Mistral vs Llama
Mistral and Llama side by side: model sizes, the VRAM you need, context window, abilities, licence and which one to pick.
Side by side
| Mistral | Llama | |
|---|---|---|
| Type | Open-weight models | Open-weight models |
| Developer | Mistral AI | Meta |
| Newest model | Mistral Large 4 · Sep 2026 | Llama 4 · Apr 2025 |
| Sizes | 3B · 8B · 14B · 24B · 128B | Scout 109B · Maverick 400B |
| Smallest VRAM (4-bit) | ~4 GB (Ministral 3 3B) | ~74.5 GB (Llama 4 Scout 109B) |
| Context window | 256K tokens | 10M tokens |
| Abilities | Tool callingReasoning modeImage input | Tool callingImage input |
| Licence | Mistral Ecosystem Open-Weight License, Modified MIT, Apache 2.0 | Llama 4 Community License |
| Run it | ollama run ministral-3:3b | ollama run llama4:scout |
Which one should you pick?
- Mistral runs on smaller hardware: from ~4 GB of GPU memory, against ~74.5 GB.
- Llama reads longer inputs: a 10M-token context window.
- Only Mistral has a reasoning mode.
- Mistral has the simpler licence for commercial use (Apache 2.0 or MIT).
Pick Mistral if…
- you want a European vendor and Apache 2.0 small models
- you need dependable function calling at 24B
- you want vision on edge hardware (Ministral 3)
Pick Llama if…
- you need the widest tool and fine-tune support
- your stack or tutorial already targets Llama 3.1
- you want very long context on a multi-GPU server (Llama 4 Scout)
VRAM and download size
Ollama's default 4-bit builds with an 8K context. Read GB as system RAM on a CPU-only machine or a Mac.
Mistral
| Model | Download | VRAM |
|---|---|---|
| Ministral 3 3B | 3 GB | ~4 GB |
| Ministral 3 8B | 6 GB | ~7.5 GB |
| Ministral 3 14B | 9.1 GB | ~11 GB |
| Mistral Small 3.2 24B | 15 GB | ~17 GB |
| Mistral Medium 3.5 128B | 80 GB | ~88.5 GB |
Llama
| Model | Download | VRAM |
|---|---|---|
| Llama 4 Scout 109B | 67 GB | ~74.5 GB |
| Llama 4 Maverick 400B | 245 GB | ~270 GB |
Frequently asked questions
Is Mistral better than Llama?
It depends on the job and your hardware. Mistral runs on smaller hardware: from ~4 GB of GPU memory, against ~74.5 GB. Llama reads longer inputs: a 10M-token context window. Only Mistral has a reasoning mode. For quality, test both on your own prompts: benchmark rankings change with every release.
Which needs less VRAM, Mistral or Llama?
Mistral starts at ~4 GB (3 GB download). Llama starts at ~74.5 GB (67 GB download).
Can I use Mistral and Llama commercially?
Mistral: Mistral Ecosystem Open-Weight License, Modified MIT, Apache 2.0. Llama: Llama 4 Community License. Check the exact licence of the model you deploy; custom licences can limit very large companies or certain uses.
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