GPT-5.6 Terra wins on price ($2 vs $5/1M input). GPT-5.5 wins on coding (96 vs 94). For most workflows, GPT-5.6 Terra is the stronger default — best openai value — near-flagship capability at 60% off.
The safest GPT-5.6 Terra vs GPT-5.5 default, the cheaper option worth trying first, and the specialist pick — before you read the detail below.
GPT-5.6 Terra is the strongest answer here for GPT-5.6 Terra vs GPT-5.5 — pick it when quality of output matters more than the $2.00/1M/1M input you pay for it.
GPT-5.1-Codex-Max handles the same job for about 20% less per token. Start here and only move up if the output is not good enough.
GPT-5.5 is the fastest of these for GPT-5.6 Terra vs GPT-5.5 — worth it when latency is what the reader notices, not the last few points of reasoning depth.
GPT-5.5 leads on coding with a score of 96 vs 94 for GPT-5.6 Terra.
GPT-5.6 Terra has the larger context window: 1.05M vs 1M for GPT-5.5.
GPT-5.6 Terra is cheaper at $2/1M input tokens vs $5/1M for GPT-5.5.
Go with GPT-5.6 Terra if you want one model to handle coding and writing — it targets high-volume production and enterprise workloads.
Switch to GPT-5.5 when your work is mostly agentic coding and computer-use workflows; on that narrower brief it is the better tool.
Both models serve different primary workflows — GPT-5.6 Terra for high-volume production and enterprise workloads, GPT-5.5 for agentic coding and computer-use workflows — so running each where it has a clear edge often beats forcing one to do both.
Switch the scoring lens to see whether the GPT-5.6 Terra vs GPT-5.5 answer changes when cost, speed, or long-document depth leads the decision.
OpenAI / Balanced / Aug 6, 2026
Best OpenAI value — near-flagship capability at 60% off.
Ranks models by the broadest mix of coding, writing, research, and long-context usefulness.
You need Sol-level computer use or Opus 5-level repo engineering.
Where the GPT-5.6 Terra vs GPT-5.5 recommendation shifts once you weigh price or latency differently.
Best OpenAI value — near-flagship capability at 60% off.
Best OpenAI flagship for agentic coding, research, and computer-use work.
List prices and published scores — the numbers this page's pick is built from.
| Model | Input | Output | Est. month | Context | Speed | Coding | Writing | Research |
|---|---|---|---|---|---|---|---|---|
| GPT-5.6 TerraOpenAI | $2.00/1M | $12.00/1M | $44 | 1.1M tokens | Balanced | 94 | 92 | 93 |
| GPT-5.5OpenAI | $5.00/1M | $30.00/1M | $110 | 1M tokens | Balanced | 96 | 92 | 94 |
Scores out of 100 — how we evaluate models. “Est. month” is 10M in / 2M out at list price: a ceiling, no discounts.
Why each one is on the shortlist for GPT-5.6 Terra vs GPT-5.5, what it is genuinely good at, and where we would steer you away from it.
Our pick for GPT-5.6 Terra vs GPT-5.5. It scores 94/100 on the coding axis we weight this page by, and nothing else in this shortlist matches it on output quality.
The balanced mid-tier of the GPT-5.6 family — GPT-5.5-class capability at a fraction of the price, built for production workloads at scale.
You need Sol-level computer use or Opus 5-level repo engineering.
The sensible OpenAI default for production. Terra delivers ~97% of Sol's benchmark line at 40% of the output price, making GPT-5.5's rate card look obsolete. Pick Sol only for computer-use agents and the hardest reasoning.
Full pricing, benchmark table and release notes on the GPT-5.6 Terra page.
Here for latency: it answers fastest of anything listed for GPT-5.6 Terra vs GPT-5.5, at 96/100 on coding.
OpenAI's latest agentic flagship for coding, research, computer-use workflows, and long multi-step knowledge work.
You only care about the highest public coding benchmark score or need a cheaper high-volume model.
The strongest OpenAI pick for agentic coding and knowledge work. Claude Opus 4.7 still wins on the public SWE-Bench Pro coding number, but GPT-5.5 is the better OpenAI default when ecosystem, Codex, or computer-use workflows matter.
Full pricing, benchmark table and release notes on the GPT-5.5 page.
UseRightAI recommendations are based on practical decision factors people actually feel in day-to-day use.
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GPT-5.6 Terra wins on more of the categories we score — coding, writing, research — so it is the better default of the two. GPT-5.5 is the better pick when your work is mostly agentic coding and computer-use workflows. Neither is universally "better": GPT-5.6 Terra is aimed at high-volume production and enterprise workloads, GPT-5.5 at agentic coding and computer-use workflows.
GPT-5.6 Terra is cheaper at $2/1M input and $12/1M output. GPT-5.5 costs $5/1M input and $30/1M output.
GPT-5.6 Terra has the larger context window at 1.05M tokens vs GPT-5.5's 1M. For large document analysis, GPT-5.6 Terra is the stronger pick.
GPT-5.5 is better for coding with a score of 96 vs GPT-5.6 Terra's 94 (out of 100). GPT-6 Astra is the overall coding leader in this directory at 100/100.
Both GPT-5.6 Terra and GPT-5.5 have similar speed profiles — rated balanced. Neither will be the bottleneck if latency is your deciding factor.
Big gap to Sol on computer use (OSWorld 50.2% vs 62.6%) — not a drop-in Sol replacement for agents. Well behind Claude Opus 5 on repository-level coding (SWE-bench Pro ~63% vs 79.2%). Avoid it if you need Sol-level computer use or Opus 5-level repo engineering. That is the main case for looking at GPT-5.5 instead.
Claude Opus 4.7 leads GPT-5.5 on SWE-Bench Pro for pure coding ceiling. Premium API pricing makes it less attractive for high-volume low-risk work. Avoid it if you only care about the highest public coding benchmark score or need a cheaper high-volume model. Against GPT-5.6 Terra specifically, the gap shows up most on coding (94 vs 96).
Take a moderate workload of 10M input and 2M output tokens a month. GPT-5.6 Terra runs $44.00 (at $2/1M in and $12/1M out); GPT-5.5 runs $110.00 (at $5/1M in and $30/1M out). That is a $66.00/month difference — GPT-5.6 Terra is the cheaper of the two at this volume, and the gap scales linearly as you send more. Output tokens dominate the bill on both, so prompt length matters far less than response length.
Yes, and for most teams that beats picking one. A common split is GPT-5.6 Terra for high-volume production and enterprise workloads, with GPT-5.5 handling agentic coding and computer-use workflows. Since GPT-5.6 Terra is both the stronger and the cheaper option here, a split mainly makes sense if GPT-5.5 covers a capability you specifically need.