Updated 9 October 2026. This post is from April 2026 and compares against GPT-4o-mini, Llama 3.3 and Qwen 2.5. The method still holds; for newer local options by memory size, see our table by hardware and task.
There’s something fitting about a Paris-based company building a multilingual open model for European businesses. Mistral AI released Mistral Small 24B Instruct 2501 in January 2025, and it is a strong default for anything that touches several European languages.
Here are the real numbers, the honest trade-offs, and where it fits.
The Real Benchmarks (From HuggingFace, Not Marketing)
Most reviews cherry-pick benchmarks. Here’s the full picture from Mistral’s official model card, showing how it compares to models both smaller and larger:
Reasoning & Knowledge
| Benchmark | Mistral Small 24B | Gemma 2 27B | Llama 3.3 70B | Qwen 2.5 32B | GPT-4o-mini |
|---|---|---|---|---|---|
| MMLU-Pro (5-shot) | 66.3% | 53.6% | 66.6% | 68.3% | 61.7% |
| GPQA (5-shot) | 45.3% | 34.4% | 53.1% | 40.4% | 37.7% |
Coding & Math
| Benchmark | Mistral Small 24B | Gemma 2 27B | Llama 3.3 70B | Qwen 2.5 32B | GPT-4o-mini |
|---|---|---|---|---|---|
| HumanEval (Pass@1) | 84.8% | 73.2% | 85.4% | 90.9% | 89.0% |
| Math Instruct | 70.6% | 53.5% | 74.3% | 81.9% | 76.1% |
Instruction Following & Conversation
| Benchmark | Mistral Small 24B | Gemma 2 27B | Llama 3.3 70B | Qwen 2.5 32B | GPT-4o-mini |
|---|---|---|---|---|---|
| MTBench Dev | 8.35 | 7.86 | 7.96 | 8.26 | 8.33 |
| Arena Hard | 87.3% | 78.8% | 84.0% | 86.0% | 89.7% |
| IFEval | 82.9% | 80.7% | 88.4% | 84.0% | 85.0% |
Bold marks the best score in each row. Source: Mistral’s model card, checked 2026-10-09.
What this tells us: against GPT-4o-mini, Mistral Small 24B wins 2 of the 7 rows (MMLU-Pro, GPQA), ties on MT-Bench (8.35 vs 8.33, within noise) and loses the other 4: HumanEval, Math Instruct, Arena Hard and IFEval. So it is close to GPT-4o-mini, not a match across the board, and it runs on your own hardware. It also trails Llama 3.3 70B on reasoning, but Llama 3.3 70B is a 43 GB download in Ollama against 14 GB for Mistral Small, about 3x the memory.
xychart-beta
title "Mistral Small 24B — Efficiency Sweet Spot"
x-axis ["MMLU-Pro", "HumanEval", "MATH"]
y-axis "Score (%)" 0 --> 100
bar [66.3, 84.8, 70.6]The real story is the value per parameter: at 24B it lands within a few points of Llama 3.3 70B on MMLU-Pro and HumanEval, on Mistral’s own table.
The Multilingual Edge
This is where Mistral Small fits best. The model card says it “supports dozens of languages”, naming English, French, German, Spanish, Italian, Chinese, Japanese, Korean, Portuguese, Dutch, and Polish.
For a European business, this isn’t a checkbox feature. It’s the difference between:
- One model that handles your Spanish customer tickets, German compliance docs, French marketing copy, and English internal comms
- Four separate models (or expensive cloud APIs) stitched together with translation middleware
Benchmarks for Spanish or French business writing specifically are not in the model card, so test it on your own content before you commit.
Hardware: What You Actually Need
Download sizes from the Ollama library, checked 2026-10-09. Add a few GB for context.
| Quantization | Download | Device examples | Our recommendation |
|---|---|---|---|
| Q4_K_M | 14 GB | 24 GB GPU (RTX 4090), Mac with 24-32 GB | Best for most SMEs |
| Q8_0 | 25 GB | 32 GB+ GPU, Mac with 48 GB | Closer to full quality |
| FP16 | 47 GB | 80 GB GPU, Mac with 64 GB | Maximum quality, not needed for most tasks |
The Q4 build is a one-time hardware purchase, not a monthly API bill. For the cost comparison, see our cloud vs local AI cost analysis.
Where It Fits
The tasks where a multilingual 24B model pays off:
- Client communications: drafting emails and reports in Spanish and English
- Knowledge base work: generating and reviewing articles on European regulatory topics
- Research: summarizing company profiles and market data from sources in several languages
- Localization: first drafts of Spanish and English versions of the same document
On our own DGX Spark we currently run qwen3.6:35b and gemma4:26b for this kind of work (ollama list, 2026-10-09), because the box has the memory for them. On a 24-32 GB machine, Mistral Small is the sensible multilingual pick.
The Honest Trade-offs
Let’s be fair about what it’s NOT great at:
- Math and coding: Qwen 2.5 32B beats it significantly (81.9% vs 70.6% on math). If your primary use case is code generation, Qwen or Llama 3.3 are better choices.
- Complex reasoning: Llama 3.3 70B outperforms on GPQA (53.1% vs 45.3%). For deep analytical tasks, you want a bigger model.
- Context length: 32K tokens is good but not exceptional. For processing very long documents, models with 128K+ context may be needed.
- Speed on small hardware: At 24B parameters, it’s slower than Gemma 2 9B or Phi-4 on the same device. If latency matters more than quality, consider a smaller model.
Getting Started (5 Minutes)
# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh
# Pull Mistral Small (quantized for typical hardware)
ollama pull mistral-small
# Test with a multilingual prompt
ollama run mistral-small "Traduce esta cláusula contractual al inglés y resume los puntos clave: [tu texto aquí]"
# Serve as API for your applications
ollama serve
# Then: curl http://localhost:11434/api/chat -d '{"model":"mistral-small","messages":[{"role":"user","content":"..."}]}'Who Should Use This Model
Choose Mistral Small 24B if you need multilingual European language support, want open-source licensing (Apache 2.0), and have 14+ GB of VRAM available.
Choose something else if your work is primarily English-only coding/math (use Qwen 2.5) or you need the absolute best reasoning performance (use Llama 3.3 70B).
For a broader comparison of all the models we recommend, see our Q2 2026 local LLM guide.
Want help deploying Mistral Small in your business? We specialize in local AI deployments for European SMEs: private, affordable, GDPR-compliant. Book a free assessment →
Sources: Mistral Small 24B Model Card (HuggingFace) · MarkTechPost Review · Mistral AI
Next steps
- Compare these benchmarks against our local LLM comparison guide.
- Check your hardware for the required 14 GB of VRAM.
- Evaluate if your use case requires the coding strengths of Qwen 2.5 Coder instead.
Related reading
- Llama 3.3 70B Instruct: The Open-Source Giant That Genuinely Rivals GPT-4o
- Qwen 2.5 72B Instruct: The 29-Language Powerhouse That Belongs on Every Local AI Shortlist
- Qwen2.5-Coder-7B-Instruct
Work with us
We size the model and the machine by measuring, not by guessing. If you want to see your own task running on real hardware, book a 15-minute call or see how we work in consulting.