About 17; min
The open-source LLM race has produced two clear frontrunners from opposite sides of the world. Meta’s Llama 3.1 represents Silicon Valley’s bet on open AI, while Alibaba’s Qwen 2.5 has emerged as the strongest challenger from China’s AI labs. Both models are free to download and run locally, but their architecture, performance benchmarks, and licensing terms differ in ways that matter for production use.
In 2026, choosing between Llama and Qwen depends on your use case, language requirements, and deployment preferences. Here’s a detailed comparison of both model families.
Quick Model Overview
| Spec | Llama 3.1 | Qwen 2.5 |
|---|---|---|
| Developer | Meta (Facebook) | Alibaba Cloud |
| Model Sizes | 8B, 70B, 405B | 0.5B, 1.5B, 3B, 7B, 14B, 32B, 72B |
| Context Window | 128K tokens | 128K tokens (32K default) |
| License | Llama 3.1 Community License | Apache 2.0 (most sizes) |
| Training Data | 15T+ tokens (multilingual) | 18T+ tokens (multilingual) |
| Multilingual | 8 languages | 29+ languages |
| Coding Variant | CodeLlama (separate) | Qwen2.5-Coder (integrated) |
| Math Variant | Not specialized | Qwen2.5-Math (specialized) |
Llama 3.1 Overview
Meta’s Llama 3.1 comes in three sizes: 8B for lightweight tasks, 70B for strong general performance, and 405B as the open-source flagship competing with GPT-4 class models. All versions support 128K token context windows. The Llama 3.1 Community License allows commercial use for organizations with under 700 million monthly active users — effectively free for every company except the largest tech giants. Running locally via Ollama, the 8B model needs 8GB VRAM, 70B needs 48GB, and 405B requires multi-GPU setups. API access is available through Together AI ($0.18/M tokens for 8B), Groq, Fireworks, and AWS Bedrock. Llama 3.1 70B consistently ranks among the top 3 open-source models on LMSYS Chatbot Arena and HumanEval coding benchmarks.
Qwen 2.5 Overview
Alibaba’s Qwen 2.5 offers the widest range of model sizes — from 0.5B (runs on a phone) to 72B (competitive with Llama 70B). Most sizes use the Apache 2.0 license, the most permissive open-source license available — no usage restrictions, no MAU limits. The 72B model uses a custom Qwen license that’s still commercially permissive. Qwen 2.5 supports 29+ languages with particularly strong Chinese, Japanese, Korean, and Southeast Asian language performance. Specialized variants include Qwen2.5-Coder (coding), Qwen2.5-Math (mathematical reasoning), and Qwen-VL (vision-language). Running locally via Ollama, the 7B model needs 6GB VRAM, 14B needs 12GB, 32B needs 24GB, and 72B needs 48GB. API access through Alibaba Cloud starts at $0.14/M tokens for smaller models.
Performance Comparison
| Benchmark | Llama 3.1 70B | Qwen 2.5 72B |
|---|---|---|
| MMLU (general knowledge) | 82.0 | 85.3 |
| HumanEval (coding) | 80.5 | 86.6 |
| GSM8K (math) | 83.7 | 91.6 |
| MATH (advanced math) | 47.2 | 55.2 |
| MT-Bench (conversation) | 8.6 | 8.7 |
| ARC Challenge (reasoning) | 87.3 | 88.5 |
| Multilingual (avg) | Good (8 languages) | Excellent (29+ languages) |
| Chinese performance | Moderate | Best-in-class |
| English performance | Excellent | Excellent |
Smaller Model Comparison
| Benchmark | Llama 3.1 8B | Qwen 2.5 7B |
|---|---|---|
| MMLU | 68.4 | 74.2 |
| HumanEval | 62.2 | 75.6 |
| GSM8K | 56.4 | 82.6 |
| VRAM Required | ~8GB | ~6GB |
| Ollama Download | 4.7GB (Q4) | 4.4GB (Q4) |
| Speed (tokens/sec, M2) | ~35 t/s | ~38 t/s |
Running Locally
Ollama Setup
Both models run through Ollama with a single command. Install Ollama, then pull and run:
Llama 3.1: ollama run llama3.1 (8B default) or ollama run llama3.1:70b
Qwen 2.5: ollama run qwen2.5 (7B default) or ollama run qwen2.5:72b
Quantized versions (Q4_K_M) reduce VRAM requirements by 50-60% with minimal quality loss. Both models support function calling, JSON mode, and system prompts through Ollama’s API. For developers building local AI applications, both models expose an OpenAI-compatible API endpoint at localhost:11434.
