Gemma 3 27B vs Mistral Small 3.1 vs QwQ 32b — These Models Are Now Superseded
🚀 These models have been superseded
Gemma 3 27B → Gemma 4 31B and QwQ 32b → Qwen3 are the current generations. This page preserves the original comparison for reference and points you to the updated guides.
The Original Comparison (For Reference)
When this comparison was first written, Gemma 3 27B, Mistral Small 3.1, and QwQ 32b were three open-weight models commanding attention in the AI community. Here's what set each apart:
Gemma 3 27B
- Multimodal support — strong combined text and image processing
- 128K token context — ideal for document summarization and image analysis
- 140+ languages — excellent for global applications
- Adaptable — fine-tunable for specific tasks
Mistral Small 3.1
- Efficiency-focused — optimized for cases where computational resources are limited
- Compact — designed for cost-effective deployment
- Less extensively documented than its peers at the time
QwQ 32b
- Reasoning-oriented — built for deep logical and analytical tasks
- Strong mathematical problem-solving
Why This Comparison Is Now Outdated
The open-weight landscape moves fast. Since this comparison, every model in it has been replaced by a substantially more capable successor:
| Old model (this page) | Current generation | Key jump |
|---|---|---|
| Gemma 3 27B | Gemma 4 31B | 85.2% MMLU Pro, 89.2% AIME 2026 — competes with models twice its size |
| QwQ 32b | Qwen3 (0.6B – 235B) | Full new generation from Alibaba's Qwen team, released April 2026 |
| Mistral Small 3.1 | (Mistral's newer line) | Efficiency tier continues under newer releases |
The practical takeaway: if you're choosing an open-weight model today, you should be looking at Gemma 4 and Qwen3, not the three models this page originally compared.
Current Guides (Updated)
➡️ How to Run Gemma 4 31B Locally — Unsloth, Ollama, llama.cpp, and HuggingFace methods; hardware requirements, GGUF quantization, and step-by-step setup.
➡️ How to Run Qwen3 Locally — a practical guide using Ollama and vLLM with performance insights.
Still Comparing the Legacy Models?
If you're locked into the older generation for reproducibility or existing pipelines, the quick summary is:
- Pick Gemma 3 27B if you need multimodal (text + image) and broad language coverage with a 128K context.
- Pick Mistral Small 3.1 if compute budget is the overriding constraint.
- Pick QwQ 32b if pure reasoning / math is the workload.
⚠️ For any new project, skip this generation entirely and go straight to Gemma 4 31B or Qwen3.