GPT-5.6 Sol vs Terra vs Luna
Three tiers of the same model. The benchmark gaps are small - except in one place, where they are enormous.
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TL;DR
Use Terra by default. It scores 87.4% on Terminal-Bench 2.1 against Sol's 88.8%, holds 89.6% long-context recall against Sol's 91.5%, and answers in roughly 500-900ms. For most work the flagship is not worth the wait.
Use Sol when being wrong is expensive - the hardest reasoning, the longest documents, multi-step agentic runs. Use Luna when speed is the point: sub-400ms replies for classification, routing, autocomplete and quick answers.
The one number that decides it: long-context recall. Sol 91.5%, Terra 89.6%, Luna 41.3%. Luna is not a slightly weaker Sol - on long inputs it quietly loses information. If your input is long, Terra is the cheapest tier worth trusting.
How to read this comparison
This page compares GPT-5.6 Sol and GPT-5.6 Luna by practical task fit, not only by headline specs. Treat the verdicts as a starting point, then test both options with the same prompt if your work depends on tone, accuracy, speed, coding quality, visual reasoning, or long-context handling.
The best choice is the model that gives you the most usable first draft with the fewest corrections for your actual workflow.
Side-by-side specs
| GPT-5.6 Sol | GPT-5.6 Terra | GPT-5.6 Luna | |
|---|---|---|---|
| Vendor | OpenAI | OpenAI | OpenAI |
| Context window | 1.05M tokens | 1.05M tokens | 1.05M tokens (41.3% recall) |
| Image input | ✓ Yes | ✓ Yes | ✓ Yes |
| Voice mode | - No | - No | - No |
| Web search | - No | - No | - No |
| Knowledge cutoff | 2026 | 2026 | 2026 |
| Best for | Hard reasoning, long documents, agentic and terminal workflows | Everyday production work, interactive assistants, document analysis | Quick answers, classification, routing, high-volume work |
| Available on AskAI.free | Pro/Max on AskAI.free | Pro/Max on AskAI.free | Pro/Max on AskAI.free |
Winner by task
| Task | Winner | Why |
|---|---|---|
| Hardest reasoning and planning | GPT-5.6 Sol | Sol leads on Agents' Last Exam (53.6 vs Terra 50.4 and Luna 50.3) and the gap widens on long multi-step runs. |
| Long documents / large codebases | GPT-5.6 Sol | MRCR long-context recall: Sol 91.5%, Terra 89.6%, Luna 41.3%. Sol is the safest on very long inputs. |
| Everyday production work | GPT-5.6 Terra | Terra sits one to three points behind Sol on most benchmarks at a fraction of the cost - the sensible default. |
| Interactive chat assistants | GPT-5.6 Terra | Terra's 500-900ms latency keeps conversation natural while staying coherent turn to turn. |
| Quick questions and short tasks | GPT-5.6 Luna | Luna targets sub-400ms and still scores 84.7% on Terminal-Bench 2.1 against Sol's 88.8%. |
| Classification, routing, tagging | GPT-5.6 Luna | High-volume, low-complexity work is exactly what Luna is priced and tuned for. |
| Agentic / terminal workflows | GPT-5.6 Sol | Terminal-Bench 2.1: Sol 88.8%, Terra 87.4%, Luna 84.7%. Sol Ultra reaches 91.9% with four agents. |
| Repo-level bug fixing | Tie | None of them is the best choice here - Claude Fable 5 scores 80% on SWE-Bench Pro against Sol's 64.6%. |
| Browsing-style research | GPT-5.6 Sol | Sol reaches 92.2% on BrowseComp, the strongest of the family on retrieval-heavy tasks. |
| Cost-sensitive, high volume | GPT-5.6 Luna | Luna is roughly 25x cheaper than Sol per token on OpenAI's API, which compounds fast at volume. |
Real-world use cases
Analysing a long contract or research paper
Sol or Terra, never Luna. This is the clearest split in the family: Sol recalls 91.5% of a long input and Terra 89.6%, but Luna manages only 41.3%. Luna will answer confidently having missed material in the middle of your document, which is worse than refusing.
Powering a customer-facing chatbot
Terra. It holds conversational coherence turn to turn at roughly 500-900ms, which feels responsive without the shallowness users notice. Sol's latency profile suits background jobs rather than live chat.
Classifying or routing thousands of items
Luna. The task is simple and the volume is the problem, so latency and cost dominate. Luna targets sub-400ms and is around 25x cheaper than Sol per token - and none of Sol's extra reasoning helps here.
Debugging across a whole repository
Sol among these three, but compare it against Claude. On SWE-Bench Pro, Claude Fable 5 scores 80% against Sol's 64.6%. Sol is stronger on agentic and terminal-style work; Claude is stronger at repo-level bug fixing. Both are in your sidebar, so run the same prompt through each.
Related tools and guides
Stop reading. Try both side-by-side on AskAI.free - Your first question is free.
Start a free chat →Frequently asked questions
Which GPT-5.6 model should most people use?
Terra. It lands within one to three points of Sol on most published benchmarks while being much cheaper and faster, so the flagship rarely justifies itself for everyday work. Escalate to Sol when a mistake is genuinely expensive.
Is Luna actually much worse than Sol?
On short tasks, barely - 84.7% vs 88.8% on Terminal-Bench 2.1. On long inputs, dramatically: 41.3% vs 91.5% long-context recall. Luna is a speed tool, not a smaller flagship, and the difference only shows up when your input gets long.
What is the difference in speed?
Luna targets sub-400ms responses, Terra roughly 500-900ms, and Sol is slow enough that OpenAI suggests reserving it for asynchronous or background work rather than live conversation.
Do they all have the same context window?
Yes - all three accept about 1.05M tokens with up to 128K output. But accepting a long input and reliably using it are different things, which is what the recall numbers measure.
Why did GPT-5.6 refuse my security question?
GPT-5.6 ships with OpenAI's strictest safety stack yet - its cyber safeguards block roughly ten times more potentially harmful activity than earlier models, and real-time classifiers occasionally catch legitimate work near those boundaries.
Can I try all three free?
All three are premium models on AskAI.free and need a Pro plan. The 7-day free trial unlocks them with nothing charged today, and you can switch between them mid-conversation to compare.