GPT-5.5 vs SAM 3
Compare GPT-5.5 and SAM 3 side-by-side. See how these vision models stack up in Object Detection.
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Models in this comparison
GPT-5.5 vs SAM 3 Comparison Table
Evals updated October 8, 2026Pricing updated October 11, 2026
| Property | GPT-5.5 | SAM 3 |
|---|---|---|
| Organization | OpenAI | Meta |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Apr 2026 | Nov 2025 |
| Context Window | 1.0M | — |
| Parameters | Unknown | Unknown |
| License | Proprietary | Custom |
| Pricing per 1M tokens | ||
| Input $/1M | $5.00 | No published price |
| Output $/1M | $30.00 | No published price |
| Vision Tasks | ||
| Object Detection | Demo | Demo |
| Captioning | Demo | Not listed |
| Chart Question Answering | Supported | Not listed |
| Classification | Demo | Not listed |
| Document Question Answering | Supported | Not listed |
| Image Tagging | Supported | Not listed |
| Instance Segmentation | Not listed | Supported |
| Multi-Label Classification | Supported | Not listed |
| OCR | Demo | Not listed |
| Open Vocabulary Object Detection | Not listed | Supported |
| Promptable Concept Segmentation | Not listed | Demo |
| Video Object Tracking | Not listed | Supported |
| Vision Language | Supported | Not listed |
| Visual Question Answering | Demo | Not listed |
| Zero Shot Segmentation | Not listed | Supported |
| Model Features | ||
| Foundation Vision | Supported | Supported |
| Multimodal Vision | Supported | Supported |
| LLMs with Vision Capabilities | Supported | Not listed |
| Zero-shot Detection | Not listed | Supported |
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 66.0% | Not evaluated |
| Avg cost / sample | $0.025 | – |
| Avg speed / sample | 10.10s | – |
| By task | ||
| Object Detection (low) | 43.6% ±2.2, Mean of 3 runs, range 41.7 to 46.1 | – |
| Object Detection (high) | 44.2% ±0.6, Mean of 3 runs, range 43.5 to 44.8 | – |
| Counting (low) | 68.0% ±3.4, Mean of 3 runs, range 64.9 to 71.6 | – |
| Counting (high) | 68.0% ±1.4, Mean of 3 runs, range 66.2 to 68.9 | – |
| Identification (low) | 89.6% ±1.6, Mean of 3 runs, range 87.5 to 90.6 | – |
| Identification (high) | 88.5% ±1.6, Mean of 3 runs, range 87.5 to 90.6 | – |
| OCR (low) | 58.4% | – |
| by category |
| |
| OCR (high) | 60.6% | – |
| by category |
| |
| Reasoning (low) | 70.6% ±1.0, Mean of 3 runs, range 69.5 to 71.5 | – |
| Reasoning (high) | 72.2% ±3.6, Mean of 3 runs, range 68.9 to 76.2 | – |
GPT-5.5 vs SAM 3: Overview
GPT-5.5 is a multimodal large language model released by OpenAI on April 23, 2026, engineered for autonomous, multi-step knowledge work and agentic workflows. It accepts text, images, and code as input, featuring enhanced spatial reasoning and visual grounding to support its computer use capabilities for operating software and navigating UI elements. Built to execute complex workflows end-to-end, the model interprets loosely defined tasks, selects appropriate tools, and performs self-verification with minimal user intervention. It is available in a standard version, a Thinking mode for extended reasoning budgets, and a Pro variant that uses parallel test-time compute for maximum precision on complex tasks.
Co-optimized with NVIDIA for GB200 NVL72 infrastructure, GPT-5.5 delivers per-token latency comparable to its predecessor GPT-5.4 while maintaining a 1-million-token context window. Despite increased capability, the model achieves greater token efficiency in coding and data analysis workflows, often completing tasks with fewer total tokens than previous versions. OpenAI reports a 60% reduction in hallucination rate compared to GPT-5.4, improving reliability for accuracy-sensitive applications. API access is available via the Responses and Chat Completions endpoints.
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.
Frequently Asked Questions
SAM 3 has not yet been evaluated on Roboflow's current Vision Evals, so this comparison shows specs, licensing, and pricing rather than benchmark scores.
GPT-5.5 is released under Proprietary, while SAM 3 uses Custom. Licensing often matters more than raw accuracy for commercial deployments, so check the terms against how you plan to ship.
Yes. The comparison demo on this page runs both models on the same image side by side for object detection in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.
