Kimi-K3-GGUF
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查看详情 ↗DISCOVER & COMPARE
探索 AI 模型 87
Laguna-S-2.1-GGUF
AtomicChat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。
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查看详情 ↗Laguna-XS-2.1-GGUF
AtomicChat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。
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查看详情 ↗Laguna-XS-2.1-MLX-8bit
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查看详情 ↗LFM2.5-24B-A2B-GGUF
AtomicChat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。
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查看详情 ↗LFM2.5-24B-A2B-Uncensored-GGUF
AtomicChatLiquid's refusal behaviour projected out of the original weights, in the second two thirds of the network only. Shipped two ways: six baked GGUF builds, and a 24 MB LoRA adapter for the LFM2.5-24B-A2B quants you already have. The measurements behind this card are public. Built by the team behind Atomic Chat , the app that runs open-weight models locally and offline. On held-out prompts, refusals fall from 96.3% to 1.2% in English, and from 30 of 30 to none with thinking. MMLU stays where it was: 64.40% before, 65.55% after (inside the noise). No empty or looping replies. Only blocks 14-39 are edited. The same edit across all 40 blocks also removed the refusals, but cost 4 MMLU points; the first 14 blocks carry the damage, not the refusal. Already running our LFM2.5-24B-A2B quants ? Add the adapter with --lora-scaled instead of downloading a new model. **`AD-Q4_K_M` is the one to take** on a 24 GB machine. With 32 GB, `AD-Q6_K` is nearly as close to the original as `AD-Q8_0` at 5 GB less; `AD-IQ3_M` is the smallest build here. Refusals were measured on `AD-Q8_0` and `AD-Q4_K_M`; neither gave an empty or looping reply. A smaller `AD-IQ3_XXS` (11.1 GB) was made too and is held back: it matched the original's token choice only 74.5% of the time and refused 3.7% of English prompts.
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查看详情 ↗LFM3-1.7B-GGUF
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查看详情 ↗LFM3-1.7B-Uncensored-GGUF
AtomicChat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。
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查看详情 ↗LFM3-Audio-2B-GGUF
AtomicChat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。
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查看详情 ↗LFM3-Omni-2B-GGUF
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查看详情 ↗LFM3-VL-2B-GGUF
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查看详情 ↗Ling-3.0-flash-GGUF
AtomicChatLing 3.0 flash is a 124B MoE with 5.1B active parameters; its GGUF chat template includes thinking mode and tool calling. Our AD layouts have 31–41% lower mean KL than stock llama.cpp presets at comparable sizes. The raw measurements and controls are public. From the team behind Atomic Chat , the local AI app for chat and agents. These Ling files require our TurboQuant build; use the CLI instructions below . The tests documented below cover the CLI workflow, not app compatibility. Quantizations of `inclusionAI/Ling-3.0-flash`: 124B total, 5.1B active, hybrid linear attention (35 KDA blocks interleaved 5:1 with 7 gated MLA blocks) over a 512-expert MoE. Bits are placed by hand rather than by the default rules, and the controls that prove it is worth something are published next to the files. At the same size, our layout sits 31 to 41 % closer to BF16 than what `llama-quantize` produces on its own
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查看详情 ↗Ling-3.0-flash-NVFP4-GGUF
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查看详情 ↗Ling-3.0-flash-Sante-GGUF
AtomicChatBuilt from inclusionAI's original weights with our own importance matrix. The calibration corpora behind our builds are public. You can now run Ling in Atomic Chat . See our quantization analysis below for measurements and instructions. Every number below is measured, not estimated. How we measured it is at the bottom, and the raw logs are in the metrics repo so you can check any of it yourself.
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查看详情 ↗Ling-3.0-flash-Sante-Uncensored-GGUF
AtomicChat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。
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查看详情 ↗Ling-3.0-flash-VL-GGUF
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查看详情 ↗Ling-3.1-flash-GGUF
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查看详情 ↗Ling-3.1-flash-Uncensored-GGUF
AtomicChat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。
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查看详情 ↗MiniCPM-V-4.7-1B-GGUF
AtomicChatBuilt from openbmb's original weights with our own importance matrix. The calibration corpora behind our builds are public. You can now run MiniCPM in Atomic Chat . See our quantization analysis below for measurements and instructions. Every number below is measured, not estimated. How we measured it is at the bottom, and the raw logs are in the metrics repo so you can check any of it yourself.
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查看详情 ↗MiniCPM-V-4.7-35B-A3B-GGUF
AtomicChatBuilt from openbmb's original weights with our own importance matrix. The calibration corpora behind our builds are public. You can now run MiniCPM in Atomic Chat . See our quantization analysis below for measurements and instructions. Every number below is measured, not estimated. How we measured it is at the bottom, and the raw logs are in the metrics repo so you can check any of it yourself.
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查看详情 ↗MiniCPM-V-4.7-35B-A3B-Uncensored-GGUF
AtomicChatOne refusal direction projected out of OpenBMB's original weights, shipped two ways: three baked GGUF builds, and a 106 MB LoRA adapter for the MiniCPM-V 4.7 quants you already have. The measurements behind this card are public. On held-out prompts, refusals fall from 96.3% to 0% in English, and from 13 of 30 to none with thinking on. MMLU moves by 0.25 points, inside the noise. You can now run the Uncensored MiniCPM-V 4.7 in Atomic Chat , locally on your own machine. The 20.1 GB build matches the original's token choice 94.5% of the time; our quant of the unmodified model at the same size does 94.9%. Already running our MiniCPM-V 4.7 quants ? Add the adapter with --lora-scaled instead of downloading a new model. *28 Oct 2026: each build is now a single file instead of 11 to 16 shards of 2 GB. The weights are unchanged (tensor-for-tensor identical).*
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查看详情 ↗MiniMax-M3.1-Flash-GGUF
AtomicChat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。
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价格待核验多模态与渠道费用须分别核对
查看详情 ↗Muse-Glimmer-30B-GGUF
AtomicChatGGUF builds of Meta's Muse Glimmer 30B, made from the original BF16 weights with a calibrated importance matrix and a bit layout chosen by measurement. > [!NOTE] > Support was merged into llama.cpp master in > #26841 (commit `62bf73d`). > You need a build from master — the last tagged release predates it. > [!TIP] > `AD-Q6_K` at 28.3 GB is dominated by plain `Q8_0` at 29.6 GB, which is 1.3 GB larger > and 31 % more accurate. Take `Q8_0` instead unless you need exactly that size.
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查看详情 ↗Muse-Spark-Open-816B-A42B-GGUF
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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。