Llama 4 Maverick 400BvsDeepSeek V3.2
Complete comparison between Llama 4 Maverick 400B (400B parameters, Meta) and DeepSeek V3.2 (685B, DeepSeek). VRAM required by quantization, tokens per second measured on 4 reference GPUs, verdict by use case, license, installation commands. All figures are calculated from catalog data—no copy-pasting between pages.
In brief
| Feature | Llama 4 Maverick 400B | DeepSeek V3.2 |
|---|---|---|
| Settings | 400B | 685B |
| Family | Llama | DeepSeek |
| Author | Meta | DeepSeek |
| Origin | US | CN |
| License | Llama 4 Community | MIT |
| Context | 1,000,000 tokens | 128,000 tokens |
| Output | April 2025 | December 2025 |
Memory footprint
Approximate VRAM required for inference with an average context window. The winner (in green) is the model that uses less — smaller models have the edge.
| Quantization | Llama 4 Maverick 400B | DeepSeek V3.2 |
|---|---|---|
| Q4_K_M (lightweight) | 240 GB | 410 GB |
| Q5_K_M (balanced) | 285 GB | 490 GB |
| Q8 (near-lossless) | 425 GB | 735 GB |
| FP16 (max quality) | 800 GB | 1370 GB |
| CPU-only RAM | 280 GB | 512 GB |
Estimated speed (tokens/second)
Estimates based on the best quantization that can fit on each GPU. Actual figures depend on the prompt, context, and engine (llama.cpp, vLLM, MLX). Methodology.
| Reference GPU | Llama 4 Maverick 400B | DeepSeek V3.2 |
|---|---|---|
| RTX 4090 (24 GB) | ✗ too heavy | ✗ too heavy |
| RTX 4080 (16 GB) | ✗ too heavy | ✗ too heavy |
| RTX 3060 12GB (12 GB) | ✗ too heavy | ✗ too heavy |
| Apple M4 Pro (48 GB) (36 GB) | ✗ too heavy | ✗ too heavy |
Verdict by use case
For each common use case, we indicate which of the two is better suited based on its tags, size, and specialization.
Your private, free ChatGPT on your machine in 1 hour — LM Studio, Ollama, Open WebUI, your documents, no cloud.
- Lifetime online access
- PDF + files
- Refunded within 30 days
Strengths and weaknesses
Llama 4 Maverick 400B
400B/17B active MoE (128 experts), natively multimodal. LMArena 1417. 245 GB to download.
- LMArena 1417
- MMLU-Pro 80
- 1M context
- Multimodal
- 245 GB download
- HF gated
- Clause >700M MAU
Installation
DeepSeek V3.2
685B MoE with DeepSeek Sparse Attention. IMO gold medal. #2 by volume on OpenRouter.
- IMO gold medal
- #2 volume OpenRouter
- MIT
- 410+ GB in Q4
Installation
Frequently asked questions
Which model runs best on RTX 4090 (24 GB): Llama 4 Maverick 400B or DeepSeek V3.2?
On an RTX 4090, Llama 4 Maverick 400B does not fit in 24 GB — part of it must be offloaded to the CPU; DeepSeek V3.2 does not fit in 24 GB either. See the configurator to test your exact GPU.
Which uses the least VRAM?
In Q4_K_M, Llama 4 Maverick 400B fits in 240 GB versus 410 GB for its rival — a 170 GB difference, significant if you are targeting an RTX 3060 or an 8 GB 4060 Ti.
Can these models be used in commercial production?
Llama 4 Maverick 400B is under Llama 4 Community — check the restrictions (especially the monthly user thresholds). DeepSeek V3.2 is also free under the MIT license. For SaaS, favor Apache 2.0 or MIT.
Which one should you choose in 2026?
Depends on your primary constraint. Smaller / faster: Llama 4 Maverick 400B (400B). More capable: DeepSeek V3.2 (685B). If you're unsure, run the configurator with your GPU and use case — it will decide based on both.
Alternatives to consider
If neither works for you, here are some neighboring models you could explore.