Claude Sonnet 5 vs GPT-5.6 Sol
Compare Claude Sonnet 5 and GPT-5.6 Sol 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 Sol on Vision Evals
GPT-5.6 Sol scores higher on 3 of the six Vision Evals tasks.
The widest gap is Object Detection, where GPT-5.6 Sol leads 68.2% to 36.1%.
Overall, Claude Sonnet 5 averages 66.4% (#18 of 25) against 76.9% (#9 of 25) for GPT-5.6 Sol.
Claude Sonnet 5 is both cheaper ($0.0064 vs $0.025 per sample) and faster (4.8s vs 11.7s per sample).
Claude Sonnet 5 vs GPT-5.6 Sol Comparison Table
Evals updated August 6, 2026Pricing updated August 11, 2026
| Property | Claude Sonnet 5 | GPT-5.6 Sol |
|---|---|---|
| Organization | Anthropic | OpenAI |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Jun 2026 | Jul 2026 |
| Context Window | 1.0M | 1.5M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | $5.00 |
| Output $/1M | $10.00 | $30.00 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | ||
| Classification | Demo | Demo |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 66.4% | 76.9% |
| Avg cost / sample | $0.0064 | $0.025 |
| Avg speed / sample | 4.84s | 11.72s |
| By task | ||
| Object Detection | 36.1% $0.011 | 68.2% $0.045 |
| Counting | 56.8% $0.0030 | 73.0% $0.013 |
| Identification | 81.3% $0.0027 | 81.3% $0.0070 |
| OCR | 91.7% $0.0078 | 90.7% $0.032 |
| Data Extraction | 89.7% $0.0030 | 82.5% $0.0085 |
| Reasoning (low) | 43.0% $0.0032 | 65.6% $0.011 |
| Reasoning (high) | 43.0% $0.0043 | 72.2% $0.016 |
Claude Sonnet 5 vs GPT-5.6 Sol: 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 Sol is the flagship model in OpenAI's GPT-5.6 family, which also includes Terra (a balanced everyday-work tier) and Luna (a fast, cost-efficient tier). Sol is designed for demanding reasoning, long-horizon agentic workflows, software engineering, computer use, scientific research, and cybersecurity tasks. It introduces two new capability modes: a "max" reasoning effort setting that allocates additional compute time for difficult problems, and an "ultra" mode that coordinates multiple subagents in parallel to accelerate complex, multi-step work. The model supports native multimodal input, allowing it to process screenshots, diagrams, charts, documents, and photographs alongside text. A reported context window of approximately 1.5 million tokens enables processing of large codebases, lengthy research documents, and extended agentic sessions.
GPT-5.6 Sol was announced on June 26, 2026, initially in a limited preview for trusted partners, and reached general availability on July 9, 2026. On the Agents' Last Exam benchmark, which evaluates long-running professional workflows across 55 fields, Sol scores 53.6. On Terminal-Bench 2.1, which tests command-line agentic coding workflows, Sol Ultra achieves 91.9%. The model also demonstrates gains in life sciences evaluations, including long-horizon genomics and quantitative biology analyses. OpenAI paired the release with its most extensive safety evaluation to date, combining human red teaming with large-scale automated testing, and classified Sol as High capability in both cybersecurity and biological risk under its Preparedness Framework, though it does not cross the Critical threshold in either category.
Frequently Asked Questions
On Roboflow's Vision Evals, GPT-5.6 Sol performed better. It scores higher on 3 of the six vision tasks and averages 76.9% (#9 of 25) against 66.4% (#18 of 25) 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, GPT-5.6 Sol leads with 68.2% against 36.1%. This is the widest gap between the two models across the benchmark's tasks.
Claude Sonnet 5 is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0064 per sample against $0.025. Claude Sonnet 5 is priced at $2.00 per 1M input tokens and $10.00 per 1M output; GPT-5.6 Sol is priced at $5.00 per 1M input tokens and $30.00 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 4.8s per inference against 11.7s. 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.