Claude Sonnet 4.6 vs GPT-5.6 Sol
Compare Claude Sonnet 4.6 and GPT-5.6 Sol side-by-side. See how these vision models stack up in Image Captioning, Classification, Open Prompt, Object Detection, and OCR.
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Claude Sonnet 4.6 vs GPT-5.6 Sol: 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-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.
Claude Sonnet 4.6 vs GPT-5.6 Sol Comparison Table
| Property | Claude Sonnet 4.6 | GPT-5.6 Sol |
|---|---|---|
| Organization | Anthropic | OpenAI |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Feb 2026 | Jul 2026 |
| Context Window | 1.0M | 1.5M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $3.00 | $5.00 |
| Output $/1M | $15.00 | $30.00 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Classification | Demo | Demo |
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Chart Question Answering | ||
| Document Question Answering | ||
| Model Features | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
| Foundation Vision | ||
Vision Evalspass/fail results · 67 prompts Score key:≥75%40–74%<40% | ||
| Visual Understanding | ||
| Overall Score | 70.15% | |
| Avg Response Time | 4.24s | |
| Median input tokensincl. image tokens | 2.2K | |
| Median output tokens | 105 | |
| Est. cost / taskon this benchmark | $0.0080 | |
| Defect Detection | 80%(12/15) | |
| Document Understanding | 77.8%(7/9) | |
| Object Counting | 30%(3/10) | |
| Object Understanding | 71.4%(10/14) | |
| Spatial Understanding | 78.9%(15/19) | |
| OCR | ||
| Overall Score | 81.66% | |
| Avg Response Time | 3.42s | |
| Median input tokensincl. image tokens | 736 | |
| Median output tokens | 85 | |
| Est. cost / taskon this benchmark | $0.0035 | |
| Focused Scene OCR | 85.9%(85/99) | |
| Handwritten Math | 50%(5/10) | |
| License Plate Recognition | 90%(27/30) | |
| Text Recognition | 86.7%(26/30) | |
| VQA & Extraction | 73.3%(44/60) | |
Output tokens (incl. reasoning) and est. cost / task are measured on this benchmark from a single low-temperature run, and shown only for models whose run covered at least 90% of prompts. Methodology