Mistral Small 3.1 24B vs Gemma 3 27B
Side-by-side specs, benchmarks, and a verdict by use case.
Updated 2026-07-13
| Spec | Mistral Small 3.1 24B | Gemma 3 27B |
|---|---|---|
| Parameters | 24B | 27B |
| Author | Mistral AI | |
| License | Apache 2.0 | Gemma |
| Context window | 0k | 0k |
| VRAM at Q4 | 14 GB | 16 GB |
| VRAM at Q5 | 17 GB | 19 GB |
| VRAM at Q8 | 26 GB | 29 GB |
| VRAM at FP16 | 48 GB | 54 GB |
| Use cases | chat, general, vision, multilingual, fr | chat, general, vision, multilingual |
Verdict
Both models sit in a similar size class. The pick depends on tags, license, and benchmarks rather than raw parameter count.
For unambiguous commercial use, Mistral Small 3.1 24B has the safer license (Apache 2.0) compared to Gemma.
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 Gemma 3 27B
Google's flagship Gemma 3 at 27B — multimodal, 128K context, and an LMArena Elo of 1338 that beats Llama 3.1 405B at 15x smaller. Sets the bar for open chat under 30B. Strengths: LMArena Elo 1338 — beats Llama 3.1 405B at 15x smaller, Multimodal with vision input, 128K context window, 140 language coverage.
How they compare
Mistral Small 3.1 24B comes from Mistral AI and Gemma 3 27B from Google, they belong to the Mistral and Gemma 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 27B parameters, Gemma 3 27B is the larger of the two. At Q4, Mistral Small 3.1 24B fits in about 14 GB of VRAM versus 16 GB for the other — a 2 GB difference that matters on consumer GPUs.
Where they overlap on benchmarks, Mistral Small 3.1 24B takes MMLU with 80.6 against 78.6 — a narrow 2.0-point margin. For workloads weighted toward that benchmark, Mistral Small 3.1 24B is the stronger default.
On a typical mid-range GPU, Mistral Small 3.1 24B pushes roughly 15 tokens/sec versus 13, 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 Gemma 3 27B requires Q4 ≈ 16 GB, Q5 ≈ 19 GB, Q8 ≈ 29 GB, FP16 ≈ 54 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 Gemma 3 27B about 28 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 13 from Gemma 3 27B, scaling up to 40 and 32 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 Gemma 3 27B to the card you actually own:
- On a 16 GB GPU: Mistral Small 3.1 24B runs at Q4 (14 GB); Gemma 3 27B runs at Q4 (16 GB).
- On a 24 GB GPU: Mistral Small 3.1 24B runs at Q5 (17 GB); Gemma 3 27B runs at Q5 (19 GB).
Benchmark scores
Reported benchmarks for Mistral Small 3.1 24B: MMLU 80.6, MMMU 64.
Reported benchmarks for Gemma 3 27B: LMArena Elo 73, MMLU 78.6, MMLU-Pro 67.5, MATH 89.
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 Gemma 3 27B for maximum headline quality.
- Pick Mistral Small 3.1 24B if MMLU performance is your priority (80.6 vs 78.6).
- Pick Mistral Small 3.1 24B if your workload is fr.
Which GPU should you buy to run Gemma 3 27B?
To run Gemma 3 27B locally at Q4, you need ~16 GB of VRAM. The best value for this is a RTX 5070 Ti (16 GB VRAM).
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Frequently asked questions
What is the difference between Mistral Small 3.1 24B and Gemma 3 27B?
The headline differences: Mistral Small 3.1 24B is a 24B model and Gemma 3 27B is 27B; they ship under different licenses (Apache 2.0 vs Gemma). 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 Gemma 3 27B 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; Gemma 3 27B needs about 16 GB and fits comfortably on a 24 GB GPU. Mistral Small 3.1 24B is the lighter option for tight VRAM budgets.
Is Mistral Small 3.1 24B or Gemma 3 27B more capable?
On MMLU, Mistral Small 3.1 24B scores higher (80.6 vs 78.6), a 2.0-point advantage on this benchmark.
Which is faster, Mistral Small 3.1 24B or Gemma 3 27B?
Mistral Small 3.1 24B is the smaller model (24B vs 27B), 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 Gemma 3 27B?
Mistral Small 3.1 24B ships under Apache 2.0, a permissive license with no usage restrictions, whereas the other is under Gemma — check its terms before commercial deployment.