Gemini 3.6 Flash vs SAM 3
Compare Gemini 3.6 Flash and SAM 3 side-by-side. See how these vision models stack up in Object Detection.
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Gemini 3.6 Flash vs SAM 3 Comparison Table
Evals updated July 10, 2026Pricing updated July 21, 2026
| Property | Gemini 3.6 Flash | 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 | |
| Chart Question Answering | ||
| classification | Demo | |
| Document Question Answering | ||
| Instance Segmentation | ||
| OCR | Demo | |
| Promptable Concept Segmentation | Demo | |
| Video Classification | ||
| Video Object Tracking | ||
| Vision Language | ||
| Visual Question Answering | Demo | |
| Zero Shot Segmentation | ||
| Model Features | ||
| Zero-shot Detection | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Gemini 3.6 Flash vs SAM 3: Overview
Gemini 3.6 Flash is a multimodal language model from Google DeepMind, positioned as the workhorse tier in the Gemini 3.x family. It accepts text, image, video, audio, and PDF inputs with a 1 million token context window and produces up to 64,000 output tokens. The model builds directly on Gemini 3.5 Flash, incorporating developer and customer feedback to improve token efficiency, coding quality, and knowledge work performance. According to the Artificial Analysis Index, it consumes 17% fewer output tokens than its predecessor, and on some benchmarks such as DeepSWE, token reduction reaches up to 65%. It supports function calling, structured output, search as a tool, and code execution, and includes computer use as a built-in capability in the Gemini API and Gemini Enterprise.
On coding benchmarks, Gemini 3.6 Flash scores 49% on DeepSWE versus 37% for 3.5 Flash, and 63.9% on MLE Bench versus 49.7%. Computer use performance on OSWorld-Verified improves from 78.4% to 83%, and knowledge work scores on GDPval-AA v2 rise from 1349 to 1421. The model carries a knowledge cutoff of March 2026 and ships with enhanced Frontier Safety safeguards covering chemical, biological, radiological, nuclear, and cyber offense domains, with training to minimize refusals for beneficial uses. It is a proprietary, closed-weights model available in preview through the Gemini API via Google AI Studio, Android Studio, Google Antigravity, Gemini Enterprise, and the Gemini app.
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.
