spamai
cybert79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。
基础资料已收录 · 缺失资料后续补充
输入 文本→ 输出 文本
价格待核验多模态与渠道费用须分别核对
查看详情 ↗DISCOVER & COMPARE
探索 AI 模型 84991
Dolphin-Mistral-24B-Venice-Edition-mlx-4Bit
cyboghostginx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。
基础资料已收录 · 缺失资料后续补充
输入 文本→ 输出 文本
价格待核验多模态与渠道费用须分别核对
查看详情 ↗Dolphin-X1-8B-mlx-4Bit
cyboghostginx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。
基础资料已收录 · 缺失资料后续补充
输入 文本→ 输出 文本
价格待核验多模态与渠道费用须分别核对
查看详情 ↗restore_photo_v4.1-lora
cyburn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。
基础资料已收录 · 缺失资料后续补充
输入 图像→ 输出 图像
价格待核验多模态与渠道费用须分别核对
查看详情 ↗restore_photo_v4-lora
cyburn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。
基础资料已收录 · 缺失资料后续补充
输入 文本→ 输出 图像
价格待核验多模态与渠道费用须分别核对
查看详情 ↗CmdCaliper-base
CyCraftAI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。
输入 文本→ 输出 向量
价格待核验多模态与渠道费用须分别核对
查看详情 ↗PLLuM-12B-base-2512
CYFRAGOVPL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。
基础资料已收录 · 缺失资料后续补充
输入 文本→ 输出 文本
价格待核验多模态与渠道费用须分别核对
查看详情 ↗PLLuM-12B-chat-2412
CYFRAGOVPL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。
输入 文本→ 输出 文本
价格待核验多模态与渠道费用须分别核对
查看详情 ↗PLLuM-12B-chat-2512
CYFRAGOVPLPLLuM is a family of large language models (LLMs) specialized in Polish with additional English data incorporated for broader generalization. Developed through an extensive collaboration with various data providers, PLLuM models are built on high-quality text corpora and refined through instruction tuning, preference learning, and advanced alignment techniques. These models are intended to generate contextually coherent text, offer assistance in various tasks (e.g., question answering, summarization), and serve as a foundation for specialized applications such as domain-specific intelligent assistants. In 2024, PLLuM models were developed by the PLLuM consortium. Since 2025, their development has continued under HIVE AI, a broader alliance of research institutions and organizations delivering digital public services, focused on open language technologies for Polish public administration. - **Extensive Data Collection** We gathered large-scale, high-quality text data in Polish and English, focusing on rigorous cleaning and deduplication to ensure the highest training standards. - **Organic Instruction Dataset** We curated the largest Polish collection of manually created “organic instructions”. This human-authored instruction set is based on an extensive typology of human-model interactions and it covers a range of subtle aspects of supervised fine-tuning (SFT) that might be overlooked with automated approaches (including large scale distillation of 'strong LLMs'). It was also designed to mitigate negative linguistic transfer from non-Polish textual data used in the pre-training phase.
译文排队中 · 当前显示来源原文
输入 文本→ 输出 文本
价格待核验多模态与渠道费用须分别核对
查看详情 ↗PLLuM-12B-instruct-2512
CYFRAGOVPL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。
基础资料已收录 · 缺失资料后续补充
输入 文本→ 输出 文本
价格待核验多模态与渠道费用须分别核对
查看详情 ↗PLLuM-4B-chat-2512
CYFRAGOVPL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。
基础资料已收录 · 缺失资料后续补充
输入 文本→ 输出 文本
价格待核验多模态与渠道费用须分别核对
查看详情 ↗PLLuM-4B-instruct-2512
CYFRAGOVPL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。
基础资料已收录 · 缺失资料后续补充
输入 文本→ 输出 文本
价格待核验多模态与渠道费用须分别核对
查看详情 ↗Quantizations of trohrbaugh/Qwen3.8-27B-heretic-ara with **MTP (Multi-Token Prediction) tensors preserved** (866 tensors) and **vision projector (mmproj)** for multimodal image understanding. Includes **ROCmFPX quants** for the ROCmFPX fork of llama.cpp — **~42 t/s average decode** on AMD Strix Halo (50–75% faster than stock Vulkan). trohrbaugh/Qwen3.8-27B-heretic-ara — Arbitrary-Rank Ablation (ARA) uncensoring of Qwen3.8-27B using heretic v1.2.0+custom. Parameters: start_layer 26, end_layer 56, preserve_good_behavior_weight 0.9432, steer_bad_behavior_weight 0.0009. KL divergence 0.0535, 0/100 refusals.
译文排队中 · 当前显示来源原文
输入 文本 · 图像→ 输出 文本
价格待核验多模态与渠道费用须分别核对
查看详情 ↗IQ4_XS quantization of Qwen3.8-Flash-Next abliterated with Heretic v1.3.0+custom (fork), with NGQ4-compressed n-gram prediction table. Built and benchmarked on AMD Strix Halo (Ryzen AI Max+ 395, gfx1151, 128 GB unified). Quantized from the Heretic2 BF16 safetensors → Q8_0 intermediate → IQ4_XS using the llama-flash-next fork of llama.cpp (build 10656, commit 035e22731). The NGQ4 step compresses the 54 GB per-layer n-gram prediction table to ~27 GB by lowering precision on the indexer's token embedding, reducing the model from 116 GB to 92 GB with no measurable accuracy impact through 32K context. Requires the llama-flash-next fork (qwen4exp branch) — standard llama.cpp cannot load the qwen4exp architecture.
