OpenAI's flagship GPT-5.6 model served in pro reasoning mode for the hardest coding, agentic and long-horizon reasoning tasks.
Output Speed *
Intelligence Index *
Context Window *
Input price
Output price
Performance
Why Sol Pro Leads on Agentic and Terminal-Based Coding
Benchmarks
GPT-5.6 Sol Pro Benchmarks: Coding Agents vs SWE-Bench Pro
Output Speed
Intelligence Index
MMLU *
GPQA *
HLE *
LiveCodeBench *
Technical Specifications
What the model supports
Comparison
GPT-5.6 Sol Pro vs GPT-5.6 Terra: When Is Pro Mode Worth It?
Both models come from the same GPT-5.6 family and share the API, tool calling, structured outputs and document input. Sol Pro is the flagship Sol model served in pro reasoning mode for the hardest tasks; Terra is the balanced, lower-cost tier positioned as GPT-5.5-class quality at a lower price. On benchmarks the gap is often small—Terra reaches 87.4% on Terminal-Bench 2.1 versus Sol's 88.8%, and 63.4% versus 64.6% on SWE-Bench Pro—so the practical decision is whether a task genuinely needs Sol Pro's deeper reasoning or whether Terra's economics win at volume.
Choose GPT-5.6 Sol Pro when correctness outweighs cost: complex debugging, long-horizon agents, GUI/computer-use tasks and problems that reward Pro mode's extra verification—areas where Sol's lead is most pronounced. Choose GPT-5.6 Terra for sustained production workloads at volume, where its close benchmark results and lower cost make it the default. Because independent notes place Luna and Sol on the intelligence-vs-cost Pareto frontier ahead of Terra at some effort levels, benchmark your own representative tasks across tiers before standardizing.
Limitations & Trade-offs
Best-Fit Workloads
Where this model earns its place
Terminal-based and agentic coding
Sol Pro's strongest evidence is command-line and multi-step coding. GPT-5.6 Sol leads the independent Coding Agent Index and scores 88.8% on Terminal-Bench 2.1 (91.9% in Ultra multi-agent mode). For CLI agents, large refactors and repository-level engineering, Pro mode's added verification improves reliability—though SWE-Bench Pro is the exception where Claude leads, so test GitHub-issue workflows separately.
Long-horizon autonomous agents
With a leading 53.6 on Agents' Last Exam across long-running professional workflows, plus Responses API features like Programmatic Tool Calling, persisted reasoning and multi-agent (beta), Sol Pro suits agents that run for extended periods, coordinate tools and re-read large context. The ~1.05M-token window supports big working sets; higher pricing above 272K tokens is the main cost consideration.
Computer use and complex web research
Independent notes highlight Sol's advantage on OSWorld and BrowseComp, and OpenAI reports state-of-the-art BrowseComp results. Agents that interact with real GUI environments or conduct reliable multi-step web research benefit from Sol Pro's edge in these areas. This makes it a fit for browsing agents and computer-use automation where accuracy on complex, multi-step navigation matters.
High-difficulty analytical and scientific reasoning
Pro mode extends internal exploration, revision and verification, and GPT-5.6 Sol lands within about a point of the top intelligence score at roughly half the estimated cost and far less time. For hard scientific reasoning, deep analysis and one-off difficult problems, Sol Pro with high or max effort is appropriate—reserve it for tasks where the extra reasoning earns its higher latency on your own evals.