Claude Opus 4.6 vs GPT-6 Luna
Compare Claude Opus 4.6 and GPT-6 Luna side-by-side.
Compare Claude Opus 4.6 vs GPT-6 Luna live
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Models in this comparison
Claude Opus 4.6 vs GPT-6 Luna Comparison Table
Evals updated September 22, 2026Pricing updated September 22, 2026
| Property | Claude Opus 4.6 | GPT-6 Luna |
|---|---|---|
| Organization | Anthropic | OpenAI |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Feb 2026 | Sep 2026 |
| Context Window | 1.0M | 1.1M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $5.00 | $0.100 |
| Output $/1M | $25.00 | $0.500 |
| Vision Tasks | ||
| Captioning | Demo | |
| Chart Question Answering | ||
| Classification | Demo | |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Object Detection | Demo | |
| OCR | Demo | |
| Vision Language | ||
| Visual Question Answering | Demo | |
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | Not evaluated | 68.6% |
| Avg cost / sample | – | $0.0004 |
| Avg speed / sample | – | 11.27s |
| By task | ||
| Object Detection (low) | – | 56.8% ±1.9, Mean of 3 runs, range 54.8 to 58.5 |
| Object Detection (high) | – | 64.1% ±0.5, Mean of 3 runs, range 63.6 to 64.5 |
| Counting (low) | – | 65.8% ±1.4, Mean of 3 runs, range 64.9 to 67.6 |
| Counting (high) | – | 64.4% ±2.0, Mean of 3 runs, range 62.2 to 66.2 |
| Identification (low) | – | 81.3% ±0.0, Mean of 3 runs, range 81.3 to 81.3 |
| Identification (high) | – | 80.2% ±1.6, Mean of 3 runs, range 78.1 to 81.3 |
| OCR (low) | – | 87.9% ±0.6, Mean of 3 runs, range 87.2 to 88.3 |
| OCR (high) | – | 88.5% ±0.6, Mean of 3 runs, range 87.9 to 89.2 |
| Data Extraction (low) | – | 68.0% ±3.1, Mean of 3 runs, range 65.0 to 71.1 |
| Data Extraction (high) | – | 66.7% ±0.5, Mean of 3 runs, range 66.0 to 67.0 |
| Reasoning (low) | – | 52.1% ±2.0, Mean of 3 runs, range 49.7 to 53.6 |
| Reasoning (high) | – | 60.7% ±1.7, Mean of 3 runs, range 58.9 to 62.3 |
Claude Opus 4.6 vs GPT-6 Luna: Overview
Claude Opus 4.6 is the flagship large language model from Anthropic, released on 2026-02-05 for advanced reasoning, complex coding, and enterprise agent workflows. It supports text and image inputs via API, offers a 200K-token standard context window with a 1M-token beta option, and enables outputs up to 128K tokens, with adaptive reasoning and context compaction for sustained tasks.
As of 2026-02-17, Anthropic also released Claude Sonnet 4.6, extending the 1M-token context window to a broader tier. Opus remains positioned for maximum depth and benchmark performance, while Sonnet 4.6 brings long-context capability to more cost- and latency-sensitive production use cases.
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.