Meta's Llama 4 is the most significant open-weight AI release since DeepSeek V3. Scout and Maverick arrived in April 2025 as the first natively multimodal open-weight models using a Mixture-of-Experts architecture. Scout has a 10-million-token context window and runs on a single H100 GPU. Maverick scored 73.4% on MMMU against GPT-4o's 69.1%. Both are freely downloadable and commercially usable. India's Ministry of Skill Development is already building on Llama for student learning. Meta AI powered by Llama 4 is live in WhatsApp across India.
The Three Llama 4 Models
- Llama 4 Scout — 17B active parameters, 16 experts, 109B total parameters. 10M token context window. Fits on a single H100 GPU. Best multimodal open-weight model in its compute class.
- Llama 4 Maverick — 17B active parameters, 128 experts, 400B total parameters. Architecture similar to DeepSeek V3. API pricing $0.19–$0.49 per million blended tokens.
- Llama 4 Behemoth — 288B active parameters, 2T total parameters. Still training. Outperforms GPT-4.5 and Claude Sonnet 3.7 on STEM benchmarks. Teacher model for Scout and Maverick.
What Llama 4 Does Better Than Previous Models
- Native multimodality from training — Jointly pre-trained on text, images, and video using early fusion. Image understanding is architecturally integrated, not bolted on.
- 10M token context (Scout) — Processes entire textbook collections, full codebases, or year-long document archives in one session. No open-weight model has come close to this.
- Free WhatsApp access for India — Meta AI in India is powered by Llama 4. Every Indian WhatsApp user can access it free by typing @Meta AI in any chat.
- Hindi language support — Official Hindi support alongside 11 other languages. First major Llama model with explicit Hindi support.
- Commercial use under 700M MAU limit — Free usage and modification for most organisations.
Benchmark Controversy: What Independent Testing Shows
Meta's initial benchmark claims attracted criticism. Independent researchers found the publicly available Maverick version performed worse than the version submitted to LMSYS Arena. The honest result from independent 2026 testing: Maverick consistently trails GPT-5.3 by 1–2 percentage points on reasoning benchmarks but matches or exceeds it on code generation. Scout is best-in-class for its compute tier — remarkable quality in a single-GPU footprint.
| Task | Llama 4 Maverick | Alternative to Consider |
|---|---|---|
| Code generation | Excellent — top open-weight coding model | Claude Sonnet 4.6 leads SWE-bench |
| Multimodal (image) understanding | 73.4% MMMU — beats GPT-4o (69.1%) | Gemini 3 Pro for video |
| 10M token long context | Scout only — best open-weight | Gemini 3.1 Pro (1M GA) |
| Local private deployment | Best — open weights, runs on H100 | DeepSeek V3 also open weights |
| Free daily access India | WhatsApp @Meta AI — completely free | DeepSeek web interface free |
How Indian Students Can Access Llama 4 Free
- WhatsApp — Type @Meta AI in any chat in India. No download, no extra account. Llama 4 Maverick responds instantly.
- Meta.ai website — Free access with image upload capability. Works in any browser.
- Hugging Face — Download weights for Scout and Maverick. Free with Meta licence acceptance.
- together.ai and Groq — API access at very low cost. Best for building applications.
Pro Tip: Best Llama 4 use case for Indian students right now: open Meta AI in WhatsApp, upload a photo of a handwritten physics problem or NCERT diagram, and ask for explanation. Native multimodality understands the visual content directly — no transcription needed. For NEET and JEE students who work with diagrams constantly, this free WhatsApp multimodal Q&A is genuinely useful.