Mistral Small 3.1 24BvsLlama 3.3 70B Instruct
Complete comparison between Mistral Small 3.1 24B (24B 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 Small 3.1 24B | Llama 3.3 70B Instruct |
|---|---|---|
| Settings | 24B | 70B |
| Family | Mistral | Llama |
| Author | Mistral AI | Meta |
| Origin | FR | US |
| License | Apache 2.0 | Llama 3.3 Community |
| Context | 128,000 tokens | 128,000 tokens |
| Output | March 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 Small 3.1 24B | Llama 3.3 70B Instruct |
|---|---|---|
| Q4_K_M (lightweight) | 14 GB | 40 GB |
| Q5_K_M (balanced) | 17 GB | 48 GB |
| Q8 (near-lossless) | 26 GB | 75 GB |
| FP16 (max quality) | 48 GB | 140 GB |
| CPU-only RAM | 24 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 Small 3.1 24B | Llama 3.3 70B Instruct |
|---|---|---|
| RTX 4090 (24 GB) | 40 tok/s · Q5_K_M | ✗ too heavy |
| RTX 4080 (16 GB) | 15 tok/s · Q4_K_M | ✗ too heavy |
| RTX 3060 12GB (12 GB) | ✗ too heavy | ✗ too heavy |
| Apple M4 Pro (48 GB) (36 GB) | 15 tok/s · Q8 | ✗ 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 Small 3.1 24B
Small 3 enhanced with vision. 128k ctx, Apache 2.0. Replaced by Small 3.2 since June 2025.
- Vision + text in a 24B
- 128k ctx
- Apache 2.0
- ~150 tok/s inference
- Requires Ollama ≥ 0.6.5
- Small 3.2 (June 2025) improves it slightly
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 Small 3.1 24B or Llama 3.3 70B Instruct?
On an RTX 4090, Mistral Small 3.1 24B runs at Q5_K_M (~40 tok/s), Llama 3.3 70B Instruct doesn't fit on 24 GB either. See the configurator to test your exact GPU.
Mistral Small 3.1 24B or Llama 3.3 70B Instruct: which is better in French?
For formal French (writing, summarization, translation), Mistral Small 3.1 24B 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, Mistral Small 3.1 24B fits in 14 GB versus 40 GB for its rival—a 26 GB gap, significant if you are targeting an RTX 3060 or an 8 GB 4060 Ti.
Can these models be used in commercial production?
Mistral Small 3.1 24B 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: Mistral Small 3.1 24B (24B). More capable: Llama 3.3 70B Instruct (70B). 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.