Mistral Small 3.1 24B vs Llama 3.3 70B Instruct
Side-by-side specs, benchmarks, and a verdict by use case.
Updated 2026-07-13
| Spec | Mistral Small 3.1 24B | Llama 3.3 70B Instruct |
|---|---|---|
| Parameters | 24B | 70B |
| Author | Mistral AI | Meta |
| License | Apache 2.0 | Llama 3.3 Community |
| Context window | 0k | 0k |
| VRAM at Q4 | 14 GB | 40 GB |
| VRAM at Q5 | 17 GB | 48 GB |
| VRAM at Q8 | 26 GB | 75 GB |
| VRAM at FP16 | 48 GB | 140 GB |
| Use cases | chat, general, vision, multilingual, fr | chat, general, reasoning |
Verdict
Llama 3.3 70B Instruct is significantly larger (70B vs 24B), so expect higher quality but heavier VRAM and slower throughput.
For unambiguous commercial use, Mistral Small 3.1 24B has the safer license (Apache 2.0) compared to Llama 3.3 Community.
The two models at a glance
About Mistral Small 3.1 24B
Mistral AI's Small 3.1 — Small 3 plus a vision encoder, a 128k context, and ~150 tok/s inference under Apache 2.0. Small 3.2 (June 2025) is a drop-in upgrade. Strengths: Vision and text combined in one 24B model, 128k context window, Apache 2.0 license, Around 150 tokens/sec inference.
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 Small 3.1 24B 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 24B vs 70B parameters, Llama 3.3 70B Instruct is the larger of the two. At Q4, Mistral Small 3.1 24B fits in about 14 GB of VRAM versus 40 GB for the other — a 26 GB difference that matters on consumer GPUs.
Where they overlap on benchmarks, Llama 3.3 70B Instruct takes MMLU with 86 against 80.6 — a clear 5.4-point margin. For workloads weighted toward that benchmark, Llama 3.3 70B Instruct is the stronger default.
On a typical mid-range GPU, Mistral Small 3.1 24B pushes roughly 15 tokens/sec versus 6, so it is the more responsive choice for interactive or high-volume use.
Memory, quantization & throughput
Across quantization levels, Mistral Small 3.1 24B requires Q4 ≈ 14 GB, Q5 ≈ 17 GB, Q8 ≈ 26 GB, FP16 ≈ 48 GB, while Llama 3.3 70B Instruct requires Q4 ≈ 40 GB, Q5 ≈ 48 GB, Q8 ≈ 75 GB, FP16 ≈ 140 GB. In practice Mistral Small 3.1 24B needs a 16 GB card 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 Small 3.1 24B needs roughly 24 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 15 tokens/sec from Mistral Small 3.1 24B and 6 from Llama 3.3 70B Instruct, scaling up to 40 and 20 tokens/sec on high-end hardware.
Which fits your GPU
Here is the highest-quality quantization of each model that fits common GPU memory budgets, so you can match Mistral Small 3.1 24B or Llama 3.3 70B Instruct to the card you actually own:
- On a 16 GB GPU: Mistral Small 3.1 24B runs at Q4 (14 GB); Llama 3.3 70B Instruct does not fit.
- On a 24 GB GPU: Mistral Small 3.1 24B runs at Q5 (17 GB); Llama 3.3 70B Instruct does not fit.
Benchmark scores
Reported benchmarks for Mistral Small 3.1 24B: MMLU 80.6, MMMU 64.
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 Small 3.1 24B if you need a permissive (Apache 2.0) license for commercial deployment.
- Pick Mistral Small 3.1 24B for lower VRAM and faster inference; pick Llama 3.3 70B Instruct for maximum headline quality.
- Pick Llama 3.3 70B Instruct if MMLU performance is your priority (86 vs 80.6).
- Pick Mistral Small 3.1 24B if your workload is fr, multilingual, vision.
- Pick Llama 3.3 70B Instruct if your workload is reasoning.
Which GPU should you buy to run Llama 3.3 70B Instruct?
To run Llama 3.3 70B Instruct locally at Q4, you need ~40 GB of VRAM. The best value for this is a Apple Mac Studio (64+ GB unified memory).
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Frequently asked questions
What is the difference between Mistral Small 3.1 24B and Llama 3.3 70B Instruct?
The headline differences: Mistral Small 3.1 24B is a 24B model and Llama 3.3 70B Instruct is 70B; 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 Small 3.1 24B and Llama 3.3 70B Instruct run on a 24 GB GPU?
At a Q4 quantization, Mistral Small 3.1 24B needs about 14 GB of VRAM and fits comfortably on a 24 GB GPU; Llama 3.3 70B Instruct needs about 40 GB and needs more than 24 GB. Mistral Small 3.1 24B is the lighter option for tight VRAM budgets.
Is Mistral Small 3.1 24B or Llama 3.3 70B Instruct more capable?
On MMLU, Llama 3.3 70B Instruct scores higher (86 vs 80.6), a 5.4-point advantage on this benchmark.
Which is faster, Mistral Small 3.1 24B or Llama 3.3 70B Instruct?
Mistral Small 3.1 24B is the smaller model (24B vs 70B), 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 Small 3.1 24B or Llama 3.3 70B Instruct?
Mistral Small 3.1 24B 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.