Google's Gemini 3 Flash, which became the default model in the Gemini app on December 17, is consolidating workflows that previously required multiple specialized models. The approach: use multimodal reasoning and code execution to handle image generation, segmentation, and detection in a single pipeline.
Traditional computer vision workflows meant orchestrating separate systems. Background removal required models like SAM. Object detection needed YOLO or custom OpenCV scripts. Each step added integration overhead. Gemini 3 Flash's code execution capability changes this by writing and running Python scripts in a sandboxed environment to manipulate images programmatically.
What it does
The model handles background removal by generating OpenCV code that converts images to grayscale, applies threshold masks, and outputs transparent PNGs. For object detection, it writes HSV color segmentation logic, calculates contours, and draws bounding boxes based on specified criteria.
Google positions this as part of the model's multimodal function responses, which now support returning images and PDFs alongside text. The model includes a 1M token context window and configurable media resolution control to balance token usage against processing granularity.
The trade-offs
Google claims 30% fewer tokens versus Gemini 2.5 Pro and 50% improvements in image editing response times. These are internal benchmarks measured on "typical traffic," not specialized computer vision workloads. How Gemini 3 Flash performs against dedicated segmentation and detection models on production datasets remains unvalidated by independent testing.
The model is currently in preview, indicating potential for changes. The code execution environment is sandboxed, which adds security but may limit certain processing approaches. For enterprises running established vision pipelines, the question is whether consolidation justifies migration risk.
What this means
This represents a consolidation play. Instead of maintaining separate models for generation, segmentation, and detection, teams could run everything through one multimodal system. The approach works if your use cases fit the model's capabilities and if the cost-performance profile beats your current stack.
The broader pattern: foundation models absorbing specialized tasks. Whether that's better than purpose-built tools depends on your specific requirements and risk tolerance. The model is available now in Google AI Studio for testing.
Worth noting: the examples shown (T-Rex cutouts, LEGO brick detection) are demonstration cases. Production computer vision often involves edge cases, specific accuracy requirements, and performance constraints that require validation beyond demos.