GPT-6 Sol vs SAM 3
Compare GPT-6 Sol and SAM 3 side-by-side.
Compare GPT-6 Sol vs SAM 3 live
Run the same image across every model that supports a task and compare their outputs side-by-side.
These models don't share enough common tasks for a side-by-side demo. See the comparison table below for their capabilities.
Models in this comparison
GPT-6 Sol vs SAM 3 Comparison Table
Evals updated September 22, 2026Pricing updated September 22, 2026
| Property | GPT-6 Sol | SAM 3 |
|---|---|---|
| Organization | OpenAI | Meta |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Sep 2026 | Nov 2025 |
| Context Window | 1.1M | — |
| Parameters | undisclosed | |
| License | Proprietary | Custom |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | |
| Output $/1M | $10.00 | |
| Vision Tasks | ||
| Object Detection | Demo | |
| Captioning | ||
| Chart Question Answering | ||
| Classification | ||
| Document Question Answering | ||
| Image Tagging | ||
| Instance Segmentation | ||
| Multi-Label Classification | ||
| OCR | ||
| Open Vocabulary Object Detection | ||
| Promptable Concept Segmentation | Demo | |
| Video Object Tracking | ||
| Vision Language | ||
| Visual Question Answering | ||
| Zero Shot Segmentation | ||
| Model Features | ||
| Foundation Vision | ||
| Multimodal Vision | ||
| LLMs with Vision Capabilities | ||
| Zero-shot Detection | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 80.7% | Not evaluated |
| Avg cost / sample | $0.0065 | – |
| Avg speed / sample | 8.15s | – |
| By task | ||
| Object Detection (low) | 73.6% ±0.6, Mean of 3 runs, range 72.9 to 74.2 | – |
| Object Detection (high) | 75.2% ±0.9, Mean of 3 runs, range 74.1 to 75.9 | – |
| Counting (low) | 74.8% ±2.7, Mean of 3 runs, range 71.6 to 77.0 | – |
| Counting (high) | 76.1% ±3.4, Mean of 3 runs, range 71.6 to 78.4 | – |
| Identification (low) | 91.7% ±3.1, Mean of 3 runs, range 87.5 to 93.8 | – |
| Identification (high) | 92.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 | – |
| OCR (low) | 91.7% ±0.4, Mean of 3 runs, range 91.3 to 92.1 | – |
| OCR (high) | 91.9% ±0.3, Mean of 3 runs, range 91.6 to 92.2 | – |
| Data Extraction (low) | 80.4% ±0.0, Mean of 3 runs, range 80.4 to 80.4 | – |
| Data Extraction (high) | 82.5% ±1.0, Mean of 3 runs, range 81.4 to 83.5 | – |
| Reasoning (low) | 72.2% ±1.7, Mean of 3 runs, range 70.9 to 74.2 | – |
| Reasoning (high) | 77.9% ±2.6, Mean of 3 runs, range 75.5 to 80.8 | – |
GPT-6 Sol vs SAM 3: Overview
GPT-6 Sol is a proprietary multimodal reasoning model from OpenAI, released on September 22, 2026 alongside GPT-6 Luna as an efficiency-oriented tier of the GPT-6 family that began with GPT-6 Astra. OpenAI states that Sol and Luna are trained with methods similar to those used for Astra, carrying the same work on professional tasks, factuality, coding, computer use, and alignment into models that run faster. Sol accepts text and image input and returns text output, and OpenAI documents a context window of roughly one million tokens together with a knowledge cutoff of April 20, 2026.
The model targets complex coding and agentic workflows and exposes a configurable reasoning effort setting with levels of none, low, medium, high, xhigh, and max, which trades latency and token consumption against answer quality. OpenAI reports results including 33.2% on AutomationBench at xhigh effort and 56.4% on Agents' Last Exam at max effort, while its reported DeepSWE and OSWorld 2.0 figures of 68.8% and 64.4% fall below those of the earlier GPT-5.6 Sol. Its vision behavior covers image understanding tasks such as visual question answering, captioning, document and chart interpretation, and text recognition.
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-6 Sol 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.
