GPT-5.6 Luna vs GPT-6.1 Sol
GPT-6.1 Sol and GPT-5.6 Luna score 81.7% vs 64.7% averaged across five Vision Evals tasks (#9 and #35 of 69 models), so GPT-6.1 Sol leads overall.
- GPT-6.1 Sol wins all 5 tasks, with its widest lead on OCR (75.8% vs 51.8%).
- GPT-5.6 Luna is about 1/6 of the cost: $0.0011 vs $0.0068 overall average cost per sample.
- GPT-5.6 Luna is faster: 8.6s vs 12.8s per sample.
Roboflow Vision Evals. Full breakdown below.
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GPT-5.6 Luna vs GPT-6.1 Sol on Vision Evals
Roboflow Vision Evals. Full breakdown below.
GPT-5.6 Luna vs GPT-6.1 Sol Comparison Table
Evals updated November 12, 2026Pricing updated November 13, 2026
| Property | GPT-5.6 Luna | GPT-6.1 Sol |
|---|---|---|
| Organization | OpenAI | OpenAI |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Jul 2026 | Sep 2026 |
| Context Window | 1.5M | 1.1M |
| Parameters | Unknown | undisclosed |
| License | Proprietary | Proprietary |
| Commercial use | commercial use per vendor terms | commercial use per vendor terms |
| Pricing per 1M tokens | ||
| Input $/1M | $0.200 | $2.00 |
| Output $/1M | $1.20 | $10.00 |
| Vision Tasks | ||
| Promptable Concept Segmentation | Not listed | Demo |
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 64.7% | 81.7% |
| Avg cost / sample | $0.0011 | $0.0068 |
| Avg speed / sample | 8.60s | 12.82s |
| By task | ||
| Object Detection (low) | 61.0% ±1.2, Mean of 3 runs, range 59.9 to 62.2 | 80.8% ±0.1, Mean of 3 runs, range 80.7 to 80.9 |
| Object Detection (high) | 62.3% ±1.2, Mean of 3 runs, range 61.4 to 63.8 | 81.6% ±0.4, Mean of 3 runs, range 81.1 to 82.0 |
| Counting (low) | 67.1% ±1.4, Mean of 3 runs, range 66.2 to 68.9 | 78.8% ±3.4, Mean of 3 runs, range 75.7 to 82.4 |
| Counting (high) | 70.7% ±3.4, Mean of 3 runs, range 66.2 to 73.0 | 80.2% ±3.4, Mean of 3 runs, range 77.0 to 83.8 |
| Identification (low) | 83.3% ±1.6, Mean of 3 runs, range 81.3 to 84.4 | 89.6% ±1.6, Mean of 3 runs, range 87.5 to 90.6 |
| Identification (high) | 84.4% ±6.3, Mean of 3 runs, range 78.1 to 90.6 | 88.5% ±1.6, Mean of 3 runs, range 87.5 to 90.6 |
| OCR (low) | 51.8% | 75.8% |
| by category |
|
|
| OCR (high) | 55.7% | 75.8% |
| by category |
|
|
| Reasoning (low) | 60.5% ±5.0, Mean of 3 runs, range 55.0 to 64.9 | 83.7% ±1.3, Mean of 3 runs, range 82.1 to 84.8 |
| Reasoning (high) | 65.6% ±3.6, Mean of 3 runs, range 60.9 to 68.2 | 88.7% ±2.0, Mean of 3 runs, range 87.4 to 91.4 |
GPT-5.6 Luna vs GPT-6.1 Sol: 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. 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.
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.
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% (#9 of 69) against 64.7% (#35 of 69) for GPT-5.6 Luna. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
No. On the Vision Evals OCR benchmark at low effort, GPT-6.1 Sol leads with 75.8% against 51.8%. This is the widest gap between the two models across the benchmark's tasks.
GPT-5.6 Luna is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0011 per sample against $0.0068. GPT-5.6 Luna is priced at $0.20 per 1M input tokens and $1.20 per 1M output; GPT-6.1 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-5.6 Luna is faster. Across Roboflow's Vision Evals it averaged 8.6s 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 classification and image captioning in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.