27B-SLQ-EAR99
kaitchup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 ↗DISCOVER & COMPARE
Explore AI models 85001
Qwen3.8-27B-SLQ-GGUF
kaitchupThis is Qwen/Qwen3.8-27B, compressed with a **customized version of SLQ (Statistically-Lossless Quantization)** and assembled as GGUF. The search combines existing quantized donor weights and chooses a source separately for 498 complete matrices, including embeddings and the output head. The filename suffixes **Q5_K_S**, **Q5_K_M**, and **Q6_K** are Hub compatibility and dominant-precision-family labels for these **custom SLQ allocations**. The search considered **UD-Q4_K_XL, UD-Q5_K_XL, UD-Q6_K_XL, Q4_0, Q8_0, and BF16** from unsloth/Qwen3.8-27B-GGUF, plus **IQ3_S** from ISTA-DASLab/Qwen3.8-27B-GSQ-RCO-GGUF. Identical tensor payload alternatives were canonicalized. The final three models select no GSQ donor tensors; its candidates remained eligible during search.
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Ornith-1.5-122B-DFlash2-GGUF
kaivossThis model was converted to GGUF format from `incoai/Ornith-1.5-122B-DFlash2` using upstream llama.cpp (`ead9f32`). This is a **DFlash 2 draft model** (0.9B, block-diffusion drafter) for `ornith-ai/Ornith-1.5-122B`. It is not a standalone model. It reuses the target's token embeddings and LM head at runtime, so load it with `-md` next to an Ornith-1.5-122B GGUF. The following quants are available: BF16, F16, F32, IQ3_M, IQ3_S, IQ4_NL, IQ4_XS, MXFP4_MOE, Q1_0, Q2_0, Q2_K, Q3_K, Q3_K_L, Q3_K_M, Q3_K_S, Q4_0, Q4_1, Q4_K, Q4_K_M, Q4_K_S, Q5_0, Q5_1, Q5_K, Q5_K_M, Q5_K_S, Q6_K, Q8_0, TQ1_0, TQ2_0
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 ↗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。
Basic profile indexed · More details to follow
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗DiffLlama-0.3B-handcut
kajuma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 ↗vits-ljs
kakao-enterprise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 Audio
Price not verifiedCheck multimodal and channel pricing separately
View details ↗vits-vctk
kakao-enterprise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 Audio
Price not verifiedCheck multimodal and channel pricing separately
View details ↗kf-deberta-base
kakaobank**금융도메인 벤치마크** | Model | FN-Sentiment (v1) | FN-Sentiment (v2) | FN-Adnews | FN-NER | KorFPB | KorFiQA-SA | KorHeadline | Avg (FiQA-SA 제외) | |:-------------------:|:-----------------:|:-----------------:|:---------:|:---------:|:---------:|:----------:|:-----------:|:-----------------:| | | ACC | ACC | ACC | F1-micro | ACC | MSE | Mean F1 | | | KLUE-RoBERTa (Base) | 98.26 | 91.21 | 96.34 | 90.31 | 90.97 | 0.0589 | 81.11 | 94.03 | | KoELECTRA (Base) | 98.26 | 90.56 | 96.98 | 89.81 | 92.36 | 0.0652 | 80.69 | 93.90 | | KF-DeBERTa (Base) | **99.36** | **92.29** | **97.63** | **91.80** | **93.47** | **0.0553** | **82.12** | **95.27** | **범용도메인 벤치마크** | Model | NSMC | PAWS | KorNLI | KorSTS | KorQuAD | Avg (KorQuAD 제외) | |:-------------------:|:---------:|:---------:|:---------:|:---------:|:---------------:|:----------------:| | | ACC | ACC | ACC | spearman | EM/F1 | | | KLUE-RoBERTa (Base) | 90.47 | 84.79 | 81.65 | 84.40 | 86.34/94.40 | 85.33 | | KoELECTRA (Base) | 90.63 | 84.45 | 82.24 | 85.53 | 84.83/93.45 | 85.71 | | KF-DeBERTa (Base) | **91.36** | **86.14** | **84.54** | **85.99** | **86.60/95.07** | **87.01** | KF-DeBERTa의 소스코드 및 모델은 MIT 라이선스 하에 공개되어 있습니다. 라이선스 전문은 MIT 파일에서 확인할 수 있습니다. 모델의 사용으로 인해 발생한 어떠한 손해에 대해서도 당사는 책임을 지지 않습니다.
