Claude Opus 4.8 vs GPT-5.6 Sol
Compare Claude Opus 4.8 and GPT-5.6 Sol side-by-side. See how these vision models stack up in Image Captioning, Classification, OCR, Object Detection, and Open Prompt.
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Models in this comparison
Claude Opus 4.8 vs GPT-5.6 Sol on Vision Evals
GPT-5.6 Sol scores higher on 4 of the six Vision Evals tasks.
The widest gap is Object Detection, where GPT-5.6 Sol leads 68.2% to 38.6%.
Overall, Claude Opus 4.8 averages 66.8% (#19 of 31) against 76.9% (#10 of 31) for GPT-5.6 Sol.
GPT-5.6 Sol is cheaper ($0.0089 vs $0.016 per sample), while Claude Opus 4.8 is faster (5.2s vs 11.7s per sample).
Claude Opus 4.8 vs GPT-5.6 Sol Comparison Table
Evals updated August 20, 2026Pricing updated August 23, 2026
| Property | Claude Opus 4.8 | GPT-5.6 Sol |
|---|---|---|
| Organization | Anthropic | OpenAI |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | May 2026 | Jul 2026 |
| Context Window | 1.0M | 1.5M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $5.00 | $2.00 |
| Output $/1M | $25.00 | $10.00 |
| 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 | 66.8% | 76.9% |
| Avg cost / sample | $0.016 | $0.0089 |
| Avg speed / sample | 5.20s | 11.72s |
| By task | ||
| Object Detection | 38.6% $0.026 | 68.2% $0.016 |
| Counting | 52.7% $0.0076 | 73.0% $0.0048 |
| Identification | 75.0% $0.0067 | 81.3% $0.0027 |
| OCR | 93.8% $0.020 | 90.7% $0.011 |
| Data Extraction | 87.6% $0.0076 | 82.5% $0.0033 |
| Reasoning (low) | 53.0% $0.0078 | 65.6% $0.0042 |
| Reasoning (high) | 52.3% $0.0078 | 72.2% $0.0057 |
Claude Opus 4.8 vs GPT-5.6 Sol: Overview
Claude Opus 4.8 is Anthropic's most capable generally available large language model, released on May 28, 2026 as an incremental upgrade to Claude Opus 4.7. The model accepts text and image inputs and produces text outputs, with a 1 million token context window on the Claude API, Amazon Bedrock, and Google Cloud Vertex AI (200k tokens on Microsoft Foundry) and up to 128k max output tokens. It uses adaptive thinking and supports adjustable effort tiers — high by default, with extra and max tiers available for more demanding tasks. A fast mode operates at approximately 2.5x standard speed. The model is described by Anthropic as a hybrid reasoning model designed for advanced coding, agentic workflows, long-context reasoning, and professional knowledge work.
Key behavioral improvements over Opus 4.7 include substantially reduced rates of unreported code flaws, improved honesty in self-assessment, and better tool-calling reliability. On Anthropic's Super-Agent benchmark, Opus 4.8 completes every case end-to-end, and it scores 84% on Online-Mind2Web for computer-use and browser-agent tasks. It achieves 88.6% on SWE-bench Verified and 69.2% on SWE-bench Pro. Alongside the model, Anthropic launched Dynamic Workflows in Claude Code (research preview), which enables Claude to orchestrate hundreds of parallel subagents for codebase-scale tasks such as large migrations. The Messages API was also updated to accept mid-task system messages without breaking prompt caching, improving support for long-running agentic pipelines.
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 4 of the six vision tasks and averages 76.9% (#10 of 31) against 66.8% (#19 of 31) for Claude Opus 4.8. 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 38.6%. This is the widest gap between the two models across the benchmark's tasks.
GPT-5.6 Sol is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0089 per sample against $0.016. Claude Opus 4.8 is priced at $5.00 per 1M input tokens and $25.00 per 1M output; GPT-5.6 Sol is priced at $2.00 per 1M input tokens and $10.00 per 1M output. Actual costs depend on your image sizes, prompts, and output length.
Claude Opus 4.8 is faster. Across Roboflow's Vision Evals it averaged 5.2s 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 image captioning and image classification in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.