Mistral Small 3.2 24B
Mistral's 24B instruction-tuned model with vision input, robust function calling, and a 128K context.
From Mistral, Mistral Small 3.2 24B is an open-weight text model. It is multimodal: alongside a text prompt it accepts images, then replies with generated text. Its context window handles up to 131K input tokens. It runs entirely on your own hardware through Ollama: no API key and no per-use fees.
- Released
- Jun 2025
- Pricing
- Free, runs on your hardware
- Inputs
- Text, Images
- Context window
- 131K in
- Text rank
- #213
- Vision rank
- #125
Samples
Generated with Mistral Small 3.2 24B via Ollama. The same three prompts run against every text model in the catalog, shown verbatim, so the only thing that changes between two models’ answers is the model.
Explain what an API is to someone who has never written code. Do not use a restaurant or waiter analogy. Plain prose only — no headings, bullet points, or markdown formatting. Maximum 50 words.
An API is like a messenger that lets different pieces of software talk to each other. Imagine you have a remote control for your TV. The buttons on the remote are like an API—they send signals to the TV to change the channel or adjust the volume. The TV understands these signals and responds accordingly.
A two-person startup is deciding whether to build their own user authentication or use a hosted provider. Give them a clear recommendation, then the single strongest argument against your own recommendation. Plain prose only — no headings, bullet points, or markdown formatting. Maximum 150 words.
Use a hosted provider like Auth0 or Firebase Authentication. It saves time, reduces security risks, and lets you focus on your core product. Hosted solutions handle compliance, scalability, and maintenance, which are complex and costly to manage in-house. The strongest argument against this is cost. Hosted providers can become expensive as your user base grows, especially if you need advanced features. For a two-person startup, the recurring fees might strain the budget, whereas building a custom solution could be a one-time investment with lower long-term costs if managed well.
Write a briefing for a non-technical executive explaining why their company's spending on AI is hard to predict, and what they can do about it. Cover at least three distinct causes. Plain prose only — no headings, bullet points, or markdown formatting. Write exactly 205 words.
Predicting AI spending is challenging for several reasons. First, AI projects often start small but scale unpredictably as teams discover new use cases or refine their models. What begins as a modest investment can grow significantly as the technology proves its value, making budgeting difficult. Second, AI requires specialized talent, and the cost of hiring or training skilled professionals can vary widely depending on market conditions and competition. These costs are hard to forecast, especially as demand for AI expertise outpaces supply. Third, AI projects frequently rely on cloud computing, where usage can spike unexpectedly due to factors like data volume or model complexity. Cloud costs are notoriously volatile, adding another layer of uncertainty. To manage this unpredictability, executives should adopt flexible budgeting approaches. Allocating funds in phases rather than upfront allows for adjustments as needs become clearer. Investing in cross-training existing employees can reduce reliance on expensive external hires. Monitoring cloud usage closely and setting spending alerts can help control costs. Finally, treating AI as an ongoing initiative rather than a one-time project encourages continuous evaluation and adjustment. By embracing adaptability, companies can navigate the uncertainties of AI spending more effectively.
Mistral
Mistral is a Paris-based lab that ships fast, efficient open-weight language models with strong reasoning per parameter.
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