The two most important open-weight AI models in 2026 are Meta's Llama 4 Maverick and DeepSeek V3. Both are free to download and commercially usable. Both use Mixture-of-Experts (MoE) architecture. Both achieve near-frontier performance at open-source cost. For Indian developers and researchers deciding which to build on — for production applications, research projects, or portfolio work — the choice matters significantly. This guide gives you the complete, architecture-to-use-case comparison to make that decision correctly.
Architecture Comparison
| Architectural Property | Llama 4 Maverick | DeepSeek V3 |
|---|---|---|
| Total parameters | 400B (128 experts, 17B active/token) | 671B (MoE, 37B active/token) |
| Context window | 1M tokens (Maverick); 10M (Scout) | 128K tokens |
| Native multimodality | Yes — text, image, video via early fusion | Text only |
| Architecture type | MoE — similar to DeepSeek V3 in sparsity | MoE with shared + routed experts + MLA |
| Licence | Meta Llama 4 licence — free under 700M MAU | MIT licence — fully open, no user limits |
Performance: Where Each Wins
| Task | Llama 4 Maverick | DeepSeek V3 |
|---|---|---|
| Coding tasks | Excellent — code-heavy training data, matches GPT-5.3 | Excellent — near-frontier, especially maths-adjacent code |
| Multimodal (image/video) | Wins — native multimodal, 73.4% MMMU | Not available — text only |
| Maths and reasoning | Strong but trails on hardest reasoning | Very strong — extended thinking mode, transparent chain-of-thought |
| Long context handling | Wins — 1M tokens (Maverick), 10M (Scout) | 128K context — significant limitation |
| Hindi language quality | Official support — better quality | Moderate Hindi quality |
| API pricing (cloud) | $0.19–$0.49 per million blended tokens | $0.27 per million input tokens (via API) |
| Free web access | via WhatsApp Meta AI (Llama 4 only, limits) | chat.deepseek.com — no account, no limits |
Licence Differences: Why They Matter for Indian Developers
DeepSeek V3's MIT licence is genuinely more open than Llama 4's licence. MIT allows use, modification, and distribution without any restriction beyond attribution. Llama 4's licence restricts use for companies with over 700 million monthly active users (requiring a special Meta licence) and prohibits certain uses of the model. For most Indian developers and organisations, the Llama 4 restriction is not practically relevant. But for any company with ambitions to reach large scale, or for any government/institutional deployment with legal concerns about commercial licence terms, DeepSeek V3's MIT licence is cleaner.
Data Privacy: The Critical Consideration
For applications involving sensitive Indian user data, DeepSeek's Chinese ownership is the most important factor in the choice. Both models can be downloaded and deployed on-premise. But if you use DeepSeek's cloud API at api.deepseek.com, your queries pass through Chinese-controlled servers. If you use Llama 4 via Meta's ecosystem, through together.ai, or through Groq, your data stays in US/EU data centres. For healthcare AI, fintech applications, or government-adjacent projects with Indian DPDP Act compliance requirements, Llama 4 through US cloud providers is the safer choice.
The Decision Framework
- Build on Llama 4 if — Your application requires image or video understanding; you need a context window beyond 128K; you want official Hindi language support; your application will be embedded in Meta's platforms; or data sovereignty requirements point toward US/EU infrastructure.
- Build on DeepSeek V3 if — Your application is text-only; you need the cleanest open-source licence (MIT); your use case benefits from extended chain-of-thought reasoning; you want the cheapest possible API access; or you are deploying on-premise with hardware you already have.
- Use both if — You need multimodal capability from Llama 4 for vision tasks and DeepSeek V3 for text-heavy reasoning tasks, routing between them based on input type.