Llama 4 Maverick
MetaLarge Language ModelOpen SourceMeta's latest open-weight Mixture-of-Experts model with 400B total parameters (17B active). Excellent multilingual performance.
Abilities
Use Cases
Available in Tools
Availability
How to Use
Pros
- Open weights — inspect, fine-tune, and deploy on your own infrastructure
- MoE architecture — only 17B parameters active per token despite 400B total, great efficiency
- Strong multilingual support covering 12 languages natively
- Competitive with top closed models on many benchmarks
- Massive community — largest open-source LLM ecosystem with thousands of fine-tunes
- Available on all major cloud platforms for easy deployment
Cons
- Requires significant compute for self-hosting — 400B total params need multi-GPU setups
- License restricts commercial use above 700M monthly active users
- MoE architecture requires more total VRAM than equivalent dense models
- Self-hosting requires DevOps/ML engineering expertise for optimal performance
- Quality can vary more than closed models — less consistent on edge cases
What to Use It For
Perfect For
Open weights competitive with top closed models — full control over infrastructure and data
Natively trained on 12 languages with strong cross-lingual transfer — not just English-first
Largest open-source ecosystem with thousands of fine-tunes — proven recipes and tooling
Good For
MoE architecture delivers strong conversational quality with efficient inference
Competitive benchmark scores on code and knowledge tasks at zero licensing cost
Not Recommended
400B total parameters need multi-GPU setups — too large for single consumer GPUs
Try instead: Llama 3.3 70B, Phi-4
Vision capabilities exist but do not match the depth of frontier closed models on complex analysis
Try instead: Claude Opus 4
Do Not Use For
Requires DevOps setup (multi-GPU, vLLM, etc.) — days of setup vs minutes with a managed API
Try instead: GPT-4o
Far too large for any mobile device — even quantized variants need server-class hardware
Try instead: Gemma 3, Phi-4