# The Open-Source Landscape for Large Models: New Shifts and Strategies
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## Overview
During the recent National Day holiday, while many industries paused for a break, the **large model sector** continued evolving rapidly.
In the past two weeks alone, leading teams in China and abroad have launched **over ten major products**, each with distinct priorities.
Highlights include:
- **Late September:** Alibaba unveiled its all-in-one lineup centered around **Qwen3-Max**, showcasing its tech strength.
- **End of September:** DeepSeek and Anthropic targeted real-world programming scenarios, releasing **DeepSeek V3.2-Exp** and **Claude Sonnet 4.5**.
- **Zhipu AI:** Introduced **GLM-4.6**, positioning it as China's strongest coding model.
With some companies pursuing full-spectrum coverage and others focusing on niche excellence, **competition strategies are diverging**.
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## 1 — Ant's Bold Entry into the Trillion-Parameter Era
At midnight on **October 9**, Ant Group’s “Bailing” large model team announced **Ling-1T**, a natural language model boasting **1 trillion parameters (1000B)**.
Only ten days earlier, the team had open-sourced **Ring-1T-preview** — its self-developed trillion-parameter reasoning model.
**Two trillion-parameter models in ten days**
This aggressive pace has sparked intense industry discussions about Ant’s intentions.
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## 2 — Pushing the Limits of Intelligence
### The Bailing Philosophy
Ant Group named its foundational models “Bailing” (百灵) — meaning *success after a hundred tries*, symbolizing inclusive tech.
Development is handled by an independent team, similar to Alibaba’s approach.
**Series distinction:**
- **Ling-Series:** Non-reasoning models (MoE architecture)
- **Ring-Series:** Reasoning models
- **Ming-Series:** Native multimodal models
**Key milestone:** Both Ling and Ring models have now reached the **trillion-parameter scale**.
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### Why Trillion Parameters Matter
- Equivalent to **human brain neuron count**
- Very few domestic models reach this scale
- Other Chinese trillion-parameter models:
**Kimi K2**, **Qwen3-Max** (Alibaba), **Hunyuan** (Tencent)
Despite theories like *“data wall”* or *“end of pre-training”*, consensus is forming:
**More data + more parameters ⇒ sustained performance gains**
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## 3 — Industry Perspectives
- **Alibaba:** Algorithm chief Lin Junyang states scaling law “has yet to hit a ceiling”.
- **Kimi Team:** Unveiled flagship K2 after two months of work despite difficulties.
- **Tongyi Qianwen:** Adheres to “Just Scale It”.
**Conclusion:**
Leaders are still exploring the **upper limits of intelligence**.
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## 4 — Bailing's Rapid Model Rise
Ant’s dual releases signal:
1. Faster iteration cycles
2. Wider open-source adoption
3. Positioning in global high-performance model competition
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### Open Platforms Matter
Example: **[AiToEarn官网](https://aitoearn.ai)**
- Open-source *global AI content monetization* platform
- Supports multi-platform publishing: Douyin, Kwai, WeChat, Bilibili, Xiaohongshu, Facebook, Instagram, LinkedIn, Threads, YouTube, Pinterest, X (Twitter)
- Offers analytics + AI model rankings
- Bridges innovation with global audiences
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## 5 — Balancing Scale and Efficiency
### The Pareto Challenge
- Large models must balance **parameter size** and **inference speed**
- Ant’s Ling-1T shows strong benchmark performance — topping coding & math metrics
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### Inference Accuracy: AIME 25
Ling-1T beats **DeepSeek-V3.1-Terminus**, **Kimi-K2-Instruct-0905**, **GPT-5**, **Gemini-2.5-Pro**:
- Shorter reasoning paths
- Higher accuracy
- 128K long text window processing
- ~50B parameters activated per token
Training involved:
- **20 trillion** high-quality tokens
- *Ling Scaling Laws* for hyperparameter optimization
- **LPO strategy optimization** + hybrid reward mechanisms
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## 6 — Ling Model Family Overview
- **Scale:** 16B → 1T parameters
- **Multimodal:** Vision, audio, speech, graphics
- **Deployment modes:**
- *Ling-mini*: Mobile devices
- *Ling-flash*: SME server
- *Ling-1T*: Cloud
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**Community Impact**
- *Ming-lite-omni v1.5*: #1 on Hugging Face’s any-to-any chart
- *Ring-1T-preview*: #3 on Hugging Face’s text generation toplist
- **Yann LeCun:** Commented “Impressive”
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### Developer Feedback
Apple engineer after local quantization: *“Getting closer to GPT-5 at home”*.
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## 7 — Co-Building AGI Through Open Source
Ant’s **AI First** strategy aligns with:
- “Alipay’s dual flywheels”
- “Accelerating globalization”
Open source intention:
> Build a truly open AGI ecosystem via transparency and iteration.
Only fully open-source trillion-parameter models:
- Ling-1T
- Ring-1T-preview
- Kimi K2
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### Benefits of Open Source
- Ready-to-use starting points for complex applications
- Feedback loops accelerate intelligence emergence
- Democratizes technology access
**InclusionAI**: Ant's open-source org
- Full-stack large-model tech
- RL inference framework **AReaL**
- Multi-agent framework **AWorld**
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## 8 — Extending Impact to Creative Economies
Platforms like **[AiToEarn官网](https://aitoearn.ai)** can:
- Connect AI content generation → cross-platform publishing
- Integrate analytics & ranking
- Monetize outputs globally
These complement open-source ecosystems by turning model power into **sustainable creative economies**.
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## 9 — Flywheel Reversal in Robotics and AI
[Read Related](https://mp.weixin.qq.com/s?__biz=MzA5ODEzMjIyMA==&mid=2247726783&idx=1&sn=22fb998547c97e203038b1b3ef3066f3&scene=21#wechat_redirect)
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### Challenge
Agile component makers face:
- Demand gaps
- Rising R&D costs
- Pricing pressure from integrated AI robotics firms
**Opportunity:** Integrate high-performance computing to make components “AI-native”.
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### Cross-Ecosystem Thinking
Logic from platforms like **AiToEarn**:
- Connect outputs to global audiences
- Use analytics to identify niches
- Apply in robotics beyond traditional verticals
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