译文排队中 · 当前显示来源原文
输入 文本 · 图像→ 输出 文本
价格待核验多模态与渠道费用须分别核对
查看详情 ↗First working GGUF build of Qwen3.8-Flash-Next (`qwen4exp` architecture) with vision, running on stock llama.cpp — no custom tensor formats or forked runtime required. Built from orcarouter/Qwen3.8-Flash-Next-Uncensored, the abliterated (uncensored) release of Qwen's newest hybrid architecture. ⚠️ **Research artifact.** Refusal behaviour has been removed from the source model. This does not add capability — it removes guardrails. Use it deliberately, in a context where that is appropriate, and own the output. **5.61 BPW average, 98.4 GB total.** The n-gram table alone is ~29 GB of that at Q4_0 — it's lookup-only (hash → embedding), so it tolerates low-bit quantization well, but it needed a separate streaming dequant/requant pass (chunked, ~2M rows at a time) because standard `llama-quantize` can't process its unusual 160-wide row shape and would need ~200+ GB to dequantize it in one pass.
译文排队中 · 当前显示来源原文
输入 待核验→ 输出 待核验
价格待核验多模态与渠道费用须分别核对
查看详情 ↗GGUF quantizations of philbert440/Qwen3.8-27B-Uncensored-Cyber, calibrated on an importance matrix built from **real agentic-coding traffic** rather than generic English prose. **All model weights and behaviour are philbert440's work. This repository contributes only the quantization and the calibration methodology** — no fine-tuning, no merging, no behavioural change of any kind was performed here. Inherited from upstream, and stated plainly so nobody is surprised: this is a **de-refusal ("abliterated") model specialized for the cyber/offensive-security domain**. The vision tower and the MTP speculative-decoding head are preserved upstream, which is what makes the multimodal path and the MTP graft work at all. Read the base model's card before using it — it documents the recipe and the evaluation.
译文排队中 · 当前显示来源原文
输入 文本 · 图像→ 输出 文本
价格待核验多模态与渠道费用须分别核对
查看详情 ↗stable-diffusion-v1-5
cym256413987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。
基础资料已收录 · 缺失资料后续补充
输入 文本→ 输出 图像
价格待核验多模态与渠道费用须分别核对
查看详情 ↗qwen-demo-test
cynthiaa-saab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。
基础资料已收录 · 缺失资料后续补充
输入 文本→ 输出 文本
价格待核验多模态与渠道费用须分别核对
查看详情 ↗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。
输入 文本→ 输出 文本
价格待核验多模态与渠道费用须分别核对
查看详情 ↗CPH-Community-7B
CypherTroopersA compact 7B-model fine-tuned for **Cypherium blockchain operations**, validator support, node configuration, RPC troubleshooting, and general-purpose lightweight reasoning. This model is optimized for **CPU-only inference** using `llama.cpp` and provides fast responses on low-resource servers such as VPS instances (2–6 vCPUs, 8–16GB RAM). - **Base model:** Qwen2-7B - **Fine-tuning:** QLoRA - **Domain:** Cypherium blockchain RPC, node operations, validator troubleshooting - **Format:** GGUF (`Q4_K_M`) - **Intended use:** lightweight on-device assistant for Cypherium node operators
译文排队中 · 当前显示来源原文
输入 文本→ 输出 文本
价格待核验多模态与渠道费用须分别核对
查看详情 ↗QwerySmith-1.0-Merged
Cyrax321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。
基础资料已收录 · 缺失资料后续补充
输入 文本→ 输出 文本
价格待核验多模态与渠道费用须分别核对
查看详情 ↗trocr-handwritten-cyrillic
cyrillic-trocr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。
输入 待核验→ 输出 待核验
价格待核验多模态与渠道费用须分别核对
查看详情 ↗Qwen3.6-27B-A2.8B
Cyronius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。
基础资料已收录 · 缺失资料后续补充
输入 文本→ 输出 文本
价格待核验多模态与渠道费用须分别核对
查看详情 ↗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。
输入 待核验→ 输出 待核验
价格待核验多模态与渠道费用须分别核对
查看详情 ↗暂未找到匹配模型
可以调整厂商、分类或输入输出条件；新模型正持续收录。
COST EXPLORER
好模型，也要算好成本。
调整月度 Token 用量，直观比较不同模型的预估 API 开支。
1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价
仅比较同一渠道的基础文本 Token 报价
使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。
MODEL HANDBOOK
读懂模型，再做选择
API 价格应该怎么比较？+
先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。
开放权重，就代表可以免费商用吗？+
不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。
排行榜第一，就是最适合我的模型吗？+
榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。