GPT-5.6 Sol vs GPT-6 Sol
Compare GPT-5.6 Sol and GPT-6 Sol side-by-side.
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.
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-5.6 Sol vs GPT-6 Sol on Vision Evals
GPT-6 Sol scores higher on 5 of the six Vision Evals tasks.
The widest gap is Reasoning, where GPT-6 Sol leads 72.2% to 66.0%.
Overall, GPT-5.6 Sol averages 79.0% (#14 of 57) against 80.7% (#10 of 57) for GPT-6 Sol.
GPT-6 Sol is both cheaper ($0.0065 vs $0.0088 per sample) and faster (8.1s vs 10.3s per sample).
GPT-5.6 Sol vs GPT-6 Sol Comparison Table
Evals updated September 22, 2026Pricing updated September 22, 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 | undisclosed | |
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | |
| Output $/1M | $10.00 | |
| Vision Tasks | ||
| Captioning | Demo | |
| Chart Question Answering | ||
| Classification | Demo | |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Object Detection | Demo | |
| OCR | Demo | |
| Vision Language | ||
| Visual Question Answering | Demo | |
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 79.0% | 80.7% |
| Avg cost / sample | $0.0088 | $0.0065 |
| Avg speed / sample | 10.32s | 8.15s |
| By task | ||
| Object Detection (low) | 68.4% ±0.7, Mean of 3 runs, range 67.9 to 69.3 | 73.6% ±0.6, Mean of 3 runs, range 72.9 to 74.2 |
| Object Detection (high) | 68.4% ±0.8, Mean of 3 runs, range 67.7 to 69.3 | 75.2% ±0.9, Mean of 3 runs, range 74.1 to 75.9 |
| Counting (low) | 74.3% ±1.4, Mean of 3 runs, range 73.0 to 75.7 | 74.8% ±2.7, Mean of 3 runs, range 71.6 to 77.0 |
| Counting (high) | 76.1% ±2.0, Mean of 3 runs, range 74.3 to 78.4 | 76.1% ±3.4, Mean of 3 runs, range 71.6 to 78.4 |
| Identification (low) | 89.6% ±4.7, Mean of 3 runs, range 84.4 to 93.8 | 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 | 92.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 |
| OCR (low) | 90.7% ±0.1, Mean of 3 runs, range 90.6 to 90.7 | 91.7% ±0.4, Mean of 3 runs, range 91.3 to 92.1 |
| OCR (high) | 90.2% ±0.2, Mean of 3 runs, range 90.0 to 90.4 | 91.9% ±0.3, Mean of 3 runs, range 91.6 to 92.2 |
| Data Extraction (low) | 84.9% ±1.0, Mean of 3 runs, range 83.5 to 85.6 | 80.4% ±0.0, Mean of 3 runs, range 80.4 to 80.4 |
| Data Extraction (high) | 86.9% ±0.5, Mean of 3 runs, range 86.6 to 87.6 | 82.5% ±1.0, Mean of 3 runs, range 81.4 to 83.5 |
| Reasoning (low) | 66.0% ±2.6, Mean of 3 runs, range 63.6 to 68.9 | 72.2% ±1.7, Mean of 3 runs, range 70.9 to 74.2 |
| Reasoning (high) | 71.7% ±1.3, Mean of 3 runs, range 70.2 to 72.8 | 77.9% ±2.6, Mean of 3 runs, range 75.5 to 80.8 |
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. On Terminal-Bench 2.1, which tests command-line agentic coding workflows, Sol Ultra achieves 91.9%. 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. 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.
Frequently Asked Questions
On Roboflow's Vision Evals, GPT-6 Sol performed better. It scores higher on 5 of the six vision tasks and averages 80.7% (#10 of 57) against 79.0% (#14 of 57) 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 72.2% 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 $0.0065 per sample against $0.0088. Actual costs depend on your image sizes, prompts, and output length.
GPT-6 Sol is faster. Across Roboflow's Vision Evals it averaged 8.1s per inference against 10.3s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.