Mistral Small 3.1 vs. Gemma 3: Can the 24 billion parameter challenge 27 billion?

Lightweight large models are becoming the new battleground in AI. Following the launch of Google DeepMind's Gemma 3 Later.Mistral AI Released in March 2024 Mistral Small 3.1. With its efficiency, multimodal capabilities, and open-source nature, this 24 billion-parameter model has generated a lot of attention and claimed to outperform in several benchmarks the Gemma 3 cap (a poem) GPT-4o Mini.. The parameter scale is a key measure of the model's performance and efficiency, and is directly related to the model's application prospects. In this paper, we will compare Mistral Small 3.1 cap (a poem) Gemma 3 parameters and analyze their similarities and differences from a number of perspectives, including performance, technology, application, and ecology.

I. Comparison of parameter sizes: $24 billion vs $27 billion, who is stronger?

Mistral Small 3.1 With 24 billion parameters, and Gemma 3 Multiple versions are available for 1 billion, 4 billion, 12 billion, and 27 billion parameters, with the 27 billion parameter version being its flagship model. The parameter size directly determines the capacity and computational requirements of the model:

Mistral Small 3.1 (24B)

• Context window: 128k tokens
• Reasoning speed: 150 tokens/s
• Hardware requirements: single RTX 4090 or a Mac with 32GB of RAM.
• Multi-modal support: text + image

Gemma 3 (27B)

• Context window: 96k tokens
• Reasoning speed: ~120 tokens/s (officially unspecified, based on community testing)
• Hardware requirements: recommended dual GPU or high-end servers (A100 40GB)
• Multimodal support: text + some visual tasks

Although the number of senators is lower by 3B.Mistral Small 3.1 Longer context windows and higher inference speeds are achieved.Gemma 3 Although the number of parameters is slightly better, it requires stronger hardware support. The table below visually compares the parameters and performance of the two:

It can be seen thatMistral Small 3.1 Better in terms of parameter efficiency, with fewer parameters to match or even surpass the Gemma 3 The performance of the

Second, the performance showdown: who is the king of lightweight?

The number of parameters is not the only criterion that determines whether a model is good or bad, the actual performance is the key. Below is a comparison of the two models in some common benchmark tests:

• MMLU (General Knowledge): Mistral Small 3.1 Score 81%.Gemma 3 27B Approx. 79%
• GPQA (Question and Answer Ability): Mistral 24B Leading the way, especially in low-latency scenarios
• MATH (Mathematical Reasoning): Gemma 3 27B Wins thanks to more parameters to support complex calculations
• Multimodal Tasks (MM-MT-Bench): Mistral 24B Stronger performance and smoother image + text comprehension

The following table shows the performance comparison of the two models in different test programs (data are hypothetical values, based on trend speculation):

From the test results.Mistral Small 3.1 It performs well in multiple tasks and achieves balanced multitasking. While Gemma 3 Then, in specific areas, such as mathematical reasoning, an advantage is achieved by virtue of more parameters.

Third, the technical highlights: small parameters, big wisdom

Mistral Small 3.1 's 24 billion parameters support multimodal capabilities (text + image) and ultra-long context processing, thanks to its hybrid attention mechanism and sparse matrix optimization. In contrast, theGemma 3 The 27-billion-parameter version relies on Google's Gemini technology stack, with more strengths in multilingualism (140+ languages) and specialized reasoning (e.g., math, code), but relatively weak multimodal capabilities.

Hardware friendliness is another notable difference.Mistral Small 3.1 can run smoothly on consumer-grade devices, while the Gemma 3 The 27 billion parameter version of Gemma is better suited for deployment on enterprise-class servers. This difference stems from the two companies' different parameter allocation strategies: Mistral tends to streamline the model structure, while Gemma chooses to keep more parameters to improve its ability to handle complex tasks.

IV. Applications and ecology: who is more grounded?

Mistral Small 3.1 adopted Apache 2.0 licenses, better openness, and developers can fine-tune the model locally for application scenarios such as real-time conversations and intelligent customer service. While Gemma 3 The 27 billion parameter version is subject to Google's security terms and is more suitable for deployment in the cloud for specialized applications such as education and programming.

In terms of applications.Mistral Small 3.1 More emphasis is placed on efficiency and flexibility for scenarios that require quick response and personalization. While Gemma 3 On the other hand, it is more focused on depth and specialization, and is suitable for handling complex professional tasks.

On the ecological front.Mistral With its openness and hardware friendliness, it's easier to attract indie developers and small teams. While Gemma Google's strong ecosystem allows it to better serve large enterprises and research organizations.

V. Industry impact and outlook

Mistral Small 3.1 achieves performance that matches or even exceeds that of Gemma 3 with fewer parameters, reflecting the ultimate pursuit of parameter efficiency. This is not only a testament to the Gemma 3 The technical challenges of AI are also a boost to its popularization.

In the future, the trend for lightweight models will be toward fewer parameters and greater efficiency. mistral has already taken the lead in this area, and Gemma 3 may need to adapt its strategy to meet this challenge.

Lighter, faster, stronger AI models are coming into our lives at an accelerated pace.