About 17; min
Open-source LLMs no longer come from a single region. China’s Qwen and Yi compete head-to-head with Meta’s Llama, and each represents different strengths shaped by their training data, target languages, and development philosophy. Here’s how Yi (01.AI), Qwen (Alibaba), and Llama (Meta) compare in 2026 — with special focus on multilingual performance and regional strengths.
Quick Comparison
| Spec | Yi | Qwen 2.5 | Llama 3.1 |
|---|---|---|---|
| Developer | 01.AI (China) | Alibaba (China) | Meta (USA) |
| Top Model | Yi-Lightning, Yi-Large (closed) / Yi 34B (open) | Qwen 2.5 72B | Llama 3.1 405B |
| Open Sizes | 6B, 9B, 34B | 0.5B, 1.5B, 3B, 7B, 14B, 32B, 72B | 8B, 70B, 405B |
| Context Window | 200K (Yi-200K) | 128K | 128K |
| License (open models) | Yi License (commercial allowed) | Apache 2.0 (most sizes) | Llama Community License |
| Languages | Strong English + Chinese | 29+ languages | 8 languages (English-focused) |
| Specialized Variants | Yi-Coder, Yi-VL | Qwen-Coder, Qwen-Math, Qwen-VL | CodeLlama (separate) |
Yi Overview
Yi from 01.AI (founded by Kai-Fu Lee) provides open-weight models in 6B, 9B, and 34B sizes. The Yi-34B model competes with Llama 70B on benchmarks despite being half the size. The Yi License allows commercial use with terms similar to Llama’s Community License. Yi excels at bilingual English-Chinese performance — both languages are first-class rather than secondary additions. Yi-200K extends the context window to 200,000 tokens, matching Claude. Yi-Coder targets programming tasks. Yi-VL adds vision capabilities. The closed-source Yi-Lightning (fast) and Yi-Large (capable) models are available through 01.AI’s API for production use cases requiring more capability than the open releases. For developers building bilingual applications or wanting strong performance from smaller open models, Yi delivers excellent capability per parameter.
Qwen 2.5 Overview
Alibaba’s Qwen 2.5 offers the widest size range among major open model families — 0.5B to 72B parameters. Apache 2.0 license (most sizes) is the most permissive available. Qwen 2.5 72B outperforms Llama 3.1 70B on most benchmarks despite the similar parameter count. Multilingual support across 29+ languages with best-in-class Chinese, Japanese, Korean, and Southeast Asian language performance. Specialized variants are deeply integrated: Qwen2.5-Coder leads coding benchmarks, Qwen2.5-Math handles mathematical reasoning, Qwen-VL processes images. The 7B and 14B sizes hit a sweet spot of capability and hardware accessibility — a 7B model that scores 74.2 on MMLU runs on any laptop with 16GB RAM. For multilingual applications and teams that need specific size targets, Qwen provides the most flexibility.
Llama 3.1 Overview
Meta’s Llama 3.1 comes in 8B, 70B, and 405B sizes. The 405B model is the largest open-weight model available, competing with GPT-4 class systems. The Llama Community License permits commercial use for organizations under 700M MAUs. Llama’s primary advantage is community — the largest fine-tuning community, the most third-party tool support, and the widest deployment options (Together AI, Groq, Fireworks, AWS Bedrock, Azure, GCP). The 8B model serves as a solid lightweight option, the 70B is the workhorse for production deployments, and the 405B handles tasks that require maximum capability. Multilingual support covers 8 languages well but doesn’t match Qwen’s 29-language breadth. For teams that prioritize community support, broad deployment options, and proven performance, Llama remains the safest open-source choice.
