Mistral AI Unveils Magistral Small 1.2: The Multimodal Reasoning Powerhouse

• Parameters: 24 Billion
• Context Window: 128k Tokens
• Languages: 24 Supported
• Hardware: RTX 4090 / MacBook Pro 32GB

Performance & Benchmarks

Mistral AI has demonstrated substantial improvements in Magistral Small 1.2 compared to the 1.1 version. The model achieves over 10% performance improvement on critical benchmarks like AIME (American Invitational Mathematics Examination) and LiveCodeBench. These metrics indicate a significant leap in mathematical reasoning and code generation capabilities, essential for developer-focused applications.

In terms of general knowledge and language understanding, the model secures top-tier scores across public benchmarks. It outperforms many larger models in specific reasoning tasks due to its optimized attention mechanisms. The addition of the visual encoder also results in improved performance on multimodal benchmarks, where it can accurately identify objects and relationships within images while reasoning about them.

Sampling parameters have been tuned for optimal speed and quality. The model delivers faster time-to-first-token (TTFT) compared to previous iterations, making it suitable for real-time applications. This balance of speed and accuracy ensures that Magistral Small 1.2 is not just a research project but a production-ready tool for enterprise and personal use.

• AIME Score: +10% vs 1.1
• LiveCodeBench: Top-Tier Scores
• TTFT: Reduced Latency
• MMLU: High Accuracy

API Pricing

For users accessing the model via the API, Mistral AI offers competitive pricing structures designed to scale with usage. The pricing model is transparent, allowing developers to estimate costs accurately before deployment. This cost-effectiveness is a primary driver for adoption among startups and large enterprises alike.

The input and output token costs are optimized for the 24B parameter size. Users can expect low-cost inference for text-only tasks, with additional charges for multimodal inputs. The pricing structure encourages experimentation, making it viable to test the model's capabilities on large datasets without prohibitive financial barriers.

Free tier availability is also a consideration for developers. While the API is primarily pay-per-token, there are often credits available for new accounts to facilitate testing. This approach lowers the entry barrier for new users and allows for rapid prototyping of applications that rely on the model's reasoning and visual capabilities.

• Input Price: $0.20 per million tokens
• Output Price: $0.60 per million tokens
• Free Tier: Available for testing
• Volume Discounts: Available

Comparison Table

When evaluating Magistral Small 1.2 against competitors, it becomes clear where it fits in the ecosystem. The following table highlights the key differences between Magistral Small 1.2 and other leading models in the reasoning and multimodal space. Developers can use this data to select the model that best fits their specific performance and budget requirements.

• Direct competitor analysis provided below
• Context window and pricing compared

Use Cases

The versatility of Magistral Small 1.2 opens up numerous application scenarios. In the realm of software development, the model excels at code generation and debugging, leveraging its strong performance on LiveCodeBench. Developers can integrate it into IDEs to provide real-time suggestions and error analysis.

For enterprise applications, the model is ideal for RAG (Retrieval-Augmented Generation) systems that require reasoning over both text and document images. It can analyze scanned contracts, diagrams, and manuals to extract actionable insights. This capability is particularly valuable for legal, financial, and engineering sectors where precision is paramount.

Additionally, the model is well-suited for autonomous agents. These agents can perceive their environment through images and reason about actions to achieve goals. The multimodal nature of the model allows these agents to interact with the physical or digital world more effectively than text-only models.

• Coding & Debugging
• Document Analysis & RAG
• Autonomous Agents
• Visual Reasoning Tasks

Getting Started

Accessing Magistral Small 1.2 is straightforward for developers. The model is available on Hugging Face under the official Mistral AI organization. Users can download the weights in Safetensors format for local deployment or use the provided SDKs for API integration.

To integrate the model into an application, developers can utilize the Mistral AI API. The documentation provides clear examples for both Python and JavaScript. For local deployment, the model supports vLLM, enabling efficient serving on standard GPU clusters.

Documentation and community support are robust, ensuring that teams can resolve issues quickly. The official Mistral Docs provide comprehensive guides on model cards, benchmark results, and sampling parameters. This support structure accelerates the development lifecycle for teams adopting the new model.

• Platform: Hugging Face
• SDKs: Python, JavaScript
• Serving: vLLM Support
• Docs: Official Mistral Docs

Comparison

API Pricing — Input: $0.20 / Output: $0.60 / Context: 128k

Sources