Claude Opus 4 vs GPT-6 Luna
GPT-6 Luna: 69.7% average across five Vision Evals tasks (#22 of 65 models). Claude Opus 4 is deprecated and was not run through Roboflow's Vision Evals, so there is no head-to-head score.
Compare Claude Opus 4 vs GPT-6 Luna live
Run the same image across every model that supports a task and compare their outputs side-by-side.
Detect and compare bounding boxes across models on the same image.
Upload an image
Drag and drop an image here, or click to browse
Claude Opus 4 is deprecated and can no longer be run. Details and evals are still available on its model page.
Claude Opus 4 vs GPT-6 Luna Comparison Table
Evals updated October 26, 2026Pricing updated November 7, 2026
| Property | Claude Opus 4 | GPT-6 Luna |
|---|---|---|
| Organization | Anthropic | OpenAI |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | May 2025 | Sep 2026 |
| Context Window | 200K | 1.1M |
| Parameters | Unknown | Unknown |
| License | Proprietary | Proprietary |
| Commercial use | commercial use per vendor terms | commercial use per vendor terms |
| Pricing per 1M tokens | ||
| Input $/1M | No published price | $0.100 |
| Output $/1M | No published price | $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 | Deprecated | 69.7% |
| Avg cost / sample | – | $0.0004 |
| Avg speed / sample | – | 10.21s |
| By task | ||
| Object Detection (low) | – | 65.5% ±0.4, Mean of 3 runs, range 65.2 to 66.0 |
| 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 |
| 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 |
| Identification (high) | – | 87.5% ±3.1, Mean of 3 runs, range 84.4 to 90.6 |
| OCR (low) | – | 59.8% |
| by category |
| |
| OCR (high) | – | 62.5% |
| by category |
| |
| Reasoning (low) | – | 64.2% ±3.0, Mean of 3 runs, range 60.3 to 66.2 |
| Reasoning (high) | – | 71.1% ±2.3, Mean of 3 runs, range 68.2 to 72.8 |
Claude Opus 4 vs GPT-6 Luna: Overview
Claude 4 Opus, released by Anthropic in May 2025, is the flagship model of the Claude 4 family, built for complex, long-horizon reasoning and advanced coding workflows. It is multimodal, supporting text (including voice), images, and tool use, and operates as a hybrid reasoning model—able to deliver quick answers in fast mode or switch to extended thinking for deeper, multi-step problem solving. With a ~200,000-token context window and a training cutoff around March 2025, it is optimized for handling large documents, long conversations, and sophisticated agentic tasks.
It is best suited for research, enterprise automation, and software development at scale. The model is classified at Anthropic’s ASL-3 safety level, denoting advanced oversight and safety features.
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.