GPT-5.4 vs SAM 3
Compare GPT-5.4 and SAM 3 side-by-side. See how these vision models stack up in Object Detection.
Compare GPT-5.4 vs SAM 3 live
Run the same image across every model that supports a task and compare their outputs side-by-side.
Detect and compare bounding boxes across models on the same image.
Upload an image
Drag and drop an image here, or click to browse
Models in this comparison
GPT-5.4 vs SAM 3: Overview
GPT-5.4 is a proprietary multimodal large language model developed by OpenAI and released on March 5, 2026. It is designed for professional workloads such as advanced software development, research, and agentic automation. The model combines the general reasoning capabilities of the GPT-5 series with software engineering improvements derived from GPT-5.3-Codex. In the API and Codex environments it supports context windows of up to 1 million tokens, enabling long-context reasoning and large-scale code or document workflows.
Compared with GPT-5.2, GPT-5.4 reduces false individual claims by 33% and lowers overall response errors by 18%, improving factual reliability across complex tasks. It is also the first general-purpose OpenAI release with native computer-use capabilities, allowing agents to interact with desktops, browsers, and external applications to complete multi-step workflows. The model family includes three variants: GPT-5.4 (standard), GPT-5.4 Pro for higher-performance workloads, and GPT-5.4 Thinking, a reasoning-oriented version in ChatGPT that presents an upfront plan before generating its response. The API also introduces a Tool Search system that allows models to retrieve tool definitions dynamically, reducing token usage in tool-heavy integrations.
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.
GPT-5.4 vs SAM 3 Comparison Table
| Property | GPT-5.4 | SAM 3 |
|---|---|---|
| Organization | OpenAI | Meta |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Mar 2026 | Nov 2025 |
| Context Window | 1.1M | — |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $2.50 | |
| Output $/1M | $15.00 | |
| Vision Tasks | ||
| Object Detection | Demo | Demo |
| Captioning | Demo | |
| Classification | Demo | |
| Instance Segmentation | ||
| OCR | Demo | |
| Promptable Concept Segmentation | Demo | |
| Video Object Tracking | ||
| Vision Language | ||
| Visual Question Answering | Demo | |
| Zero Shot Segmentation | ||
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
| Zero-shot Detection | ||
Vision Evalspass/fail results · 67 prompts Score key:≥75%40–74%<40% | ||
| Overall Score | 77.61% | |
| Avg Response Time | 7.16s | |
| Median input tokensincl. image tokens | 1.4K | |
| Median output tokens | 108 | |
| Est. cost / taskon this benchmark | $0.0052 | |
| Defect Detection | 86.7%(13/15) | |
| Document Understanding | 88.9%(8/9) | |
| Object Counting | 40%(4/10) | |
| Object Understanding | 85.7%(12/14) | |
| Spatial Understanding | 78.9%(15/19) | |
Output tokens (incl. reasoning) and est. cost / task are measured on this benchmark from a single low-temperature run, and shown only for models whose run covered at least 90% of prompts. Methodology