Gemma 3 27B vs Mistral Small 22B
Side-by-side comparison of hardware requirements, quantization options, and specifications to help you choose the right model for your device.
Specifications Comparison
| Spec | Gemma 3 27B | Mistral Small 22B |
|---|---|---|
| Parameters | 27B | 22B |
| Architecture | gemma3 | mistral |
| License | Gemma | Apache 2.0 |
| Context Length | 32K tokens | 32K tokens |
| Category | Language Model | Language Model |
| Author | Mistral AI | |
| HF Downloads | 1.3M | 8.3K |
| VRAM Range | 15.91 - 15.91 GB | 12.93 - 12.93 GB |
| Quantizations | 1 options | 1 options |
| Best Quality Score | 85% | 85% |
Quantization Options
Gemma 3 27B
Mistral Small 22B
In-depth comparison
Gemma 3 27B is the better choice for most users due to its higher parameter count and near GPT-4 quality, though it requires more VRAM. Mistral Small 22B is a solid alternative for those with limited VRAM.
When to choose Gemma 3 27B
Gemma 3 27B is the better pick for users who prioritize top-tier performance and quality in their text generation tasks. It excels in generating highly coherent and contextually rich content, making it ideal for professional applications like content creation, research, and advanced conversational AI. However, it requires at least 20GB of RAM, which may be a limitation for some users.
When to choose Mistral Small 22B
Mistral Small 22B is the better choice for users with more modest hardware, particularly those with GPUs that have 16GB or less of VRAM. Despite having fewer parameters, it still delivers high-quality outputs and is well-suited for a wide range of tasks, including content creation, summarization, and conversational AI. Its lower VRAM requirement makes it more accessible for a broader audience.
Quality
Both models achieve a best quality score of 85%, indicating they are on par in terms of output quality. However, Gemma 3 27B, with its larger parameter count and near GPT-4 quality, likely produces slightly more sophisticated and nuanced text. The difference may be subtle, but it can be significant in demanding applications.
Performance & hardware fit
Gemma 3 27B requires a minimum of 15.9GB of VRAM and ideally 20GB+ for optimal performance, making it more demanding on hardware. In contrast, Mistral Small 22B needs only 12.9GB of VRAM, making it more suitable for systems with less powerful GPUs. Despite the VRAM difference, both models offer similar quality scores, suggesting that Mistral Small 22B is a more efficient option.
Use-case fit
| coding | Gemma 3 27B | Gemma 3 27B's higher parameter count and near GPT-4 quality make it better suited for complex coding tasks and generating detailed code snippets. |
| creative writing | Gemma 3 27B | Gemma 3 27B's ability to generate highly coherent and contextually rich content makes it superior for creative writing tasks. |
| RAG / retrieval | Tie | Both models have a context length of 32,768 tokens, making them equally capable for RAG and retrieval tasks. |
| agent / tool use | Gemma 3 27B | Gemma 3 27B's advanced reasoning capabilities and higher parameter count make it more effective for agent and tool use scenarios. |
| running on consumer GPU (8-12GB) | Mistral Small 22B | Mistral Small 22B requires only 12.9GB of VRAM, making it more feasible to run on consumer GPUs with 8-12GB of VRAM. |
| long context (16K+) | Tie | Both models support a context length of 32,768 tokens, making them equally suitable for long context tasks. |
Gemma 3 27B wins for most users due to its superior quality and versatility, especially in demanding applications. Mistral Small 22B is the clear winner for users with limited VRAM, offering a good balance of performance and resource efficiency.