The open-weight AI landscape has never been more competitive. Qwen 2.5 72B (Alibaba's flagship open model), DeepSeek V3, and Llama 4 Maverick (Meta's latest) all achieve near-frontier performance that would have seemed impossible from open models just a year ago. DeepSeek V3 leads on raw benchmarks; Llama 4 offers the most permissive license; Qwen 2.5 excels at multilingual and coding tasks.
What Is an Open-Weight Model?
An "open-weight" model makes its trained model parameters publicly available for download. This is different from "open source" in the traditional sense — the training data and code may not be public, but the resulting model weights are freely downloadable and runnable on your own hardware.
This distinction matters because open weights give you data privacy (nothing leaves your servers), customizability (you can fine-tune), and deployment flexibility (run on any hardware) — without necessarily giving you the ability to reproduce the training process.
Benchmark Comparison: The Three Contenders
| Benchmark | Qwen 2.5 72B | DeepSeek V3 | Llama 4 Maverick | GPT-4o (reference) |
|---|---|---|---|---|
| MMLU | 86.1% | 88.5% | 85.5% | 87.2% |
| HumanEval (coding) | 86.6% | 82.6% | 85.5% | 90.2% |
| MATH | 83.1% | 90.2% | 79.5% | 76.6% |
| GSM8K | 91.2% | 89.3% | 88.1% | 91.6% |
| MBPP (Python coding) | 88.9% | 81.1% | 83.7% | 86.5% |
Qwen 2.5 72B: The Coding and Math Leader
Qwen 2.5 72B (developed by Alibaba Cloud) is the strongest open-weight model for coding tasks among this group. Its 86.6% on HumanEval and 88.9% on MBPP place it close to GPT-4o for Python coding tasks.
Qwen 2.5 also leads on Chinese language performance — significantly better than Llama or DeepSeek V3 for Chinese text tasks. This makes it the go-to choice for multilingual applications spanning English and Chinese.
Qwen 2.5 72B is available under Qwen's Research License, which permits commercial use with some restrictions for very large-scale deployments (over 100 million users).
DeepSeek V3: The All-Around Benchmark Leader
DeepSeek V3 is the most capable open-weight model on broad general benchmarks. Its 88.5% MMLU score and dominant 90.2% MATH score make it the best open model for general knowledge tasks and mathematical reasoning.
DeepSeek V3 uses a Mixture-of-Experts (MoE) architecture — it's technically a ~671B parameter model, but activates only ~37B parameters per forward pass. This makes inference substantially more efficient than its parameter count suggests.
The main practical concern with DeepSeek V3 is its Chinese origin. For US businesses with data sovereignty requirements, self-hosting DeepSeek V3 (rather than using their API) is advisable.
Llama 4 Maverick: The Most Permissive License
Llama 4 Maverick (released April 2025) is Meta's most capable model and has the most business-friendly license of the three. Meta's Llama license permits commercial use for virtually all organizations (except those with over 700 million monthly active users).
Llama 4 trails DeepSeek V3 and Qwen 2.5 on benchmarks, but the gap is small. More importantly, Meta's ecosystem support — integrations with PyTorch, Hugging Face, vLLM, and a massive fine-tuning community — makes Llama the easiest model to deploy, customize, and maintain.
Licensing Deep Dive
| Model | License | Commercial Use | Fine-Tuning Allowed | Redistribution |
|---|---|---|---|---|
| Llama 4 | Meta Llama 4 License | Yes (with exceptions) | Yes | Yes (with attribution) |
| DeepSeek V3 | DeepSeek License | Yes | Yes | Yes (with restrictions) |
| Qwen 2.5 72B | Qwen Research License | Yes (with exceptions) | Yes | Yes |
Deployment and Hardware Requirements
Running 70B+ parameter models requires significant GPU resources:
- Qwen 2.5 72B: ~80GB VRAM (2x A100 80GB) for FP16 inference. Can be quantized to ~40GB with minimal quality loss.
- DeepSeek V3: The full model requires ~320GB for FP16. Quantized versions run on smaller infrastructure. Best accessed via API.
- Llama 4 Maverick: ~80GB VRAM for FP16. Well-supported by vLLM, Ollama, and other inference frameworks.
API Providers for Open Models
If you want open-model quality without managing infrastructure, several providers offer hosted open models:
- Together.ai: Llama 4, Qwen 2.5, DeepSeek V3, competitively priced
- Fireworks AI: Fast inference, good reliability
- Groq: Extremely fast inference for smaller Llama models
- Hugging Face Inference API: Wide model selection, variable performance
- Deepest: Access open and closed models side by side for comparison
Which Open Model Should You Use?
| Use Case | Best Choice |
|---|---|
| General text tasks, broad knowledge | DeepSeek V3 |
| Python / coding tasks | Qwen 2.5 72B |
| Mathematics and quantitative reasoning | DeepSeek V3 |
| Chinese / English multilingual | Qwen 2.5 72B |
| Self-hosted with simple deployment | Llama 4 Maverick |
| Permissive license for commercial use | Llama 4 Maverick |
| Fine-tuning on proprietary data | Llama 4 (best ecosystem) |
Frequently Asked Questions
Are open-weight models as good as ChatGPT?
On benchmarks, the best open-weight models (DeepSeek V3, Qwen 2.5 72B) match or slightly exceed GPT-4o on several tests. In real-world use, closed models still have edges in instruction following, consistency, and handling unusual inputs. But the gap is small enough that for many tasks, open models are genuinely equivalent.
What's the difference between open-source and open-weight?
Open-source means code, data, and training methodology are fully public (OSI definition). Open-weight means the trained model parameters are downloadable, but training code and data may be proprietary. Most "open" AI models are open-weight, not fully open-source.
Can I use these models commercially?
Yes for most businesses. Each has specific restrictions — Llama 4 restricts companies with 700M+ MAU; Qwen has some enterprise restrictions. Read the specific license for each model before large-scale commercial deployment.
How do I run DeepSeek V3 if I want to self-host?
DeepSeek V3 in its full form requires substantial GPU infrastructure (~320GB VRAM). Quantized versions (Q4/Q5) can run on more modest hardware. Most self-hosting users access DeepSeek V3 through providers like Together.ai or Fireworks AI rather than truly self-hosting the full model.