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗align-base
kakaobrainThe ALIGN model was proposed in "Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision" by Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc V. Le, Yunhsuan Sung, Zhen Li, Tom Duerig. ALIGN features a dual-encoder architecture with EfficientNet as its vision encoder and BERT as its text encoder, and learns to align visual and text representations with contrastive learning. Unlike previous work, ALIGN leverages a massive noisy dataset and shows that the scale of the corpus can be used to achieve SOTA representations with a simple recipe. The code for ALIGN was not publicly released, the base model is converted from the original implementation of the Kakao Brain team. This implementation follows the same architecture and hyperparameters as provided in the original Google model but is trained on the open source COYO dataset. Google’s ALIGN model, while trained on a huge dataset of 1.8 billion image-text pairs, cannot be replicated as the datasets is not public. Kakao Brain's ALIGN is on-par or outperforms Google ALIGN's reported metrics despite being trained on the much smaller, albeit carefully curated COYO-700M dataset. COYO is available on the hub as a dataset.
Input Not verified→ Output Not verified
Price not verifiedCheck multimodal and channel pricing separately
View details ↗kanana-1.5-15.7b-a3b-base
kakaocorp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 ↗kanana-1.5-15.7b-a3b-instruct
kakaocorp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 ↗kanana-1.5-2.1b-base
kakaocorp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 ↗kanana-1.5-2.1b-instruct-2505
kakaocorp`Kanana 1.5`, a newly introduced version of the Kanana model family, presents substantial enhancements in **coding, mathematics, and function calling capabilities** over the previous version, enabling broader application to more complex real-world problems. This new version now can handle __up to 32K tokens length natively and up to 128K tokens using YaRN__, allowing the model to maintain coherence when handling extensive documents or engaging in extended conversations. Furthermore, Kanana 1.5 delivers more natural and accurate conversations through a __refined post-training process__. Models MT-Bench KoMT-Bench IFEval HumanEval+ MBPP+ GSM8K (0-shot) MATH MMLU (0-shot, CoT) KMMLU (0-shot, CoT) FunctionChatBench > [!Note] > \* Models released under Apache 2.0 are trained on the latest versions compared to other models.
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗kanana-1.5-8b-base
kakaocorp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 ↗kanana-1.5-8b-instruct-2505
kakaocorp`Kanana 1.5`, a newly introduced version of the Kanana model family, presents substantial enhancements in **coding, mathematics, and function calling capabilities** over the previous version, enabling broader application to more complex real-world problems. This new version now can handle __up to 32K tokens length natively and up to 128K tokens using YaRN__, allowing the model to maintain coherence when handling extensive documents or engaging in extended conversations. Furthermore, Kanana 1.5 delivers more natural and accurate conversations through a __refined post-training process__. Models MT-Bench KoMT-Bench IFEval HumanEval+ MBPP+ GSM8K (0-shot) MATH MMLU (0-shot, CoT) KMMLU (0-shot, CoT) FunctionChatBench > [!Note] > \* Models released under Apache 2.0 are trained on the latest versions compared to other models.
Input Text→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗kanana-1.5-v-3b-instruct
kakaocorp- Kanana-1.5-v-3b-instruct - Intended Use - Model Details - Evaluation - Model Configuration Summary - Overview - Image Benchmarks (EN) - Image Benchmarks (KO) - Multimodal Instruction Following Benchmarks (EN, KO) - Note on Benchmarking Methodology - Usage - Requirements - Quickstart - Limitations - Contributors - Contact The Unified Foundation Model (UFO) task force of Kanana at Kakao developed and released the Kanana-V family of multimodal large language models (MLLMs), a collection of pretrained text/image-to-text (TI2T) models. kanana-1.5-v-3b-instruct is intended for research and application development in multimodal understanding and text generation tasks. Typical use cases include image captioning, document understanding, OCR-based reasoning, and multimodal instruction following in both English and Korean. The model is optimized for both general-purpose and Korea-specific benchmarks, making it suitable for bilingual environments.
Input Text · Image→ Output Text
Price not verifiedCheck multimodal and channel pricing separately
View details ↗kanana-2-1.3b-base
kakaocorp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 ↗kanana-2-1.3b-instruct
kakaocorp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 ↗kanana-2-30b-a3b-instruct
kakaocorp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 ↗kanana-2-30b-a3b-instruct-2601
kakaocorp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 ↗kanana-2-30b-a3b-thinking-2601
kakaocorp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 ↗kanana-2-3b-base
kakaocorp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 ↗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.