Head-to-Head Comparison
Llama vs Yi
Which Open Source Model is right for you? See our complete breakdown.
Feature Comparison
| Feature | Llama | Yi |
|---|---|---|
| MegaOne Score | 9/10 | 6/10 |
| Category | Open Source Model | Open Source Model |
| Pricing Model | Open Source | Enterprise |
| Starting Price | Free / Open Source | $199.00/mo |
| Free Tier | Yes | Yes |
| API Available | No | Yes |
| Open Source | No | Yes |
| iOS App | No | No |
| Android App | No | No |
| Chrome Extension | No | No |
| Company | Meta Platforms | 01.AI (Beijing Lingyi Wanwu Information Technology Co., Ltd.) |
| Total Funding | $2.3B | $300M |
Visual Comparison
Llama Yi
About Llama
Meta's family of open-weight large language models designed for various AI applications, from text generation to multimodal understanding.
Llama is a family of large language models (LLMs) developed by Meta AI, with the latest Llama 4 series, released in April 2025, featuring multimodal capabilities (text and image input, text output) and a Mixture-of-Experts (MoE) architecture for efficiency. These models are designed for a wide range of tasks including natural language processing, conversational AI, coding, and powering agentic systems, and are available in various sizes for diverse computational needs.
About Yi
01.AI develops open-source large language models and enterprise AI agent platforms, focusing on bilingual capabilities and cost-effectiveness.
01.AI, founded by Kai-Fu Lee, offers the Yi series of open-source large language models, including Yi-1.5, Yi-Coder, and Yi-VL, which are recognized for strong performance in language understanding, reasoning, and coding, particularly in English and Chinese. As of August 2026, the company is transitioning its focus to enterprise-level AI solutions with platforms like TrueNorth (万策) and Wanzhi (万智), while discontinuing its self-serve API platform.
The Verdict
Llama takes the edge
With a MegaOne score of 9/10 versus 6/10, Llama edges ahead of Yi in our analysis. However, Yi may still be the better choice depending on your specific use case and budget.
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