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GPT-5.6 Sol is OpenAI's flagship 5.6 model - 88.8% on Terminal-Bench 2.1, 91.5% long-context recall, 92.2% on BrowseComp. Highest ceiling in the family, and the slowest of the three.
ChatGPT 5.6 Sol at a glance
Provider
OpenAI
Access on AskAI.free
Premium model; the 7-day Pro trial unlocks access.
Best first test
Work where a wrong answer costs more than the extra wait
Model position
New model in the OpenAI family.
What makes ChatGPT 5.6 Sol different
Sol is the quality ceiling in the GPT-5.6 trio. Choose it when a long brief, difficult repository or multi-stage research task justifies extra latency. Its advantage is least useful for short rewrites and lookups, where Terra or Luna can return a comparable practical answer faster.
What ChatGPT 5.6 Sol is best for
Work where a wrong answer costs more than the extra wait
Long documents and large codebases - 91.5% MRCR long-context recall
Multi-step agentic and terminal workflows (88.8% on Terminal-Bench 2.1)
Research and browsing-style tasks (92.2% on BrowseComp)
Defensive security review - it finds vulnerabilities well (73.5% on ExploitBench 1)
How to use ChatGPT 5.6 Sol
Step 1
Give Sol the whole problem at once. Its 91.5% long-context recall means it genuinely uses a long brief - drip-feeding context wastes the main thing it does better than Terra and Luna.
Step 2
State the goal and the success criteria explicitly. Sol's documented failure mode is confidently solving a nearby problem well rather than the one you meant, and that is cheap to prevent up front.
Step 3
For whole-repository coding, compare against Claude in the sidebar - Claude Fable 5 measurably leads on SWE-Bench Pro (80% vs 64.6%), and switching mid-conversation costs nothing.
Example prompts
Unlimited ChatGPT 5.6 Sol, free for 7 days
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Sol is the most capable of OpenAI's three GPT-5.6 variants, alongside Terra and Luna. It leads the family on the published benchmarks - 88.8% on Terminal-Bench 2.1 against Terra's 87.4% and Luna's 84.7%.
What is Sol actually bad at?
Two things worth knowing. It can lose focus on long agentic runs and confidently solve the wrong problem, so state the goal explicitly. And on repo-level bug fixing it trails Claude - 64.6% vs Claude Fable 5's 80% on SWE-Bench Pro.
Why does Sol feel slower than the other two?
It reasons for longer by design. Luna targets sub-400ms replies and Terra roughly 500-900ms; Sol's latency profile suits considered work rather than fast back-and-forth. Use Terra or Luna when responsiveness matters more than depth.
Why did it refuse my security question?
GPT-5.6 ships with OpenAI's strictest safety stack yet - its cyber safeguards block around ten times more potentially harmful activity than earlier models. Real-time cyber and biology classifiers occasionally catch legitimate work near those boundaries. Rephrasing around the defensive intent usually helps.
Sol, Terra or Luna?
Sol when being wrong is expensive, Terra for everyday production, Luna for speed and volume. On short tasks the gaps are small - but not on long inputs, where Luna collapses to 41.3% recall against Sol's 91.5%.
Do I need a paid plan?
Yes - Sol is a premium model and needs a Pro plan. The 7-day free trial unlocks it and nothing is charged today.
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