GPT-6.1 Sol vs GPT-6 Sol
GPT-6.1 Sol and GPT-6 Sol score 81.7% vs 76.9% averaged across five Vision Evals tasks (#9 and #16 of 69 models), so GPT-6.1 Sol leads overall.
- GPT-6.1 Sol wins 4 of the 5 tasks, with its widest lead on reasoning (83.7% vs 72.6%).
- GPT-6.1 Sol is cheaper: $0.0068 vs $0.0074 overall average cost per sample.
- GPT-6 Sol is faster: 10.2s vs 12.8s per sample.
Roboflow Vision Evals. Full breakdown below.
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GPT-6.1 Sol vs GPT-6 Sol on Vision Evals
Roboflow Vision Evals. Full breakdown below.
GPT-6.1 Sol vs GPT-6 Sol Comparison Table
Evals updated November 12, 2026Pricing updated November 13, 2026
| Property | GPT-6.1 Sol | GPT-6 Sol |
|---|---|---|
| Organization | OpenAI | OpenAI |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Sep 2026 | Sep 2026 |
| Context Window | 1.1M | 1.1M |
| Parameters | undisclosed | undisclosed |
| License | Proprietary | Proprietary |
| Commercial use | commercial use per vendor terms | commercial use per vendor terms |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | $2.00 |
| Output $/1M | $10.00 | $10.00 |
| Vision Tasks | ||
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 81.7% | 76.9% |
| Avg cost / sample | $0.0068 | $0.0074 |
| Avg speed / sample | 12.82s | 10.20s |
| By task | ||
| Object Detection (low) | 80.8% ±0.1, Mean of 3 runs, range 80.7 to 80.9 | 74.7% ±0.7, Mean of 3 runs, range 74.1 to 75.5 |
| Object Detection (high) | 81.6% ±0.4, Mean of 3 runs, range 81.1 to 82.0 | 76.4% ±1.2, Mean of 3 runs, range 75.0 to 77.4 |
| Counting (low) | 78.8% ±3.4, Mean of 3 runs, range 75.7 to 82.4 | 76.6% ±2.0, Mean of 3 runs, range 74.3 to 78.4 |
| Counting (high) | 80.2% ±3.4, Mean of 3 runs, range 77.0 to 83.8 | 77.5% ±1.3, Mean of 3 runs, range 75.7 to 78.4 |
| Identification (low) | 89.6% ±1.6, Mean of 3 runs, range 87.5 to 90.6 | 93.8% ±0.0, Mean of 3 runs, range 93.8 to 93.8 |
| Identification (high) | 88.5% ±1.6, Mean of 3 runs, range 87.5 to 90.6 | 92.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 |
| OCR (low) | 75.8% | 66.7% |
| by category |
|
|
| OCR (high) | 75.8% | 68.8% |
| by category |
|
|
| Reasoning (low) | 83.7% ±1.3, Mean of 3 runs, range 82.1 to 84.8 | 72.6% ±3.6, Mean of 3 runs, range 69.5 to 76.8 |
| Reasoning (high) | 88.7% ±2.0, Mean of 3 runs, range 87.4 to 91.4 | 77.7% ±1.0, Mean of 3 runs, range 76.8 to 78.8 |
GPT-6.1 Sol vs GPT-6 Sol: Overview
GPT-6.1 Sol is a reasoning model in OpenAI's GPT-6 series that accepts text and image input and returns text. It is an upgrade to GPT-6 Sol positioned to approach the intelligence of the larger GPT-6 Astra model on agentic coding, computer use, and professional knowledge work. The model exposes an adjustable reasoning effort control, ranging from low settings for simple turns to maximum settings for harder tasks, and can be driven with tool use enabled or disabled. It operates over a context window of roughly one million tokens and emits up to 128,000 output tokens in a single response, which supports long-running agent loops over large codebases and multi-document collections. Audio and video inputs are not supported.
OpenAI reports that GPT-6.1 Sol performs on par with or better than GPT-6 Sol across its image input safety evaluations, and that the share of responses containing a factual error at low reasoning effort falls from 11.4 percent to 7.7 percent.
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-6.1 Sol performed better. It scores higher on 4 of the five vision tasks and averages 81.7% (#9 of 69) against 76.9% (#16 of 69) for GPT-6 Sol. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
Yes. On the Vision Evals Reasoning benchmark at low effort, GPT-6.1 Sol leads with 83.7% against 72.6%. This is the widest gap between the two models across the benchmark's tasks.
GPT-6.1 Sol is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0068 per sample against $0.0074. GPT-6.1 Sol is priced at $2.00 per 1M input tokens and $10.00 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 12.8s. 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 image captioning and OCR in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.