Claude Sonnet 5 vs GPT-5.6 Luna
Compare Claude Sonnet 5 and GPT-5.6 Luna side-by-side. See how these vision models stack up in Object Detection, Open Prompt, OCR, Classification, and Image Captioning.
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Models in this comparison
Claude Sonnet 5 vs GPT-5.6 Luna on Vision Evals
GPT-5.6 Luna scores higher on 4 of the five Vision Evals tasks.
The widest gap is Object Detection, where GPT-5.6 Luna leads 61.0% to 36.1%.
Overall, Claude Sonnet 5 averages 55.3% (#45 of 61) against 64.7% (#27 of 61) for GPT-5.6 Luna.
GPT-5.6 Luna is cheaper ($0.0011 vs $0.0074 per sample), while Claude Sonnet 5 is faster (5.9s vs 8.6s per sample).
Claude Sonnet 5 vs GPT-5.6 Luna Comparison Table
Evals updated October 8, 2026Pricing updated October 8, 2026
| Property | Claude Sonnet 5 | GPT-5.6 Luna |
|---|---|---|
| Organization | Anthropic | OpenAI |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Jun 2026 | Jul 2026 |
| Context Window | 1.0M | 1.5M |
| Parameters | Unknown | Unknown |
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | $0.200 |
| Output $/1M | $10.00 | $1.20 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | Supported | Supported |
| Classification | Demo | Demo |
| Document Question Answering | Supported | Supported |
| Image Tagging | Supported | Supported |
| Multi-Label Classification | Supported | Supported |
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | Supported | Supported |
| Visual Question Answering | Demo | Demo |
| Model Features | ||
| Foundation Vision | Supported | Supported |
| LLMs with Vision Capabilities | Supported | Supported |
| Multimodal Vision | Supported | Supported |
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 55.3% | 64.7% |
| Avg cost / sample | $0.0074 | $0.0011 |
| Avg speed / sample | 5.86s | 8.60s |
| By task | ||
| Object Detection (low) | 36.1% | 61.0% ±1.2, Mean of 3 runs, range 59.9 to 62.2 |
| Object Detection (high) | – | 62.3% ±1.2, Mean of 3 runs, range 61.4 to 63.8 |
| Counting (low) | 56.8% | 67.1% ±1.4, Mean of 3 runs, range 66.2 to 68.9 |
| Counting (high) | – | 70.7% ±3.4, Mean of 3 runs, range 66.2 to 73.0 |
| Identification (low) | 81.3% | 83.3% ±1.6, Mean of 3 runs, range 81.3 to 84.4 |
| Identification (high) | – | 84.4% ±6.3, Mean of 3 runs, range 78.1 to 90.6 |
| OCR (low) | 59.4% | 51.8% |
| by category |
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| OCR (high) | 59.5% | 55.7% |
| by category |
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| Reasoning (low) | 43.0% | 60.5% ±5.0, Mean of 3 runs, range 55.0 to 64.9 |
| Reasoning (high) | 43.0% | 65.6% ±3.6, Mean of 3 runs, range 60.9 to 68.2 |
Claude Sonnet 5 vs GPT-5.6 Luna: Overview
Claude Sonnet 5 is a mid-tier large language model from Anthropic, released on June 30, 2026, as the latest model in the Sonnet series and a direct successor to Claude Sonnet 4.6. It is a hybrid reasoning model designed primarily for agentic workflows, software coding, and professional tasks. The model features a 1 million token context window, a 128k maximum output token limit, and runs adaptive thinking by default, giving API users fine-grained control over reasoning effort across five levels (low, medium, high, max, and extra-high). It uses an updated tokenizer shared with Opus 4.7 and later models, which produces approximately 30% more tokens for equivalent text compared to earlier Claude models. On benchmarks, Sonnet 5 scores 63.2% on agentic coding and 81.2% on OSWorld, narrowing the gap with Opus 4.8 while remaining at Sonnet-tier pricing.
The model supports text and image input with text output, and accepts tools including browsers and terminals for autonomous multi-step task execution. Anthropic's safety evaluations report that Sonnet 5 shows a lower rate of undesirable behaviors than Sonnet 4.6 and is generally safer in agentic contexts, with improved resistance to prompt injection and reduced sycophancy. Cybersecurity safeguards equivalent to those on Opus 4.7 and 4.8 are active, though Anthropic notes the model was not deliberately trained on cybersecurity tasks. The model is proprietary and API-only, with no open weights.
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 supports 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, GPT-5.6 Luna performed better. It scores higher on 4 of the five vision tasks and averages 64.7% (#27 of 61) against 55.3% (#45 of 61) for Claude Sonnet 5. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
No. On the Vision Evals Object Detection benchmark at low effort, GPT-5.6 Luna leads with 61.0% against 36.1%. 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 $0.0011 per sample against $0.0074. Claude Sonnet 5 is priced at $2.00 per 1M input tokens and $10.00 per 1M output; GPT-5.6 Luna is priced at $0.20 per 1M input tokens and $1.20 per 1M output. Actual costs depend on your image sizes, prompts, and output length.
Claude Sonnet 5 is faster. Across Roboflow's Vision Evals it averaged 5.9s per inference against 8.6s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.
Yes. The comparison demo on this page runs both models on the same image side by side for object detection and open prompts in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.