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GPT-6 Luna is on the Vision Evals leaderboard. Running it in the Playground is not available yet. View evals
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.
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Vision Evals is Roboflow's ground-truth benchmark: every model runs the same real-world samples across six vision tasks, and answers are scored against ground truth.
Evals updated September 22, 2026Pricing updated September 22, 2026
GPT-6 Luna averages 68.6% across the six Vision Evals tasks, ranking #32 of 57 models overall.
Its weakest relative showing is Data Extraction, ranking #54 of 57 at 68.0%.
At $0.0004 per sample it is the 7th cheapest of the 57 benchmarked models, and its average inference time of 11.3s per sample makes it the 33rd fastest.
Field medians: Object Detection 54.3%, Counting 61.7%, Identification 84.4%, OCR 88.7%, Data Extraction 84.5%, Reasoning 54.8%.
| Task | Score | Field (0 to 100) | Rank | Cost / sample | Speed |
|---|---|---|---|---|---|
| Object Detection (low) | 56.8% ±1.9, Mean of 3 runs, range 54.8 to 58.5 | #24 of 57 | $0.0006 | 15.29s | |
| Object Detection (high) | 64.1% ±0.5, Mean of 3 runs, range 63.6 to 64.5 | #13 of 23 | $0.0016 | 26.66s | |
| Counting (low) | 65.8% ±1.4, Mean of 3 runs, range 64.9 to 67.6 | #22 of 57 | $0.0003 | 8.88s | |
| Counting (high) | 64.4% ±2.0, Mean of 3 runs, range 62.2 to 66.2 | #19 of 23 | $0.0006 | 13.31s | |
| Identification (low) | 81.3% ±0.0, Mean of 3 runs, range 81.3 to 81.3 | #38 of 57 | $0.0002 | 6.30s | |
| Identification (high) | 80.2% ±1.6, Mean of 3 runs, range 78.1 to 81.3 | #22 of 23 | $0.0003 | 7.30s | |
| OCR (low) | 87.9% ±0.6, Mean of 3 runs, range 87.2 to 88.3 | #36 of 57 | $0.0005 | 8.59s | |
| OCR (high) | 88.5% ±0.6, Mean of 3 runs, range 87.9 to 89.2 | #21 of 23 | $0.0014 | 22.29s | |
| Data Extraction (low) | 68.0% ±3.1, Mean of 3 runs, range 65.0 to 71.1 | #54 of 57 | $0.0002 | 7.19s | |
| Data Extraction (high) | 66.7% ±0.5, Mean of 3 runs, range 66.0 to 67.0 | #23 of 23 | $0.0004 | 9.59s | |
| Reasoning (low) | 52.1% ±2.0, Mean of 3 runs, range 49.7 to 53.6 | #32 of 57 | $0.0003 | 10.13s | |
| Reasoning (high) | 60.7% ±1.7, Mean of 3 runs, range 58.9 to 62.3 | #34 of 43 | $0.0006 | 12.74s |
Overall benchmark score against estimated cost per sample, on a log scale. Upper-left is the sweet spot: high quality at low cost.
56 models on the current benchmark · scores and efficiency pooled across all six tasks at low effort · GPT-6 Luna highlighted
GPT-6 Luna scores are the mean of 3 runs per task at both low and high effort · Methodology
View all Vision Evals →GPT-6 Luna costs $0.100 per 1M input tokens and $0.500 per 1M output tokens.
Pricing updated Sep 22, 2026
Other versions in the same family as GPT-6 Luna.
GPT-6 Luna is proprietary: the weights are not distributed, and the GPT-6 Luna license is the vendor's commercial terms of service that you accept when you call the API.
Vendor terms govern data retention, whether your inputs can be trained on, rate limits, and regional availability, and they can change with notice. Review them if you handle regulated or customer data.
Proprietary terms are set by the vendor rather than negotiated per project, and no open-source obligation attaches to your code. If you would rather deploy a model whose commercial license is included in your plan — on Roboflow Managed Cloud or a Self-Hosted Inference Server — Roboflow's licensing page lists the supported alternatives to GPT-6 Luna.
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Yes. GPT-6 Luna accepts image input and handles OCR, data extraction, object counting, identification, visual reasoning, and object detection. On Roboflow's Vision Evals its strongest task is Counting at 65.8% (#22 of 57 at low effort).
Yes. its transcriptions match the ground truth 87.9% on average (#36 of 57 at low effort) on Vision Evals OCR. Pulling specific fields out of documents (data extraction) scores 68%.
It's serviceable. On Vision Evals, GPT-6 Luna scores 56.8% mAP@50 on object detection (#24 of 57 at low effort) and 65.8% judge-graded accuracy on object counting.
On our benchmark's task mix, GPT-6 Luna averages $0.0004 per sample at $0.10 per 1M input and $0.50 per 1M output tokens (#7 of 57 on cost), with an average speed of 11.3s per sample across the benchmark. Actual cost depends on your images and prompts.
On the overall Vision Evals ranking, GPT-6 Luna sits #32 of 57 at 68.6%, just behind Claude Opus 4.8 (68.7%) and just ahead of Qwen3.7 Plus (67.4%). See the full side-by-side: GPT-6 Luna vs Claude Opus 4.8.