Mistral Large 3 675BvsLlama 3.3 70B Instruct
Complete comparison between Mistral Large 3 675B (675B parameters, Mistral AI) and Llama 3.3 70B Instruct (70B, Meta). VRAM required by quantization, tokens per second measured on 4 reference GPUs, use-case verdict, license, installation commands. All figures are calculated from catalog data—no copy-pasting between pages.
In brief
| Feature | Mistral Large 3 675B | Llama 3.3 70B Instruct |
|---|---|---|
| Settings | 675B | 70B |
| Family | Mistral | Llama |
| Author | Mistral AI | Meta |
| Origin | FR | US |
| License | Apache 2.0 | Llama 3.3 Community |
| Context | 256,000 tokens | 128,000 tokens |
| Output | December 2025 | December 2024 |
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 | Mistral Large 3 675B | Llama 3.3 70B Instruct |
|---|---|---|
| Q4_K_M (lightweight) | 405 GB | 40 GB |
| Q5_K_M (balanced) | 485 GB | 48 GB |
| Q8 (near-lossless) | 720 GB | 75 GB |
| FP16 (max quality) | 1350 GB | 140 GB |
| CPU-only RAM | 480 GB | 64 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 | Mistral Large 3 675B | Llama 3.3 70B Instruct |
|---|---|---|
| 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.
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Strengths and weaknesses
Mistral Large 3 675B
675B/41B active MoE + 2.5B vision encoder, Apache 2.0. #2 OSS non-reasoning model on LMArena. Trained on 3,000 H200s.
- Flagship FR frontier
- Apache 2.0
- #2 OSS non-reasoning LMArena
- Multimodal
- 405 GB in Q4 — B200/H200 server required
Installation
Llama 3.3 70B Instruct
Llama 3.1 405B quality at 1/6 the size. Weights under a community license, gated HF access.
- 405B quality at 1/6 the size
- 128k ctx
- Very strong at reasoning and coding
- HF gated (accept Meta's terms)
- Community license with a >700M MAU clause
- No vision
Installation
Frequently asked questions
Which model runs best on RTX 4090 (24 GB): Mistral Large 3 675B or Llama 3.3 70B Instruct?
On a RTX 4090, Mistral Large 3 675B does not fit within 24 GB — part of it must be offloaded to the CPU; Llama 3.3 70B Instruct also does not fit within 24 GB. See the configurator to test your exact GPU.
Mistral Large 3 675B or Llama 3.3 70B Instruct: which is better in French?
For formal French (writing, summarization, translation), Mistral Large 3 675B has the edge—trained on a dense French-language corpus. The other remains usable, but for less demanding use cases.
Which uses the least VRAM?
In Q4_K_M, Llama 3.3 70B Instruct fits in 40 GB versus 405 GB for its rival — a 365 GB difference, significant if you're targeting an RTX 3060 or a 4060 Ti 8 GB.
Can these models be used in commercial production?
Mistral Large 3 675B is under Apache 2.0 — free for commercial use. Llama 3.3 70B Instruct is under Llama 3.3 Community—also check the terms. For SaaS, prefer Apache 2.0 or MIT.
Which one should you choose in 2026?
Depends on your primary constraint. Smaller / faster: Llama 3.3 70B Instruct (70B). More capable: Mistral Large 3 675B (675B). 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.