Claude Opus 5.5 vs GPT-5.6 Sol

The short answer: choose Claude Opus 5.5 first for demanding coding, professional knowledge work and agentic research. It beats GPT-5.6 Sol across the six directly comparable rows published with Anthropic's launch. Choose GPT-5.6 Sol when a 1.05-million-token context window, 128,000-token maximum output, or an existing OpenAI Responses API workflow matters more than the benchmark gap.

A naming correction: OpenAI's current model catalog lists GPT-5.6 Sol, not “GPT-6 Sol.” The supplied GPT-6 Sol launch URL does not resolve, so this comparison uses the verified model name and current official specifications.

The benchmark results below come from Anthropic, while pricing and product specifications are checked against each vendor's current documentation. Vendor tests are useful evidence, not a substitute for an evaluation on your own prompts, tools and acceptance criteria.

Pricing: a tie on standard tokens, an Opus advantage on cache reads

Both models list standard API rates of $4 per million input tokens and $20 per million output tokens. For a request totaling one million uncached input tokens and 200,000 output tokens, either model has a list-price cost of $8 before tool charges, discounts and retries. Opus 5.5's $0.20 cache-read rate is half GPT-5.6 Sol's $0.40 cached-input rate, which can matter for agents that repeatedly reuse a large prompt prefix.

Equal token prices do not guarantee equal task cost. Measure retries, output length, tool calls, latency and human review. The cheaper production system is the one that reaches your acceptance threshold with less total spend.

Coding: Opus 5.5 has the stronger published case

Opus 5.5 leads GPT-5.6 Sol by 29.1 points on Terminal-Bench 4.0, 6.9 points on FrontierCode v1.1 and 16.1 points on CursorBench 4.0 in Anthropic's table. That makes Opus the more defensible starting point for repository changes, terminal work and long-running code agents.

Do not compare the model alone. Claude Code and OpenAI's agent tooling use different harnesses, permissions and context strategies. Give both systems the same repository snapshot, tests, time budget and review rubric before choosing.

Knowledge work, research and automation

Opus 5.5 also leads on GDPval-AA, AutomationBench and Terminal-Bench-Science. Anthropic positions it for financial analysis, report generation, document work and multi-step business tasks. Those results favor Opus when accuracy on professional deliverables is the main constraint.

GPT-5.6 Sol remains a capable professional-work model with web search, file search, computer use, functions, structured outputs and image input. If your application already depends on OpenAI's Responses API, migration cost may outweigh a benchmark improvement until your own evaluation proves otherwise.

Context and output length

OpenAI documents 1.05 million tokens of context and a 128,000-token maximum output for GPT-5.6 Sol. That is a practical advantage when a workflow must ingest very large evidence sets or produce long structured artifacts. Still, a larger window does not guarantee better recall: curate the input, preserve source locations and test retrieval accuracy near the beginning, middle and end.

Tools and ecosystem fit

GPT-5.6 Sol supports function calling, web search, file search and computer use in OpenAI's current model catalog. Opus 5.5 is designed for Claude Code and tool-using workflows in Anthropic's ecosystem. Pick the model whose surrounding platform reduces integration work while still passing your quality tests.

Verdict

Claude Opus 5.5 is the better default for raw task performance at this price point. The two models charge the same standard token rates, but Opus leads all six direct benchmark rows shown above and offers cheaper cache reads. GPT-5.6 Sol is still the practical choice for some very-long-context and OpenAI-native applications. Run a representative evaluation before switching a production system.