The 10-second verdict: Claude Opus 5.5 is a few points more accurate; GPT-6.1 Terra is nearly an order of magnitude cheaper per task. For a computer-use agent that fires thousands of steps a day, that is not a close call in one direction or the other — it means you should run both and route between them. Terra as the default workhorse, Opus 5.5 as the escalation path. Jump to how to actually choose if you just want the decision tree.
OpenAI shipped GPT-6.1 Terra on October 26 — a mid-tier model explicitly tuned for computer use and agents — at $1 per million input tokens and $5 output ($0.05 cached), callable as gpt-6.1-terra and live in ChatGPT Work and Codex. The obvious question for anyone building agents is whether it's good enough to replace the flagship you're paying for today. The honest answer is: it depends on a number most comparison posts skip — how many model calls your agent makes per completed task.
The accuracy gap is real, and small#
On the benchmarks OpenAI published at launch, Opus 5.5 is ahead. On Agents' Last Exam at max effort, OpenAI's own chart puts Opus 5.5 at 63.2% and Terra at 60.0% — a roughly three-point gap. On the Artificial Analysis Intelligence Index, Opus 5.5 (max, with fallback) lands around 58 to Terra's ~48, a wider spread that reflects general reasoning more than agent execution. On computer-use specifically (OSWorld), Terra posts 66.7% at medium effort and 74.2% at max per OpenAI; Opus 5.5 is reported anywhere from ~81% to ~87% depending on which version of the benchmark you read.
Two caveats before you weigh those. First, these are vendor-chosen benchmarks — OpenAI picked the charts that frame Terra well, and cross-vendor numbers rarely line up apples-to-apples. Second, benchmark accuracy is not the same as your task success rate; a model that's 3 points better on a leaderboard may be 10 points better or 0 points better on your specific workflow. Run the bake-off yourself.
The cost gap is enormous, and compounding#
Here's the number that actually decides it. On that same Agents' Last Exam chart, a max-effort task costs about $1.01 on Terra and about $8.82 on Opus 5.5 — ~9x cheaper per task. Terra's token price ($1/$5) is half of GPT-6.1 Sol's and far under flagship rates.
The reason this matters more than the headline accuracy number is step multiplication. A chatbot makes one model call per reply. An agent makes dozens: read the screen, plan, call a tool, read the result, re-plan, act again. Every one of those steps pays the per-call price. A 9x cost difference on a single call becomes a 9x difference on your entire infrastructure line — and for a solo founder, the infra line is the difference between a feature that ships and one that can't afford to run. This is the same economics behind why agent costs scale faster than you expect: the model price is a multiplier, not an addend.
How to actually choose#
Don't pick a side. Pick a router. The architecture that wins on both axes is Terra-by-default with Opus-on-escalation:
- Use GPT-6.1 Terra for the high-volume, recoverable majority of steps: navigating UI, reading screens, extracting data, drafting, and any action you can cheaply verify or undo. This is where the 9x savings lives and where 3 points of accuracy rarely changes the outcome.
- Escalate to Claude Opus 5.5 for the hard or irreversible steps: a financial action, a destructive operation, a one-shot decision with no retry, or any step where a failed task costs more than your entire token bill for the day. Here the 3 points are cheap insurance.
- Route per step, not per app. A thin layer that classifies each step's value and picks the model captures most of Terra's savings without taking Opus's accuracy off the table. Keep that abstraction at your boundary so the default is a config change, not a rewrite — the same portability discipline that protects you when prices move again next quarter.
- Gate the irreversible steps regardless of model. Cheaper agents mean more agent runs, and more runs mean more chances for an unintended action. Make your tools declare their risk honestly with MCP tool annotations like
readOnlyHintanddestructiveHint, and keep a human in the loop on anything that writes to the world.
The bottom line: this launch didn't crown a new best model — Opus 5.5 is still the sharper tool. What it did was make the cheap option good enough that paying 9x for the last three points is now a deliberate choice, not a default. For most agent workloads, the right answer is both: measure them head-to-head on your own eval harness, route Terra-first, and spend the accuracy premium only where a failed task actually hurts.
