Claude Sonnet 4.6 vs GPT-6 Luna
GPT-6 Luna: 69.7% average across five Vision Evals tasks (#22 of 65 models). We have not run Claude Sonnet 4.6 through Roboflow's Vision Evals, so there is no head-to-head score. Run both on your own image below.
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Claude Sonnet 4.6 vs GPT-6 Luna Comparison Table
Evals updated October 26, 2026Pricing updated November 7, 2026
| Property | Claude Sonnet 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 | Unknown | Unknown |
| License | Proprietary | Proprietary |
| Commercial use | commercial use per vendor terms | commercial use per vendor terms |
| Pricing per 1M tokens | ||
| Input $/1M | $3.00 | $0.100 |
| Output $/1M | $15.00 | $0.500 |
| 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 | Not evaluated | 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 Sonnet 4.6 vs GPT-6 Luna: Overview
Claude Sonnet 4.6 is Anthropic's mid-tier large language model, released February 17, 2026, designed to balance performance, cost, and versatility for professional and developer use. It supports text and vision-based tasks with advanced reasoning, agentic capabilities, and Adaptive Thinking — a mode where the model dynamically scales its internal reasoning depth. A beta context window of up to 1,000,000 tokens (200K standard) enables processing of entire codebases or document collections in a single request. Parameters are undisclosed.
Optimized for coding, computer use, long-context reasoning, agent planning, and knowledge work, Sonnet 4.6 delivers a full generational upgrade over Sonnet 4.5 and approaches Opus 4.5-level performance across many benchmarks at a fraction of the cost. It is the default model on Claude.ai, Claude Cowork, and is available via API and major cloud platforms — making it well suited for production workloads requiring strong reasoning without flagship pricing.
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.