GPT-6.1 Sol vs Qwen3.7 Plus
Compare GPT-6.1 Sol and Qwen3.7 Plus side-by-side. See how these vision models stack up in Image Captioning, OCR, Classification, Object Detection, and Open Prompt.
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Models in this comparison
GPT-6.1 Sol vs Qwen3.7 Plus on Vision Evals
GPT-6.1 Sol scores higher on all five Vision Evals tasks.
The widest gap is Reasoning, where GPT-6.1 Sol leads 83.7% to 39.7%.
Overall, GPT-6.1 Sol averages 81.7% (#5 of 63) against 58.9% (#37 of 63) for Qwen3.7 Plus.
Qwen3.7 Plus is both cheaper ($0.0008 vs $0.0068 per sample) and faster (7.8s vs 12.8s per sample).
GPT-6.1 Sol vs Qwen3.7 Plus Comparison Table
Evals updated October 13, 2026Pricing updated October 17, 2026
| Property | GPT-6.1 Sol | Qwen3.7 Plus |
|---|---|---|
| Organization | OpenAI | Qwen |
| Category | closed | closed |
| Modality | multimodal | — |
| Release Date | Sep 2026 | Jun 2026 |
| Context Window | 1.1M | — |
| Parameters | undisclosed | Unknown |
| License | Proprietary | Unknown |
| Commercial use | commercial use per vendor terms | See license terms |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | $0.320 |
| Output $/1M | $10.00 | $1.28 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Classification | Demo | Demo |
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Visual Question Answering | Demo | Demo |
| Chart Question Answering | Supported | Not listed |
| Document Question Answering | Supported | Not listed |
| Image Tagging | Supported | Not listed |
| Multi-Label Classification | Supported | Not listed |
| Promptable Concept Segmentation | Demo | Not listed |
| Vision Language | Supported | Not listed |
| Model Features | ||
| Foundation Vision | Supported | Not listed |
| LLMs with Vision Capabilities | Supported | Not listed |
| Multimodal Vision | Supported | Not listed |
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 81.7% | 58.9% |
| Avg cost / sample | $0.0068 | $0.0008 |
| Avg speed / sample | 12.82s | 7.77s |
| By task | ||
| Object Detection (low) | 80.8% ±0.1, Mean of 3 runs, range 80.7 to 80.9 | 60.1% |
| Object Detection (high) | 81.6% ±0.4, Mean of 3 runs, range 81.1 to 82.0 | – |
| Counting (low) | 78.8% ±3.4, Mean of 3 runs, range 75.7 to 82.4 | 50.0% |
| Counting (high) | 80.2% ±3.4, Mean of 3 runs, range 77.0 to 83.8 | – |
| Identification (low) | 89.6% ±1.6, Mean of 3 runs, range 87.5 to 90.6 | 84.4% |
| Identification (high) | 88.5% ±1.6, Mean of 3 runs, range 87.5 to 90.6 | – |
| OCR (low) | 75.8% | 60.3% |
| by category |
|
|
| OCR (high) | 75.8% | 65.5% |
| by category |
|
|
| Reasoning (low) | 83.7% ±1.3, Mean of 3 runs, range 82.1 to 84.8 | 39.7% |
| Reasoning (high) | 88.7% ±2.0, Mean of 3 runs, range 87.4 to 91.4 | 68.2% |
GPT-6.1 Sol vs Qwen3.7 Plus: 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.
On the visual side, the model is evaluated on GDP.pdf, a benchmark that asks professional questions about complex PDF documents containing tables, charts, diagrams, and fine-print details, and on OSWorld 2.0, which measures agents operating graphical computer applications. 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.
Frequently Asked Questions
On Roboflow's Vision Evals, GPT-6.1 Sol performed better. It scores higher on all five vision tasks and averages 81.7% (#5 of 63) against 58.9% (#37 of 63) for Qwen3.7 Plus. 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 39.7%. This is the widest gap between the two models across the benchmark's tasks.
Qwen3.7 Plus is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0008 per sample against $0.0068. GPT-6.1 Sol is priced at $2.00 per 1M input tokens and $10.00 per 1M output; Qwen3.7 Plus is priced at $0.32 per 1M input tokens and $1.28 per 1M output. Actual costs depend on your image sizes, prompts, and output length.
Qwen3.7 Plus is faster. Across Roboflow's Vision Evals it averaged 7.8s 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.