> **Original model:** NaiveAI/Naive-N0.5-Flash by the > NaiveAI Team, MIT License. > This artifact is quantized from the public FP8 release > NaiveAI/Naive-N0.5-Flash-FP8. > It contains **modified weights** (EXL3 trellis quantization); the original model is © its authors. > Tokenizer and chat template are unchanged from the original. > Served by the veloGB10 engine it is listed by `/v1/models` as **`doth4580/Naive-N0.5-Flash-EXL3-3.5bpw`** (the `model_name:` line of > this card); `base_model:` is the original model it was quantized from, not this pack. An **EXL3 (ExLlamaV3 trellis) 3.5 bpw** quantization of **Naive-N0.5-Flash** — 309B parameters, 15.5B active, native 1M-token context. The pack is **~137 GB (~128 GiB) against ~315 GB for the FP8 original**, which is what lets it fit across **two NVIDIA DGX Spark (GB10)** machines. Quantized with the exllamav3 tooling, adapted for this architecture, run on **8×H100** for speed. It is served on two Sparks with the recipe in **sf-stav/Naive-N0.5-Flash-EXL3-2x-DGX-Spark** — see Serving below.

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Qwen3.8-Flash-Next-EXL3-3.05bpw

doth4580

> **Original model:** Qwen/Qwen3.8-Flash-Next by the Qwen > team, released under the Qwen Community License 1.0 — **not** an OSI license; see > License below. This artifact contains **modified weights** (EXL3 trellis quantization); > the original model is © Qwen. **Direct source:** turboderp/Qwen3.8-Flash-Next-exl3, branch/revision **`3.05bpw_h5_ng5`** — 3.05 bpw experts, 5-bit head, n-gram table included. An **EXL3 (ExLlamaV3 trellis) 3.05 bpw** quantization of **Qwen3.8-Flash-Next**: a 125B-parameter mixture-of-experts model with about 6B active parameters, plus a 51B hashed n-gram embedding and a 4B MTP layer, with a vision encoder. The pack is **~85 GB (~79 GiB) against ~360 GB for the original bf16 release**.

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Qwen3.8-Flash-Next-EXL3-4.05bpw

doth4580

> **Original model:** Qwen/Qwen3.8-Flash-Next by the Qwen > team, released under the Qwen Community License 1.0 — **not** an OSI license; see > License below. This artifact contains **modified weights** (EXL3 trellis quantization); > the original model is © Qwen. **Direct source:** turboderp/Qwen3.8-Flash-Next-exl3, branch/revision **`4.05bpw_h6_ng6`** (commit `55a732e0c4c3d4614bc42b68493bb930d9b02c0a`) — 4.05 bpw average (4-bit experts), 6-bit head, 6-bit n-gram table. An **EXL3 (ExLlamaV3 trellis) 4.05 bpw** quantization of **Qwen3.8-Flash-Next**: a 125B-parameter mixture-of-experts model with about 6B active parameters, plus a 51B hashed n-gram embedding and a 4B MTP layer, with a vision encoder. It is the higher-quality sibling of the 3.05 bpw pack: about **108 GB on disk** (68 GB of weights plus a 39 GB n-gram table) against ~360 GB for the original bf16 release.

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R3GE_Quen2.5_7B

dothisright

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。

分类待补充

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void.1

dotlabs

> - 🔹 best under 90m on sub-100m on open slm leaderboard! > - 🔸 void.2 paused to train better models focusing on stronger performance and usability. > - ▫️ step 900,000 updated with better performance, lr decayed to zero on ~10b additional bytes. State of the art, small language model pretrained from scratch on a diverse set of high-quality texts and an internal symbolic kernel. This is the first step into a series of models designed for fine-grained understanding of abstract/symbolic reasoning on text while being grounded on english. void.1 (24.38 II) achieves similar and often higher performance than palmer-006 (23.33 II) across all benchmarks and sets a new standard for future models.

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Tiel-Coder-35B-A3B-GGUF-MTP

dotnfc

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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Dots3-Note Preview (free)

dots-studio

Dots3-Note Preview is an open-weight mixture-of-experts model from Dots Studio, with 16B active parameters out of 280B total. It is the lightest model in the Dots 3 family and is...

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$0 / M 输入 TokenOpenRouter · dots-studio/dots-3-note-preview:free

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dots.llm1.base

dots-studio

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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COST EXPLORER

好模型，也要算好成本。

费用分析

调整月度 Token 用量，直观比较不同模型的预估 API 开支。

每月输入 Token 10 M每月输出 Token 2 M1 M = 100 万 Token · 美元计价
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

选型与常见问题

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。

找到适合你的 AI 模型。

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dots.llm1.base

暂未找到匹配模型

好模型，也要算好成本。

读懂模型，再做选择

资讯看趋势，工具提效率，模型筑可能。