Developers praise OpenAI's GPT-5.6 Sol for reasoning and accuracy, though some flag overengineering tendency

OpenAI rolled out its GPT-5.6 family of models globally in early July, offering developers three tiers: Sol (most capable), Terra (mainstream use), and Luna (speed-optimized). The flagship Sol model has already drawn attention from researchers and engineers testing its real-world problem-solving abilities.

Strong performance on complex tasks#

A Dallas-based AI research leader at BlogBuster reports that Sol outperforms Anthropic's Claude Opus 5 when asked to generate and validate a three-million-entry database. After Sol created the database and Claude reviewed it, the latter found only trivial issues—whereas Sol had earlier caught significant mistakes in Claude's output.

"Sol is doing a better job creating things for me and finding mistakes made by Anthropic's models; it's blowing my mind," the developer noted.

However, researchers caution that this type of comparison—using one model to generate and another to audit—measures something narrower than overall accuracy. Joshua Estrin, an AI governance researcher, emphasizes that without checking both outputs against ground-truth data, it remains unclear whether Sol truly errs less frequently or simply behaves differently when assigned specific tasks. A definitive verdict would require validating the outputs independently.

Tackling unsolved mathematics#

Columbia Business School PhD candidate Shouqiao Wang claimed to solve six open Erdős problems—long-standing conjectures in discrete mathematics—over five days using Sol's ultra-reasoning mode. This setting orchestrates multiple agents in parallel to tackle different subproblems simultaneously, trading higher token consumption for faster results on demanding work.

Wang emphasized that the Codex workflow required patience and ample processing time, but Sol's ability to sustain long mathematical searches and maintain research context across sessions proved decisive for the task.

Practical engineering tradeoffs#

A Scottish tourism booking-system developer reports using Sol as their "go-to model" for architectural decisions and high-level problem scoping—tasks previously considered out of reach for language models. The reasoning quality felt comparable to consulting a senior software engineer, though occasional nonsensical outputs still occur.

The same developer flagged one drawback: "Perhaps the main issue I've found regarding Sol 5.6 is that it has a tendency to overengineer things a little. And for some frontend tasks, I'll still occasionally jump to Claude or Gemini to solve smaller issues."