GPT-5.6 Luna vs Qwen3.7 Plus
Compare GPT-5.6 Luna and Qwen3.7 Plus side-by-side. See how these vision models stack up in Classification, Image Captioning, OCR, Object Detection, and Open Prompt.
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Models in this comparison
GPT-5.6 Luna vs Qwen3.7 Plus on Vision Evals
GPT-5.6 Luna scores higher on 3 of the five Vision Evals tasks.
The widest gap is Reasoning, where GPT-5.6 Luna leads 60.5% to 39.7%.
Overall, GPT-5.6 Luna averages 64.7% (#27 of 61) against 58.9% (#35 of 61) for Qwen3.7 Plus.
Qwen3.7 Plus is both cheaper ($0.0008 vs $0.0011 per sample) and faster (7.8s vs 8.6s per sample).
GPT-5.6 Luna vs Qwen3.7 Plus Comparison Table
Evals updated October 8, 2026Pricing updated October 8, 2026
| Property | GPT-5.6 Luna | Qwen3.7 Plus |
|---|---|---|
| Organization | OpenAI | Qwen |
| Category | closed | closed |
| Modality | multimodal | — |
| Release Date | Jul 2026 | Jun 2026 |
| Context Window | 1.5M | — |
| Parameters | Unknown | Unknown |
| License | Proprietary | Unknown |
| Pricing per 1M tokens | ||
| Input $/1M | $0.200 | $0.320 |
| Output $/1M | $1.20 | $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 |
| 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 | 64.7% | 58.9% |
| Avg cost / sample | $0.0011 | $0.0008 |
| Avg speed / sample | 8.60s | 7.77s |
| By task | ||
| Object Detection (low) | 61.0% ±1.2, Mean of 3 runs, range 59.9 to 62.2 | 60.1% |
| Object Detection (high) | 62.3% ±1.2, Mean of 3 runs, range 61.4 to 63.8 | – |
| Counting (low) | 67.1% ±1.4, Mean of 3 runs, range 66.2 to 68.9 | 50.0% |
| Counting (high) | 70.7% ±3.4, Mean of 3 runs, range 66.2 to 73.0 | – |
| Identification (low) | 83.3% ±1.6, Mean of 3 runs, range 81.3 to 84.4 | 84.4% |
| Identification (high) | 84.4% ±6.3, Mean of 3 runs, range 78.1 to 90.6 | – |
| OCR (low) | 51.8% | 60.3% |
| by category |
|
|
| OCR (high) | 55.7% | 65.5% |
| by category |
|
|
| Reasoning (low) | 60.5% ±5.0, Mean of 3 runs, range 55.0 to 64.9 | 39.7% |
| Reasoning (high) | 65.6% ±3.6, Mean of 3 runs, range 60.9 to 68.2 | 68.2% |
GPT-5.6 Luna vs Qwen3.7 Plus: Overview
GPT-5.6 Luna is the fastest and most cost-efficient model in OpenAI's GPT-5.6 family, which also includes Sol (the flagship tier) and Terra (the balanced mid-tier). Introduced under a new naming convention where the generation number (5.6) and a durable capability tier name (Luna, Terra, Sol) together define each model, Luna occupies the lightweight end of the family and is designed for high-volume, latency-sensitive workloads such as summarization, drafting, autocomplete, classification, and routine automation. The GPT-5.6 family as a whole advances capabilities in software engineering, computer use, professional knowledge work, scientific research, and cybersecurity, with all three tiers rated at the "High" capability level under OpenAI's Preparedness Framework for both cybersecurity and biological/chemical risk domains.
GPT-5.6 Luna supports multimodal input and function calling, and shares the family's 1.5 million token context window. On Terminal-Bench 2.1, Luna scores 82.5%, and on the Artificial Analysis Coding Agent Index it outperforms comparable models at roughly one-quarter the estimated cost of higher-tier alternatives. Luna supports the GPT-5.6 prompt caching scheme, which introduces explicit cache breakpoints and a 30-minute minimum cache life. The model was previewed on June 26, 2026 to a limited group of trusted partners via the OpenAI API and Codex, with general availability rolling out on July 9, 2026 across ChatGPT, Codex, and the API.
Frequently Asked Questions
On Roboflow's Vision Evals, GPT-5.6 Luna performed better. It scores higher on 3 of the five vision tasks and averages 64.7% (#27 of 61) against 58.9% (#35 of 61) 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-5.6 Luna leads with 60.5% 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 $0.0011. GPT-5.6 Luna is priced at $0.20 per 1M input tokens and $1.20 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 8.6s. 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 classification and image captioning in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.