GPT-5.6 model comparison: 4 ways to fit creator workflows and budgets
Pick the right tier—Sol, Terra, or Luna—and use Max or Ultra reasoning modes to stay under budget while getting the performance you need.
OpenAI’s July 9 launch of the GPT‑5.6 family gave creators three distinct options. In this GPT-5.6 model comparison you’ll see which tier matches common workflow patterns and how the new reasoning modes change the cost equation.
Sol: the heavyweight for complex coding and long‑running agents – GPT-5.6 model comparison
In a GPT-5.6 model comparison the Sol model is the flagship tier. It scores 80 points on the Artificial Analysis Coding Agent Index, beating Anthropic’s Claude Fable 5 on software‑engineering benchmarks (Forbes). On the Agents’ Last Exam benchmark Sol reaches 53.6, more than 13 points higher than Fable 5 (Simon Willison). Because Sol handles long‑running, multi‑step tasks, it’s ideal for developers who need code generation, automated testing, or AI‑driven data pipelines.
Pricing reflects its power: $5 per million input tokens and $30 per million output tokens (Forbes; TechCrunch). If your project runs many thousands of tokens, Sol’s cost per task is roughly one‑third of comparable Anthropic offerings.
02Terra: a balanced choice for everyday content creation
When reviewing a GPT-5.6 model comparison, Terra sits between Sol and Luna. It’s marketed as the “model for daily work” and delivers a solid mix of speed and capability. The pricing is $2.50 input / $15 output per million tokens (Forbes). In benchmark terms Terra performs just above Claude Fable 5, making it a safe pick for writers, marketers, and video editors who need reliable language generation without the heavy compute of Sol.
Because Terra’s cost is half that of Sol, a typical blog‑post workflow (≈0.2 M tokens) costs around $3 in output, well within most freelance budgets.
03Luna: the fast, low‑cost option for rapid prototyping
Luna is the smallest tier, priced at $1 input and $6 output per million tokens (Forbes). It shines when you need quick drafts, thumbnail‑size image generation, or simple chat replies. While its raw performance lags Sol, Luna still outperforms Anthropic’s Opus 4.8 on the same benchmark suite (Simon Willison), and it does so at roughly one‑sixteenth the cost.
For creators who run dozens of short prompts a day—like social‑media managers producing meme captions—Luna keeps the per‑day bill well under $1.
04Max and Ultra reasoning modes: scaling performance without changing the model
OpenAI added two new reasoning modes that sit on top of any tier. Max mode allocates extra compute for especially tough problems, while Ultra spins up four parallel sub‑agents to tackle multi‑step workflows (Forbes). The cost impact is proportional to the extra tokens consumed, but the pricing per token stays the same. For example, a Max‑level Sol request that uses 0.5 M output tokens will cost 0.5 × $30 = $15, still cheaper than a standard Claude Fable 5 run that would consume more tokens.
If you need to run a multi‑agent automation—like generating a slide deck, pulling data from Google Sheets, and then creating a summary video—Ultra on Terra often finishes faster than Sol in standard mode, letting you stay within the same budget.
Sources: Forbes – OpenAI’s GPT-5.6 Lands With Work Agents And A Desktop Pivot, TechCrunch – OpenAI launches its new family of models with GPT-5.6, Simon Willison – The new GPT-5.6 family: Luna, Terra, Sol.
For most creators, Terra offers the best balance of cost and capability, while Sol is reserved for heavy coding or long‑running agentic tasks. Luna is perfect for quick, high‑volume prompts, and the new Max/Ultra modes let you stretch performance without jumping to a pricier tier.