Hardware Requirements
| Model | VRAM Needed | Recommended GPU |
|---|---|---|
| Qwen 2.5 7B / Llama 3.1 8B | 6-8GB | RTX 3060 12GB, M1/M2 16GB |
| Qwen 2.5 14B | 12GB | RTX 4070 12GB, M2 Pro 16GB |
| Qwen 2.5 32B / Llama 3.1 70B (Q4) | 24-48GB | RTX 4090, M2 Ultra 64GB |
| Qwen 2.5 72B / Llama 3.1 70B | 48GB+ | A100 80GB, 2x RTX 4090 |
Licensing
Qwen 2.5’s Apache 2.0 license (for models up to 32B) is the most permissive option — use it commercially, modify it, redistribute it, no restrictions. Llama 3.1’s Community License is permissive for most businesses but includes a 700 million MAU threshold above which you need Meta’s explicit permission. For startups and mid-size companies, both licenses are effectively unrestricted. For large platforms (social networks, search engines), Qwen’s Apache 2.0 provides more legal certainty.
Specialized Variants
Qwen 2.5 offers purpose-built variants that Llama lacks in its core family. Qwen2.5-Coder is trained specifically on code and scores above GPT-4o on several coding benchmarks at the 32B size. Qwen2.5-Math handles mathematical reasoning with chain-of-thought prompting. Qwen-VL processes images alongside text for visual question answering and document understanding. Llama’s coding capabilities come through CodeLlama (a separate model family), and Meta has not released specialized math or vision variants under the Llama 3.1 banner. For teams that need coding or math capabilities from a single model family, Qwen’s integrated variants are more convenient.
Who Should Pick What
Pick Llama 3.1 if:
- You primarily work in English and want proven performance with the largest community
- The 405B model gives you near-GPT-4 quality for tasks that need maximum capability
- Meta’s backing and the largest fine-tuning community matter for long-term support
- Your tooling and infrastructure already supports Llama model formats
Pick Qwen 2.5 if:
- You need strong multilingual support beyond English (especially CJK languages)
- Coding and math specialized variants are important for your use case
- Apache 2.0 licensing provides the legal clarity your business requires
- The wider range of model sizes (0.5B to 72B) lets you pick the exact right fit
Qwen 2.5 wins on benchmark performance, model size variety, licensing permissiveness, and specialized variants. The 72B model outperforms Llama 3.1 70B on most benchmarks, and the smaller models (7B, 14B, 32B) offer more granular choices for different hardware constraints. Llama 3.1 earns runner-up for its massive community, the 405B flagship model that has no Qwen equivalent, and the strongest community of fine-tunes, tools, and tutorials. Both model families are excellent — the open-source LLM space benefits enormously from this competition.
FAQ
Can I run these models on a MacBook?
Yes. Both 7-8B models run well on M1/M2 MacBooks with 16GB RAM through Ollama. The 14B Qwen model works on 16GB Macs with quantization. Larger models (70B+) need M2 Ultra or dedicated GPUs.
Which is better for coding?
Qwen2.5-Coder outperforms both base Qwen and Llama on coding benchmarks. At the 32B size, it matches or exceeds GPT-4o on HumanEval and MBPP. For coding specifically, Qwen has the edge.
Are these models as good as ChatGPT?
The 70B+ models approach GPT-4o quality on many tasks. For general conversation and instruction following, they’re 85-95% as capable. Where they fall short: complex multi-step reasoning, very long context tasks, and tasks requiring real-time information.
Which has more fine-tuned variants available?
Llama has the larger fine-tuning community with thousands of variants on Hugging Face. Qwen’s community is growing rapidly, especially for multilingual and coding fine-tunes. Both models are supported by all major fine-tuning tools (LoRA, QLoRA, Axolotl).