Benchmark Comparison
| Benchmark | Yi 34B | Qwen 2.5 72B | Llama 3.1 70B |
|---|---|---|---|
| MMLU | 76.3 | 85.3 | 82.0 |
| HumanEval | 76.8 | 86.6 | 80.5 |
| GSM8K | 85.8 | 91.6 | 83.7 |
| C-Eval (Chinese) | 83.1 | 87.3 | 62.5 |
| MT-Bench | 8.5 | 8.7 | 8.6 |
| Parameters | 34B | 72B | 70B |
Multilingual Performance
For Chinese-language tasks, the gap is substantial: Qwen scores 87.3 on C-Eval, Yi scores 83.1, and Llama scores 62.5. The 25-point gap between Llama and the Chinese models reflects training data composition — Qwen and Yi were trained with substantial Chinese content, while Llama focused primarily for English. For applications serving Chinese-speaking users, Qwen or Yi produce dramatically better results than Llama. Similarly, for Japanese, Korean, and Southeast Asian languages, Qwen leads. For pure English tasks, all three are competitive within 2-3% of each other.
Size and Hardware Considerations
Qwen’s wider size range provides more deployment flexibility. The 7B model runs on a 16GB laptop, the 14B fits a 24GB GPU, and the 32B targets workstation GPUs. Yi’s 6B-9B-34B range covers similar territory with fewer options. Llama’s 8B-70B-405B has a large gap between the small and large models — there’s no Llama option between 8B and 70B, while Qwen offers 14B and 32B in that gap. For teams whose hardware sits between consumer (8B-friendly) and datacenter (70B-capable), Qwen’s middle-sized models fill a niche the others don’t address.
Who Should Pick What
Pick Yi if:
- Bilingual English-Chinese applications are your primary use case
- You want strong performance from smaller models (Yi 34B competing with 70B class)
- The 200K context window with Yi-200K matches your long-document needs
- The Yi-Lightning closed model offers production-ready capability through API
Pick Qwen 2.5 if:
- Multilingual support across 29+ languages matters for your audience
- You need a specific model size between common options (14B, 32B)
- Apache 2.0 licensing provides maximum commercial flexibility
- Specialized variants (Coder, Math, VL) eliminate fine-tuning needs
Pick Llama 3.1 if:
- The largest fine-tuning community provides domain-specific variants
- Broad deployment options (every major cloud and inference provider) matter
- The 405B flagship handles tasks requiring maximum capability
- English-focused applications match Llama’s training emphasis
Qwen 2.5 wins for the best balance of capability, size flexibility, multilingual support, and licensing. The 72B leads benchmarks against Llama 70B, the smaller models excel in their size class, and Apache 2.0 simplifies commercial deployment. Llama 3.1 earns runner-up for the largest community, broadest tooling support, and the unique 405B flagship that has no Qwen equivalent. Yi performs admirably with smaller models and excellent bilingual capability — it’s the right choice for English-Chinese applications specifically. The open-source LLM field in 2026 offers genuinely competitive options across regions, and the right choice depends on your specific language requirements and deployment needs.
FAQ
Are Chinese models safe to use commercially?
Yes for Yi and Qwen — both have permissive licenses for commercial use. The licensing is comparable to Llama’s Community License. Companies should review specific license terms for any data residency or compliance considerations relevant to their jurisdiction.
Which is best for English-only applications?
Qwen 2.5 72B narrowly leads on English benchmarks, with Llama 3.1 70B close behind. The community around Llama (more fine-tunes, more tutorials) often makes it the practical choice for English-only deployments despite slightly lower benchmark scores.
Can I run these models locally?
Yes. All three families have Ollama support. ollama run qwen2.5, ollama run yi, or ollama run llama3.1 downloads and runs the respective models. The 7-9B sizes work on consumer hardware (16GB RAM minimum).
Which has the best small model?
Qwen 2.5 7B leads small-model benchmarks (MMLU 74.2, HumanEval 75.6). Llama 3.1 8B scores lower (MMLU 68.4) but has more fine-tuned variants available. Yi-9B falls between them. For pure capability per parameter, Qwen wins at the small end.
