Minimal viable model
The smallest AI model that can successfully complete a specific task, balancing capability with efficiency.
A minimal viable model is the simplest or least powerful AI model that can still do the job you need it to do. Think of it like using a basic calculator for simple addition instead of a supercomputer. You don't need the most advanced tool for every task.
For example, if you just need to identify if an image contains a cat or a dog, a small, specialized model might be sufficient and much faster and cheaper to run than a large, general-purpose model that can understand complex reasoning and generate creative text. Choosing a minimal viable model saves resources like computing power and time.
A minimal viable model (MVM) is the smallest, least resource-intensive model that meets the performance requirements for a specific application. The concept emphasizes selecting a model based on task necessity rather than defaulting to the largest or most capable available. This approach optimizes for efficiency, reducing inference latency, computational cost, and energy consumption.
Tradeoffs involve a potential reduction in performance on tasks outside the MVM's specific training domain or a lower ceiling for emergent capabilities compared to larger foundation models. However, for well-defined tasks, an MVM can offer superior cost-effectiveness and speed.
Microsoft Copilot is pushing for developers to select AI models based on task requirements rather than solely on maximum capability. This suggests a trend towards optimizing AI deployment for efficiency and cost, moving away from using the most powerful model for every situation.
- Your work might not need the smartest modelMicrosoft · Microsoft for Developers ·
- Microsoft Copilot
- Microsoft Copilot is advising developers to choose the least complex model that can still complete a task effectively, rather than defaulting to the most advanced option.
- Foundation Model
- A large, general-purpose AI model trained on a vast dataset, d
- Quantized Model
- A model where numerical precision has been reduced (e.g., from
- Distilled Model
- A smaller model trained to mimic the behavior of a larger, more
The term 'minimal viable product' (MVP) originated in the startup and software development community. The adaptation to 'minimal viable model' (MVM) applies this principle to AI development, emphasizing the least complex model needed for a task.
Try using a basic image recognition tool in a photo app to identify objects. Then, try a more advanced AI art generator. Notice how the simple tool is faster and more direct for its specific task, while the art generator is more complex and takes longer, illustrating the concept of choosing the right model for the job.
The Playbook: reusable how-tos, prompts, configs and tips pulled out of the news.
- HeuristicChoose the minimal viable model for your work, not the smartestSelect the least expensive AI model that meets your specific task requirements to optimize cost and performance. · 15 min
- PromptRole-play a student struggling with math for AI tutor simulationSimulate a student's math struggles and uncertainty for AI tutor evaluation. · 5 min
- TemplateReusable workflow templates for healthcare teamsTeams can create and share templates for common workflows like documentation or prior authorizations. · 15 min
- TemplateTemplate for building an AI tool tailored to your workA structured approach to defining and building a custom AI tool for specific business needs. · 15 min
- TemplateEvals CSV prompt list for agent skillsA CSV file template for listing prompts used in evaluating agent skills. · 20 min
Questions people ask
- Why would I use a smaller model if a bigger one is smarter?
- Smaller models are often faster, cheaper to run, and consume less energy. For many specific tasks, a smaller model performs just as well as a larger one, making it a more efficient choice.
- Is a minimal viable model always less accurate?
- Not necessarily. For a precisely defined task, a smaller model trained specifically for it can be highly accurate. Accuracy depends on the task and the model's training, not just its size.
First seen on Subvolts Sep 9, 2026. Updated Sep 16, 2026. Written by an AI model from the items above and reviewed against them weekly; report an error.