Mistral Small 3.1 24BvsGemma 3 27B
Complete comparison between Mistral Small 3.1 24B (24B parameters, Mistral AI) and Gemma 3 27B (27B, Google). VRAM required by quantization, tokens per second measured on 4 reference GPUs, verdict by use case, license, and installation commands. All figures are calculated from the catalog data—no copy-pasting between pages.
In brief
| Feature | Mistral Small 3.1 24B | Gemma 3 27B |
|---|---|---|
| Settings | 24B | 27B |
| Family | Mistral | Gemma |
| Author | Mistral AI | |
| Origin | FR | US |
| License | Apache 2.0 | Gemma |
| Context | 128,000 tokens | 128,000 tokens |
| Output | March 2025 | March 2025 |
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 | Gemma 3 27B |
|---|---|---|
| Q4_K_M (lightweight) | 14 GB | 16 GB |
| Q5_K_M (balanced) | 17 GB | 19 GB |
| Q8 (near-lossless) | 26 GB | 29 GB |
| FP16 (max quality) | 48 GB | 54 GB |
| CPU-only RAM | 24 GB | 28 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 | Gemma 3 27B |
|---|---|---|
| RTX 4090 (24 GB) | 40 tok/s · Q5_K_M | 32 tok/s · Q5_K_M |
| RTX 4080 (16 GB) | 15 tok/s · Q4_K_M | 13 tok/s · Q4_K_M |
| RTX 3060 12GB (12 GB) | ✗ too heavy | ✗ too heavy |
| Apple M4 Pro (48 GB) (36 GB) | 15 tok/s · Q8 | 13 tok/s · Q8 |
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
Gemma 3 27B
High-end Gemma. LMArena Elo 1338 — beats Llama 3.1 405B at 15× smaller size.
- LMArena Elo 1338: beats Llama 3.1 405B while being 15× smaller
- Multimodal
- 128k ctx
- License Gemma
- No thinking mode
Installation
Frequently asked questions
Which model runs best on RTX 4090 (24 GB): Mistral Small 3.1 24B or Gemma 3 27B?
On an RTX 4090, Mistral Small 3.1 24B runs at Q5_K_M (~40 tok/s), Gemma 3 27B in Q5_K_M (~32 tok/s). In pure throughput terms, Mistral Small 3.1 24B wins. See the configurator to test your exact GPU.
Mistral Small 3.1 24B or Gemma 3 27B: 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 16 GB for its rival—a 2 GB difference, significant if you're 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. Gemma 3 27B is under Gemma — 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: Gemma 3 27B (27B). 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.