GPT-6 Sol vs Qwen3.7 Plus
Compare GPT-6 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 Sol vs Qwen3.7 Plus on Vision Evals
GPT-6 Sol scores higher on all five Vision Evals tasks.
The widest gap is Reasoning, where GPT-6 Sol leads 72.6% to 39.7%.
Overall, GPT-6 Sol averages 76.9% (#12 of 63) against 58.9% (#37 of 63) for Qwen3.7 Plus.
Qwen3.7 Plus is both cheaper ($0.0008 vs $0.0074 per sample) and faster (7.8s vs 10.2s per sample).
GPT-6 Sol vs Qwen3.7 Plus Comparison Table
Evals updated October 13, 2026Pricing updated October 17, 2026
| Property | GPT-6 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 | 76.9% | 58.9% |
| Avg cost / sample | $0.0074 | $0.0008 |
| Avg speed / sample | 10.20s | 7.77s |
| By task | ||
| Object Detection (low) | 74.7% ±0.7, Mean of 3 runs, range 74.1 to 75.5 | 60.1% |
| Object Detection (high) | 76.4% ±1.2, Mean of 3 runs, range 75.0 to 77.4 | – |
| Counting (low) | 76.6% ±2.0, Mean of 3 runs, range 74.3 to 78.4 | 50.0% |
| Counting (high) | 77.5% ±1.3, Mean of 3 runs, range 75.7 to 78.4 | – |
| Identification (low) | 93.8% ±0.0, Mean of 3 runs, range 93.8 to 93.8 | 84.4% |
| Identification (high) | 92.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 | – |
| OCR (low) | 66.7% | 60.3% |
| by category |
|
|
| OCR (high) | 68.8% | 65.5% |
| by category |
|
|
| Reasoning (low) | 72.6% ±3.6, Mean of 3 runs, range 69.5 to 76.8 | 39.7% |
| Reasoning (high) | 77.7% ±1.0, Mean of 3 runs, range 76.8 to 78.8 | 68.2% |
GPT-6 Sol vs Qwen3.7 Plus: 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.
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% (#12 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 Sol leads with 72.6% 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 $0.0074. GPT-6 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 10.2s. 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 image captioning and OCR in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.