GPT-6.1 Terra vs GPT-6 Luna
GPT-6.1 Terra and GPT-6 Luna score 85.8% vs 69.7% averaged across five Vision Evals tasks (#2 and #22 of 65 models), so GPT-6.1 Terra leads overall.
- GPT-6.1 Terra wins all 5 tasks, with its widest lead on OCR (81.9% vs 59.8%).
- GPT-6 Luna is about 1/8 of the cost: $0.0004 vs $0.0033 overall average cost per sample.
Roboflow Vision Evals. Full breakdown below.
Compare GPT-6.1 Terra vs GPT-6 Luna live
Run the same image across every model that supports a task and compare their outputs side-by-side.
These models don't share enough common tasks for a side-by-side demo. See the comparison table below for their capabilities.
GPT-6.1 Terra vs GPT-6 Luna on Vision Evals
Roboflow Vision Evals. Full breakdown below.
GPT-6.1 Terra vs GPT-6 Luna Comparison Table
Evals updated October 26, 2026Pricing updated October 27, 2026
| Property | GPT-6.1 Terra | GPT-6 Luna |
|---|---|---|
| Organization | OpenAI | OpenAI |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Oct 2026 | Sep 2026 |
| Context Window | 1.1M | 1.1M |
| Parameters | undisclosed | Unknown |
| License | Proprietary | Proprietary |
| Commercial use | commercial use per vendor terms | commercial use per vendor terms |
| Pricing per 1M tokens | ||
| Input $/1M | $1.00 | $0.100 |
| Output $/1M | $5.00 | $0.500 |
| Vision Tasks | ||
| Captioning | Supported | Demo |
| Classification | Supported | Demo |
| Object Detection | Supported | Demo |
| OCR | Supported | Demo |
| Visual Question Answering | Supported | Demo |
| 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 | 85.8% | 69.7% |
| Avg cost / sample | $0.0033 | $0.0004 |
| Avg speed / sample | 10.05s | 10.21s |
| By task | ||
| Object Detection (low) | 84.7% ±0.1, Mean of 3 runs, range 84.7 to 84.8 | 65.5% ±0.4, Mean of 3 runs, range 65.2 to 66.0 |
| Object Detection (high) | 84.7% ±0.4, Mean of 3 runs, range 84.2 to 84.9 | 68.0% ±0.7, Mean of 3 runs, range 67.3 to 68.6 |
| Counting (low) | 80.2% ±2.0, Mean of 3 runs, range 78.4 to 82.4 | 71.6% ±0.0, Mean of 3 runs, range 71.6 to 71.6 |
| Counting (high) | 84.2% ±3.4, Mean of 3 runs, range 81.1 to 87.8 | 72.1% ±0.7, Mean of 3 runs, range 71.6 to 73.0 |
| Identification (low) | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 | 87.5% ±3.1, Mean of 3 runs, range 84.4 to 90.6 |
| Identification (high) | 96.9% ±0.0, Mean of 3 runs, range 96.9 to 96.9 | 87.5% ±3.1, Mean of 3 runs, range 84.4 to 90.6 |
| OCR (low) | 81.9% | 59.8% |
| by category |
|
|
| OCR (high) | 84.4% | 62.5% |
| by category |
|
|
| Reasoning (low) | 85.4% ±1.7, Mean of 3 runs, range 83.4 to 86.8 | 64.2% ±3.0, Mean of 3 runs, range 60.3 to 66.2 |
| Reasoning (high) | 87.0% ±1.3, Mean of 3 runs, range 85.4 to 88.1 | 71.1% ±2.3, Mean of 3 runs, range 68.2 to 72.8 |
GPT-6.1 Terra vs GPT-6 Luna: Overview
GPT-6.1 Terra is an OpenAI model released on October 26, 2026 for visual reasoning, computer use and writing. It accepts text and image inputs and produces text, with a 1,050,000-token context window and up to 128,000 output tokens. OpenAI lists applications including matching product photographs to catalog entries, interpreting technical drawings and operating application screens.
The API exposes six reasoning effort settings: none, low, medium, high, xhigh and max. Medium is the default. The model supports structured outputs and tool calling through the Responses API. Vision Evals reports its Low and High results for text understanding, counting, identification, reasoning and object detection, using native OpenAI API inference. Terra is a proprietary model; its weights are not released.
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-6.1 Terra performed better. It scores higher on all five vision tasks and averages 85.8% (#2 of 65) against 69.7% (#22 of 65) for GPT-6 Luna. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
Yes. On the Vision Evals OCR benchmark at low effort, GPT-6.1 Terra leads with 81.9% against 59.8%. 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 $0.0033. GPT-6.1 Terra is priced at $1.00 per 1M input tokens and $5.00 per 1M output; GPT-6 Luna is priced at $0.10 per 1M input tokens and $0.50 per 1M output. Actual costs depend on your image sizes, prompts, and output length.
GPT-6.1 Terra is faster. Across Roboflow's Vision Evals it averaged 10.1s per inference against 10.2s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.