Gemini 3.5 Flash-Lite vs SAM 3
Compare Gemini 3.5 Flash-Lite and SAM 3 side-by-side. See how these vision models stack up in Object Detection.
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Gemini 3.5 Flash-Lite vs SAM 3 Comparison Table
Evals updated July 10, 2026Pricing updated July 21, 2026
| Property | Gemini 3.5 Flash-Lite | SAM 3 |
|---|---|---|
| Organization | Meta | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Jul 2026 | Nov 2025 |
| Context Window | 1.0M | — |
| Parameters | ||
| License | Proprietary | Proprietary |
| Vision Tasks | ||
| Object Detection | Demo | Demo |
| Captioning | Demo | |
| Classification | Demo | |
| Document Question Answering | ||
| Instance Segmentation | ||
| Multi-Label Classification | ||
| OCR | Demo | |
| Promptable Concept Segmentation | Demo | |
| Video Classification | ||
| Video Object Tracking | ||
| Vision Language | ||
| Visual Question Answering | Demo | |
| Zero Shot Segmentation | ||
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
| Real-Time Vision | ||
| Zero-shot Detection | ||
Gemini 3.5 Flash-Lite vs SAM 3: Overview
Gemini 3.5 Flash-Lite is a natively multimodal reasoning model developed by Google DeepMind, released on July 21, 2026 as part of the Gemini 3.5 model family. It is the fastest model in the 3.5 series, designed for both low-latency tasks and high-throughput production workloads such as agentic search, document processing, receipt translation, and large-scale data extraction. The model accepts text, images, audio, and video as inputs, with a context window of up to 1 million tokens, and produces text output. It supports configurable thinking levels, allowing developers to tune the balance between response quality, cost, and latency depending on workload requirements.
On agentic and coding benchmarks, Gemini 3.5 Flash-Lite significantly outperforms its predecessor, Gemini 3.1 Flash-Lite, including on Terminal-Bench 2.1 (54% vs. 31%), GDM-MRCR v2 long-context (72.2% vs. 60.1%), and real-world task execution as measured by GDPval-AA v2 (1140 vs. 642). It also surpasses Gemini 3 Flash on SWE-Bench Pro (54.2% vs. 49.6%) and OSWorld-Verified (74.0% vs. 65.1%). According to the Artificial Analysis Index, the model generates output at approximately 350 tokens per second. It is built on the Gemini 3.5 Flash foundation and is evaluated across reasoning, coding, multimodal understanding, multilingual performance, and long-context tasks. The model is developed under Google's Frontier Safety Framework.
Released on November 19th, 2025, Segment Anything 3 (SAM 3) is a zero-shot image segmentation model that “detects, segments, and tracks objects in images and videos based on concept prompts.” This model was developed by Meta as the third model in the Segment Anything series.
Unlike its previous SAM models (Segment Anything and Segment Anything 2), you can provide SAM 3 with the prompt “shipping container” and it will generate precise segmentation masks for all shipping containers in an image. SAM 3 generates segmentation masks that correspond to the location of the objects found with a text prompt.
