Mistral Large 3 675B vs Llama 3.3 70B Instruct

The two models at a glance

About Mistral Large 3 675B

Mistral AI's flagship 675B MoE (41B active) with a 2.5B vision encoder, trained from scratch on 3,000 H200s and released under Apache 2.0. Currently #2 OSS non-reasoning model on LMArena. Strengths: Top-tier open weights — #2 OSS non-reasoning on LMArena, Apache 2.0 — fully unrestricted commercial use, Native multimodal with 2.5B vision encoder, 256k context window.

About Llama 3.3 70B Instruct

Meta's Llama 3.3 70B — same quality tier as Llama 3.1 405B at one-sixth the size, thanks to improved post-training. Weights are gated on Hugging Face. Strengths: Quality competitive with Llama 3.1 405B, 128k context window, Strong reasoning and code performance, Major efficiency gain vs the 405B model.

How they compare

Mistral Large 3 675B comes from Mistral AI and Llama 3.3 70B Instruct from Meta, they belong to the Mistral and Llama families respectively. This comparison is built entirely from structured specs — parameter count, VRAM by quantization, context window, license, and published benchmark scores — so the verdict below reflects measurable differences rather than marketing claims.

At 675B vs 70B parameters, Mistral Large 3 675B is the larger of the two. At Q4, Llama 3.3 70B Instruct fits in about 40 GB of VRAM versus 405 GB for the other — a 365 GB difference that matters on consumer GPUs.

The two models target different sweet spots: Mistral Large 3 675B is tuned for chat, general, vision, multilingual, fr, moe, while Llama 3.3 70B Instruct leans toward chat, general, reasoning. Match the model to your dominant workload rather than to raw size.

On a typical mid-range GPU, Llama 3.3 70B Instruct pushes roughly 6 tokens/sec versus 5, so it is the more responsive choice for interactive or high-volume use. For long-context work, Mistral Large 3 675B offers the bigger window (250k vs 125k tokens).

Memory, quantization & throughput

Across quantization levels, Mistral Large 3 675B requires Q4 ≈ 405 GB, Q5 ≈ 485 GB, Q8 ≈ 720 GB, FP16 ≈ 1350 GB, while Llama 3.3 70B Instruct requires Q4 ≈ 40 GB, Q5 ≈ 48 GB, Q8 ≈ 75 GB, FP16 ≈ 140 GB. In practice Mistral Large 3 675B spills past 24 GB even at Q4, so plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity of Q8 or FP16.

Without a GPU, Mistral Large 3 675B needs roughly 480 GB of system RAM to run on CPU and Llama 3.3 70B Instruct about 64 GB — workable for offline use but far slower than GPU inference. On a mid-range GPU you can expect on the order of 5 tokens/sec from Mistral Large 3 675B and 6 from Llama 3.3 70B Instruct, scaling up to 15 and 20 tokens/sec on high-end hardware.

Benchmark scores

Reported benchmarks for Llama 3.3 70B Instruct: MMLU 86, GPQA Diamond 50.5, HumanEval 88.4.

Bottom line: which should you pick?

• Pick Mistral Large 3 675B if you need a permissive (Apache 2.0) license for commercial deployment.
• Pick Mistral Large 3 675B for long-context work (up to 250k tokens).
• Pick Llama 3.3 70B Instruct for lower VRAM and faster inference; pick Mistral Large 3 675B for maximum headline quality.
• Pick Mistral Large 3 675B if your workload is fr, moe, multilingual, vision.
• Pick Llama 3.3 70B Instruct if your workload is reasoning.

Which GPU should you buy to run Mistral Large 3 675B?

To run Mistral Large 3 675B locally at Q4, you need ~405 GB of VRAM. The best value for this is a Apple Mac Studio (64+ GB unified memory).

Frequently asked questions

What is the difference between Mistral Large 3 675B and Llama 3.3 70B Instruct?

The headline differences: Mistral Large 3 675B is a 675B model and Llama 3.3 70B Instruct is 70B; their context windows differ (250k vs 125k tokens); they ship under different licenses (Apache 2.0 vs Llama 3.3 Community). Below we break down VRAM by quantization, benchmark scores, and a use-case verdict so you can pick the right one.

Can Mistral Large 3 675B and Llama 3.3 70B Instruct run on a 24 GB GPU?

At a Q4 quantization, Mistral Large 3 675B needs about 405 GB of VRAM and needs more than 24 GB (multi-GPU or heavier offload); Llama 3.3 70B Instruct needs about 40 GB and needs more than 24 GB. Llama 3.3 70B Instruct is the lighter option for tight VRAM budgets.

Which is faster, Mistral Large 3 675B or Llama 3.3 70B Instruct?

Llama 3.3 70B Instruct is the smaller model (70B vs 675B), so on the same hardware it runs faster and uses less memory. The larger model trades speed for headline quality.

Which license is safer for commercial use, Mistral Large 3 675B or Llama 3.3 70B Instruct?

Mistral Large 3 675B ships under Apache 2.0, a permissive license with no usage restrictions, whereas the other is under Llama 3.3 Community — check its terms before commercial deployment.

Which has the longer context window, Mistral Large 3 675B or Llama 3.3 70B Instruct?

Mistral Large 3 675B has the larger context window (250k vs 125k tokens), so it handles longer documents and codebases in a single prompt.