GPT-5.6 Sol vs GPT-6 Sol
GPT-6 Sol and GPT-5.6 Sol score 76.9% vs 72.4% averaged across five Vision Evals tasks (#13 and #19 of 65 models), so GPT-6 Sol leads overall.
- GPT-6 Sol wins all 5 tasks, with its widest lead on reasoning (72.6% vs 66.0%).
- GPT-6 Sol is about a third of the cost: $0.0074 vs $0.019 overall average cost per sample.
- GPT-6 Sol is faster: 10.2s vs 12.2s per sample.
Roboflow Vision Evals. Full breakdown below.
Compare GPT-5.6 Sol vs GPT-6 Sol 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
GPT-5.6 Sol vs GPT-6 Sol on Vision Evals
Roboflow Vision Evals. Full breakdown below.
GPT-5.6 Sol vs GPT-6 Sol Comparison Table
Evals updated October 26, 2026Pricing updated November 7, 2026
| Property | GPT-5.6 Sol | GPT-6 Sol |
|---|---|---|
| Organization | OpenAI | OpenAI |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Jul 2026 | Sep 2026 |
| Context Window | 1.5M | 1.1M |
| Parameters | Unknown | undisclosed |
| License | Proprietary | Proprietary |
| Commercial use | commercial use per vendor terms | commercial use per vendor terms |
| Pricing per 1M tokens | ||
| Input $/1M | $4.00 | $2.00 |
| Output $/1M | $20.00 | $10.00 |
| Vision Tasks | ||
| Promptable Concept Segmentation | Not listed | Demo |
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 72.4% | 76.9% |
| Avg cost / sample | $0.019 | $0.0074 |
| Avg speed / sample | 12.16s | 10.20s |
| By task | ||
| Object Detection (low) | 68.4% ±0.7, Mean of 3 runs, range 67.9 to 69.3 | 74.7% ±0.7, Mean of 3 runs, range 74.1 to 75.5 |
| Object Detection (high) | 68.4% ±0.8, Mean of 3 runs, range 67.7 to 69.3 | 76.4% ±1.2, Mean of 3 runs, range 75.0 to 77.4 |
| Counting (low) | 74.3% ±1.4, Mean of 3 runs, range 73.0 to 75.7 | 76.6% ±2.0, Mean of 3 runs, range 74.3 to 78.4 |
| Counting (high) | 76.1% ±2.0, Mean of 3 runs, range 74.3 to 78.4 | 77.5% ±1.3, Mean of 3 runs, range 75.7 to 78.4 |
| Identification (low) | 89.6% ±4.7, Mean of 3 runs, range 84.4 to 93.8 | 93.8% ±0.0, Mean of 3 runs, range 93.8 to 93.8 |
| Identification (high) | 92.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 | 92.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 |
| OCR (low) | 63.6% | 66.7% |
| by category |
|
|
| OCR (high) | 65.3% | 68.8% |
| by category |
|
|
| Reasoning (low) | 66.0% ±2.6, Mean of 3 runs, range 63.6 to 68.9 | 72.6% ±3.6, Mean of 3 runs, range 69.5 to 76.8 |
| Reasoning (high) | 71.7% ±1.3, Mean of 3 runs, range 70.2 to 72.8 | 77.7% ±1.0, Mean of 3 runs, range 76.8 to 78.8 |
GPT-5.6 Sol vs GPT-6 Sol: Overview
GPT-5.6 Sol is the flagship model in OpenAI's GPT-5.6 family, which also includes Terra (a balanced everyday-work tier) and Luna (a fast, cost-efficient tier). Sol is designed for demanding reasoning, long-horizon agentic workflows, software engineering, computer use, scientific research, and cybersecurity tasks. It introduces two new capability modes: a "max" reasoning effort setting that allocates additional compute time for difficult problems, and an "ultra" mode that coordinates multiple subagents in parallel to accelerate complex, multi-step work. The model supports native multimodal input, allowing it to process screenshots, diagrams, charts, documents, and photographs alongside text. A reported context window of approximately 1.5 million tokens enables processing of large codebases, lengthy research documents, and extended agentic sessions.
GPT-5.6 Sol was announced on June 26, 2026, initially in a limited preview for trusted partners, and reached general availability on July 9, 2026. On the Agents' Last Exam benchmark, which evaluates long-running professional workflows across 55 fields, Sol scores 53.6. The model also demonstrates gains in life sciences evaluations, including long-horizon genomics and quantitative biology analyses. OpenAI paired the release with its most extensive safety evaluation to date, combining human red teaming with large-scale automated testing, and classified Sol as High capability in both cybersecurity and biological risk under its Preparedness Framework, though it does not cross the Critical threshold in either category.
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. Its vision behavior covers image understanding tasks such as visual question answering, captioning, document and chart interpretation, and text recognition.
Frequently Asked Questions
On Roboflow's Vision Evals, GPT-6 Sol performed better. It scores higher on all five vision tasks and averages 76.9% (#13 of 65) against 72.4% (#19 of 65) for GPT-5.6 Sol. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
No. On the Vision Evals Reasoning benchmark at low effort, GPT-6 Sol leads with 72.6% against 66.0%. This is the widest gap between the two models across the benchmark's tasks.
GPT-6 Sol is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0074 per sample against $0.019. GPT-5.6 Sol is priced at $4.00 per 1M input tokens and $20.00 per 1M output; GPT-6 Sol is priced at $2.00 per 1M input tokens and $10.00 per 1M output. Actual costs depend on your image sizes, prompts, and output length.
GPT-6 Sol is faster. Across Roboflow's Vision Evals it averaged 10.2s per inference against 12.2s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.
Yes. The comparison demo on this page runs both models on the same image side by side for OCR and image captioning in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.