Claude Sonnet 5.5 vs GPT-5.6 Luna
Compare Claude Sonnet 5.5 and GPT-5.6 Luna side-by-side.
Compare Claude Sonnet 5.5 vs GPT-5.6 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.
Models in this comparison
Claude Sonnet 5.5 vs GPT-5.6 Luna on Vision Evals
Claude Sonnet 5.5 scores higher on 5 of the six Vision Evals tasks.
The widest gap is Reasoning, where Claude Sonnet 5.5 leads 76.4% to 60.5%.
Overall, Claude Sonnet 5.5 averages 83.8% (#7 of 60) against 73.8% (#22 of 60) for GPT-5.6 Luna.
GPT-5.6 Luna is both cheaper ($0.0010 vs $0.0065 per sample) and faster (7.4s vs 10.8s per sample).
Claude Sonnet 5.5 vs GPT-5.6 Luna Comparison Table
Evals updated September 28, 2026Pricing updated September 28, 2026
| Property | Claude Sonnet 5.5 | GPT-5.6 Luna |
|---|---|---|
| Organization | Anthropic | OpenAI |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Sep 2026 | Jul 2026 |
| Context Window | 1.0M | 1.5M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $0.200 | |
| Output $/1M | $1.20 | |
| 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 | 83.8% | 73.8% |
| Avg cost / sample | $0.0065 | $0.0010 |
| Avg speed / sample | 10.78s | 7.38s |
| By task | ||
| Object Detection (low) | 74.3% ±0.9, Mean of 3 runs, range 73.5 to 75.3 | 61.0% ±1.2, Mean of 3 runs, range 59.9 to 62.2 |
| Object Detection (high) | 76.8% ±0.4, Mean of 3 runs, range 76.5 to 77.3 | 62.3% ±1.2, Mean of 3 runs, range 61.4 to 63.8 |
| Counting (low) | 79.3% ±0.7, Mean of 3 runs, range 78.4 to 79.7 | 67.1% ±1.4, Mean of 3 runs, range 66.2 to 68.9 |
| Counting (high) | 82.9% ±1.4, Mean of 3 runs, range 81.1 to 83.8 | 70.7% ±3.4, Mean of 3 runs, range 66.2 to 73.0 |
| Identification (low) | 91.7% ±3.1, Mean of 3 runs, range 87.5 to 93.8 | 83.3% ±1.6, Mean of 3 runs, range 81.3 to 84.4 |
| Identification (high) | 90.6% ±0.0, Mean of 3 runs, range 90.6 to 90.6 | 84.4% ±6.3, Mean of 3 runs, range 78.1 to 90.6 |
| OCR (low) | 90.6% ±0.9, Mean of 3 runs, range 90.0 to 91.7 | 90.7% ±1.8, Mean of 3 runs, range 88.4 to 92.0 |
| OCR (high) | 90.9% ±1.5, Mean of 3 runs, range 89.2 to 92.3 | 91.5% ±0.3, Mean of 3 runs, range 91.2 to 91.7 |
| Data Extraction (low) | 90.7% ±1.5, Mean of 3 runs, range 89.7 to 92.8 | 80.4% ±2.1, Mean of 3 runs, range 78.3 to 82.5 |
| Data Extraction (high) | 93.1% ±0.5, Mean of 3 runs, range 92.8 to 93.8 | 81.8% ±0.5, Mean of 3 runs, range 81.4 to 82.5 |
| Reasoning (low) | 76.4% ±0.7, Mean of 3 runs, range 75.5 to 76.8 | 60.5% ±5.0, Mean of 3 runs, range 55.0 to 64.9 |
| Reasoning (high) | 83.9% ±1.7, Mean of 3 runs, range 82.1 to 85.4 | 65.6% ±3.6, Mean of 3 runs, range 60.9 to 68.2 |
Claude Sonnet 5.5 vs GPT-5.6 Luna: Overview
Claude Sonnet 5.5 is a proprietary multimodal language model from Anthropic and the second release in the Claude 5.5 family, following Claude Opus 5.5. It accepts interleaved text and image input and returns text, operating with a 1M token context window and a maximum output of 128K tokens per request. The model uses adaptive thinking by default, allocating variable reasoning effort per request rather than exposing a manual extended thinking toggle, and its training data cutoff is June 2026. Anthropic positions it as a faster, lower cost complement to Opus 5.5 for well scoped everyday tasks, bug fixing, and producing documents, slides, and spreadsheets.
On visual and agentic evaluations reported at launch, Sonnet 5.5 scores 61.6% on Chartography, a chart recognition test, compared with 15.6% for Claude Sonnet 5, and 80.1% on OSWorld 2.1, a computer use benchmark measuring screenshot driven control of a desktop environment, compared with 57.0% for Sonnet 5. It reports 70.6% on Terminal-Bench 4.0 for agentic coding. Anthropic describes it as the first Sonnet model able to complete Pokemon Red from screenshots alone, and it generates output more than 30% faster than Sonnet 5 while using fewer tokens for equivalent work.
GPT-5.6 Luna is the fastest and most cost-efficient model in OpenAI's GPT-5.6 family, which also includes Sol (the flagship tier) and Terra (the balanced mid-tier). Introduced under a new naming convention where the generation number (5.6) and a durable capability tier name (Luna, Terra, Sol) together define each model, Luna occupies the lightweight end of the family and is designed for high-volume, latency-sensitive workloads such as summarization, drafting, autocomplete, classification, and routine automation. The GPT-5.6 family as a whole advances capabilities in software engineering, computer use, professional knowledge work, scientific research, and cybersecurity, with all three tiers rated at the "High" capability level under OpenAI's Preparedness Framework for both cybersecurity and biological/chemical risk domains.
GPT-5.6 Luna supports multimodal input and function calling, and shares the family's 1.5 million token context window. On Terminal-Bench 2.1, Luna scores 82.5%, and on the Artificial Analysis Coding Agent Index it outperforms comparable models at roughly one-quarter the estimated cost of higher-tier alternatives. Luna is priced at $1 per million input tokens and $6 per million output tokens, with cached input reads at $0.10 per million tokens under the GPT-5.6 prompt caching scheme, which introduces explicit cache breakpoints and a 30-minute minimum cache life. The model was previewed on June 26, 2026 to a limited group of trusted partners via the OpenAI API and Codex, with general availability rolling out on July 9, 2026 across ChatGPT, Codex, and the API.
Frequently Asked Questions
On Roboflow's Vision Evals, Claude Sonnet 5.5 performed better. It scores higher on 5 of the six vision tasks and averages 83.8% (#7 of 60) against 73.8% (#22 of 60) for GPT-5.6 Luna. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
Yes. On the Vision Evals Reasoning benchmark at low effort, Claude Sonnet 5.5 leads with 76.4% against 60.5%. This is the widest gap between the two models across the benchmark's tasks.
GPT-5.6 Luna is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0010 per sample against $0.0065. Actual costs depend on your image sizes, prompts, and output length.
GPT-5.6 Luna is faster. Across Roboflow's Vision Evals it averaged 7.4s per inference against 10.8s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.