GPT-6 Luna vs Qwen3.7 Plus
Compare GPT-6 Luna 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 Luna vs Qwen3.7 Plus on Vision Evals
GPT-6 Luna scores higher on 4 of the five Vision Evals tasks.
The widest gap is Reasoning, where GPT-6 Luna leads 64.2% to 39.7%.
Overall, GPT-6 Luna averages 69.7% (#21 of 63) against 58.9% (#37 of 63) for Qwen3.7 Plus.
GPT-6 Luna is cheaper ($0.0004 vs $0.0008 per sample), while Qwen3.7 Plus is faster (7.8s vs 10.2s per sample).
GPT-6 Luna vs Qwen3.7 Plus Comparison Table
Evals updated October 13, 2026Pricing updated October 16, 2026
| Property | GPT-6 Luna | Qwen3.7 Plus |
|---|---|---|
| Organization | OpenAI | Qwen |
| Category | closed | closed |
| Modality | multimodal | — |
| Release Date | Sep 2026 | Jun 2026 |
| Context Window | 1.1M | — |
| Parameters | Unknown | Unknown |
| License | Proprietary | Unknown |
| Commercial use | commercial use per vendor terms | See license terms |
| Pricing per 1M tokens | ||
| Input $/1M | $0.100 | $0.320 |
| Output $/1M | $0.500 | $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 | 69.7% | 58.9% |
| Avg cost / sample | $0.0004 | $0.0008 |
| Avg speed / sample | 10.21s | 7.77s |
| By task | ||
| Object Detection (low) | 65.5% ±0.4, Mean of 3 runs, range 65.2 to 66.0 | 60.1% |
| Object Detection (high) | 68.0% ±0.7, Mean of 3 runs, range 67.3 to 68.6 | – |
| Counting (low) | 71.6% ±0.0, Mean of 3 runs, range 71.6 to 71.6 | 50.0% |
| Counting (high) | 72.1% ±0.7, Mean of 3 runs, range 71.6 to 73.0 | – |
| Identification (low) | 87.5% ±3.1, Mean of 3 runs, range 84.4 to 90.6 | 84.4% |
| Identification (high) | 87.5% ±3.1, Mean of 3 runs, range 84.4 to 90.6 | – |
| OCR (low) | 59.8% | 60.3% |
| by category |
|
|
| OCR (high) | 62.5% | 65.5% |
| by category |
|
|
| Reasoning (low) | 64.2% ±3.0, Mean of 3 runs, range 60.3 to 66.2 | 39.7% |
| Reasoning (high) | 71.1% ±2.3, Mean of 3 runs, range 68.2 to 72.8 | 68.2% |
GPT-6 Luna vs Qwen3.7 Plus: Overview
GPT-6 Luna is the fast, cost-efficient tier of OpenAI's GPT-6 model family, sitting below GPT-6 Sol and the larger GPT-6 Astra model that opened the generation. It is a proprietary multimodal transformer that accepts text and image input and returns text, and it exposes an adjustable reasoning effort setting so the same model can run in a low-latency mode or spend additional inference compute on harder problems. OpenAI positions it for high-volume and latency-sensitive workloads such as conversational assistants, classification, and lightweight agentic pipelines, while noting that at higher reasoning effort it handles software engineering and computer-use tasks that previously required a Sol-tier model.
The model supports a context window of roughly 1,050,000 input tokens with a maximum output of 128,000 tokens, which allows long documents, extended agent traces, and large code repositories to be processed in a single request. OpenAI describes the GPT-6 generation as improving factual reliability and adopting a more concise communication style relative to the GPT-5.6 series, and attributes the efficiency of the Sol and Luna tiers to gains in caching and inference rather than to reduced capability. Architecture details, parameter counts, and training data are not published.
Frequently Asked Questions
On Roboflow's Vision Evals, GPT-6 Luna performed better. It scores higher on 4 of the five vision tasks and averages 69.7% (#21 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 Luna leads with 64.2% against 39.7%. This is the widest gap between the two models across the benchmark's tasks.
GPT-6 Luna is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0004 per sample against $0.0008. GPT-6 Luna is priced at $0.10 per 1M input tokens and $0.50 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.