European newcomers vs. Google products: Mistral Small 3.1 and Gemma 3 in-depth comparison
Mistral Small 3.1, a rising star in the AI field, challenges Google Gemma 3 in a competition of performance and efficiency.
Core content:
1. Comparative analysis of the parameter scale of Mistral Small 3.1 and Gemma 3
2. Technical highlights: Mistral's multimodal capabilities and Gemma's professional reasoning advantages
3. Performance showdown: How 24B parameters challenge 27B in benchmark tests
In the competition in the field of AI, lightweight large models have gradually become the focus.DeepMindroll outGemma 3after,Mistral AIIn March 2025Mistral Small 3.1A strong debut.24BThe model with 100 parameters has attracted much attention due to its high efficiency, multi-modality and open source features, and claims to have surpassed theGemma 3andGPT-4o MiniParameter scale is the core indicator of model performance and efficiency, which directly affects its application potential. This article will start from parameter comparison, combining multiple dimensions such as technology, performance and ecology to analyzeMistral Small 3.1andGemma 3The differences and similarities.
1. Parameter scale comparison: 24B vs 27B, which one is smarter?
Mistral Small 3.1have24BParameters, andGemma 3supply1B,4B,12Band27BMultiple versions, including27BThe 1000-megabyte version is its flagship model. The parameter scale directly determines the capacity and computing requirements of the model:
Mistral Small 3.1 (24B)
• Context Window: 128k tokens• Inference speed: 150 tokens/s• Hardware requirements: Single RTX 4090or32GB RAMMac can run• Multimodal support: text + images
Gemma 3 (27B)
• Context Window: 96k tokens• Inference speed: approx. 120 tokens/s(Not officially specified, based on community testing)• Hardware requirements: Dual GPUor high-end servers (A100 40GB）• Multimodal support: text + partial vision tasks
From the parameters point of view,Mistral Small 3.1by24BAchieve longer context windows and higher inference speed, whileGemma 3of27BThe capacity of the 10.1-inch and 10.1-inch versions is slightly better, but the hardware requirements are higher. The following chart directly compares the parameters and performance of the two:
Mistral Small 3.1 | 24B | 128k | 150 tokens/s | RTX 409032GB RAM |
Gemma 3 | 27B | 96k | A100 40GB+ |
Mistral Small 3.1It is superior in parameter efficiency.24BCan be comparable to or even surpass27BThe performance shows the exquisiteness of its architectural optimization.
2. Technical highlights: the secrets behind the parameters
Mistral Small 3.1of24BThe parameters support multimodal capabilities (text + image) and very long context processing, thanks to its hybrid attention mechanism and sparse matrix optimization. In contrast,Gemma 3of27BVersion based on GoogleGeminiIn terms of technology stack, it has advantages in multiple languages (140+ languages) and professional reasoning (such as mathematics and code), but its multimodal capabilities are slightly inferior.
Hardware friendliness is another big difference.Mistral Small 3.1Can run on consumer devices, andGemma 3of27BThe difference is due to the parameter allocation strategy:Mistraltend to compress redundant layers,GemmaMore parameters are retained to improve the ability of complex tasks.
3. Performance Showdown: Can 24B beat 27B?
Parameter size is not the only factor that determines the outcome, actual performance is more important. Here is a benchmark comparison between the two:
• MMLU(Comprehensive knowledge):Mistral Small 3.1Score81%,Gemma 3 27Babout79%• GPQA(Question and answer ability):Mistral 24BLeading, especially in low-latency scenarios• MATH(Mathematical reasoning):Gemma 3 27BWinning, thanks to more parameters supporting complex calculations• Multimodal tasks ( MM-MT-Bench):Mistral 24BStronger performance, smoother image + text understanding
The following figure shows the performance comparison between the two (the data is hypothetical and based on trend speculation):
MMLU | 81% | 79% |
GPQA | 85% | 80% |
MATH | 70% | 78% |
MM-MT-Bench | 88% | 75% |
Mistral Small 3.1Achieve multi-task balance with fewer parameters, andGemma 3Win by relying on parameter advantages in specific fields.
4. Ecosystem and Application: How to implement the parameters?
Mistral Small 3.1of24BParameter matchingApache 2.0The license is unparalleled in openness, and developers can fine-tune it locally to adapt it to scenarios such as real-time conversations and intelligent customer service.Gemma 3of27BThe Ubuntu version is subject to Google's security terms and is more suitable for cloud deployment and professional applications (such as education and programming).
From parameters to applications,MistralEmphasis on efficiency,GemmaFocus on depth.24BThe lightweightMistralCloser to independent developers,27BofGemmaIt serves enterprises with abundant resources.
5. Industry impact and future: the significance of the parameter dispute
Mistral Small 3.1by24Bchallenge27B, showing the ultimate pursuit of parameter efficiency.Gemma 3It is also a technical response to the democratization of AI. In the future, lightweight models will evolve towards lower parameters and higher efficiency.Mistralhas seized the opportunity, andGemma 3Or you may need to adjust your strategy to cope with it.
Conclusion
Mistral Small 3.1of24BAlthough the parameters are less thanGemma 3of27B, but it has advantages in efficiency, multimodality and open source. It proves that "less is more" is possible, andGemma 3The other is to defend its professional field with its parameter advantage. This parameter war is not only a competition of technology, but also a preview of the future of AI. Which side do you prefer?
