MODEL PROFILE

moziAI-35B-A3B-MOE-MTP

chenyumo · moziAI-35B-A3B-MOE-MTP

English | 简体中文 | 繁體中文 | 日本語 | 한국어 | हिन्दी | Deutsch | Français | Nederlands | Italiano | Русский | Español | Português | العربية | Bahasa Indonesia | Türkçe | Tiếng Việt | Polski MoziAI-35B-V3.8 is a locally deployable open-source multimodal AI large model developed by the team of Chen Yumo, a leading Chinese finance influencer. Built on the open-source base **Ornith-1.5-35B-A3B** (Qwen3.5-35B-A3B / Qwen3.6-35B-A3B architecture, MoE 35B, MIT + Apache-2.0 (Qwen3.5/3.6 & Gemma 4 components) license), it integrates the team's self-developed financial data + financial domain capabilities + dynamic seven-dimensional thinking framework + agent LOOP reflection and iteration mechanism + Uncensored characteristic + MoziSmartBit hybrid quantization algorithm. **💡 Size Advantage: only 15.9 GB** — the 35B-parameter MoE model is compressed to just **15.9 GB** via the self-developed MoziSmartBit quantization (about 30% smaller than standard Q4_K_M ~22GB). It fits in a single installer, runs on ordinary consumer GPUs (20GB VRAM+), reduces cloud token costs to **zero**, enables 7×24 hour token freedom, and ensures local data privacy and security. Licensed for **free commercial use** — zero barrier for individuals and enterprises.

Text generationlicense:apache-2.0
Context windowNot disclosed
Input → OutputText → Text
Model weightsNot verified
Profile updated2026/11/2 19:15:51
OVERVIEW

Model overview

English | 简体中文 | 繁體中文 | 日本語 | 한국어 | हिन्दी | Deutsch | Français | Nederlands | Italiano | Русский | Español | Português | العربية | Bahasa Indonesia | Türkçe | Tiếng Việt | Polski MoziAI-35B-V3.8 is a locally deployable open-source multimodal AI large model developed by the team of Chen Yumo, a leading Chinese finance influencer. Built on the open-source base **Ornith-1.5-35B-A3B** (Qwen3.5-35B-A3B / Qwen3.6-35B-A3B architecture, MoE 35B, MIT + Apache-2.0 (Qwen3.5/3.6 & Gemma 4 components) license), it integrates the team's self-developed financial data + financial domain capabilities + dynamic seven-dimensional thinking framework + agent LOOP reflection and iteration mechanism + Uncensored characteristic + MoziSmartBit hybrid quantization algorithm. **💡 Size Advantage: only 15.9 GB** — the 35B-parameter MoE model is compressed to just **15.9 GB** via the self-developed MoziSmartBit quantization (about 30% smaller than standard Q4_K_M ~22GB). It fits in a single installer, runs on ordinary consumer GPUs (20GB VRAM+), reduces cloud token costs to **zero**, enables 7×24 hour token freedom, and ensures local data privacy and security. Licensed for **free commercial use** — zero barrier for individuals and enterprises.

Key features

特点摘要待依据来源补齐,不凭模型名称推断能力。

Limitations & considerations

Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.

PRICING & COST

Source prices & cost analysis

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Source / channelInputOutputBilling unitCollected at
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Estimate monthly base token costs

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Estimates exclude cache discounts, tiered pricing, multimodal surcharges, web search, taxes and additional reasoning output.

INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

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Frequently asked questions

Do open weights permit free commercial use?

Not necessarily. Check the version license, commercial conditions and usage policies. Self-hosting incurs compute and maintenance costs.

Are these real-time billing prices?

These are timestamped source snapshots, not live billing prices. Check the channel for current versions, regions, discounts and billing methods.

Sources & traceability

许可证:apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。