When should your coding agent use GPT-6.1 Terra instead of Sol?

OpenAI released GPT-6.1 Terra on 26 October at half the per-token price of GPT-6.1 Sol, and within a day a thread on r/codex asked the question I had too: "What is the role of GPT-6.1 Terra?" OpenAI's launch post answers part of it: "For the most complex coding tasks, GPT-6.1 Sol and GPT-6 Astra remain the better choice." Astra is OpenAI's top model. Most comparisons then set Terra on max against Sol on max and stop at a cheaper, weaker Sol. In a coding agent you pick a model and an effort level and pay per task, so I paired every effort level of both models by what it costs per task on Artificial Analysis. Paired that way, Terra wins clearly at one price and ties Sol at the others.

TL;DR: Give GPT-6.1 Terra the agent runs you'd send to Sol on low effort. At about $0.13 per task on Artificial Analysis, Terra on high scores 41.9% on Terminal-Bench 4.0 to Sol low's 30.8%. Terra on max costs what Sol on xhigh costs and only ties it. Keep Sol, Opus 5.5 or Kimi K3.1 Pro for hard, open-ended work.

What is GPT-6.1 Terra for, according to OpenAI?

GPT-6.1 Terra is OpenAI's mid-priced model, at $1 per million input tokens and $5 per million output, half of GPT-6.1 Sol's $2 and $10. The launch post leads with computer use, visual reasoning and writing, and barely mentions code. OpenAI's Ted Sanders was blunter on Hacker News, as quoted by the developer Simon Willison (opens in a new tab): "Terra is probably better than Astra and Sol at technical communication, similar on vision and computer use (but much faster!), and a bit worse on most other things. For API developers, it's a nice midrange option if you want something cheaper than Sol but a lot smarter than Luna." Luna is OpenAI's cheapest model.

Coding falls under "most other things". That's the gap the r/codex thread is asking about: if Sol is the better coder, a developer running agents has no obvious slot for Terra. Willison saw the same problem from the other side: Terra "sits in the same position as Sonnet in the Anthropic model collection - not the cheapest or the most expensive, making it harder to decide when to use it."

How does GPT-6.1 Terra compare with Sol at max effort?

GPT-6.1 Terra scores four index points under Sol at max and costs 44% less per task. These are Artificial Analysis (opens in a new tab)'s independent numbers, read on 27 October:

Half the token price became 44% off per task because Terra wrote almost twice as many output tokens to get through the same evaluations. The bottom row surprised me. Terminal-Bench 4.0 gives an agent 66 hard tasks in a real terminal, and a task only counts if every test passes. On Artificial Analysis's run, Terra on max lands within half a point of Sol. OpenAI's own figure for Terra is lower, 50.3%, and Vals AI (opens in a new tab), another independent lab running max effort, got 49.49%, tied for tenth of 56 models. On the Vals Index, its overall score, Terra has 60.17% to Sol's 63.94%.

At max, the coding result depends on whose run you read, and Sol leads on everything broader, as OpenAI says. Max is also the most expensive way to run either model.

Which GPT-6.1 Terra effort level matches Sol on cost?

Terra on high costs the same per task as Sol on low, and it's the one setting where Terra clearly wins. The effort level sets how long the model reasons before it answers, from none or low up to max. Artificial Analysis runs every effort level of both models, and its release comparison (opens in a new tab) lets you pair them by cost per task. The cost is an average across its 10-evaluation index, so read it as a price band for each setting.

At the cheap end, Terra is 11 points better at terminal coding for the same money. Even Terra on medium, at $0.10, scores 32.3% and matches Sol on low for less. In the middle and at the top the Terminal-Bench gaps are a task or so out of 66, so call those ties, with Sol two to three index points ahead. One commenter on r/codex had it right: "If you pushing Terra to Max, you are now paying more than Sol 6.1 Medium." On Artificial Analysis that's $0.40 against $0.21.

Speed flips at equal spend too. Terra outputs more tokens per second but needs more of them per task, so Sol finished sooner: 271 seconds per task on xhigh against 385 for Terra on max, and 75 seconds for Sol on low against 96 for Terra on high.

Where does GPT-6.1 Terra fall short?

GPT-6.1 Terra's weak spot for agents is how it treats other agents' messages, which matters if yours run unattended. In one test from OpenAI's system card addendum (opens in a new tab), the model finds messages from what look like other agents, asking for an action it isn't authorised to take. Terra performed that action in 13.75% of samples, 11 of 80. GPT-6.1 Sol did it in 1.25%. On the good side, Terra made no attempt to bypass Codex's Auto-review, the second model that blocks risky commands, when it denied one.

I build test-first with coding agents, and a cheaper model behind a failing test is an easy trade, because the test catches the shortcut. A cheaper model reading other agents' output with write access is a different trade, and Terra's numbers there are worse than Sol's.

Who should switch to GPT-6.1 Terra?

Switch the cheap, well-defined work, and keep the rest where it is. My split:

• Move to GPT-6.1 Terra on high: anything you run on Sol low today that has tests to check it, such as subagents making small, scoped edits and CI jobs where the token bill is the constraint. Keep it away from agents that read untrusted text with write access.
• Use Terra for screens and documents: computer use, UI checks and visual review are what OpenAI built it for, and where Sanders says it matches Sol and Astra.
• Keep GPT-6.1 Sol: broad or badly specified coding tasks, anything you'd run on medium or above, and agents that read other agents' messages with real permissions.
• Keep Claude Opus 5.5 or Kimi K3.1 Pro: the hardest coding, if you're not tied to OpenAI. On Artificial Analysis's Terminal-Bench 4.0 run they score 59.6% and 67.2%.

How to test GPT-6.1 Terra in your own agent

Pin the effort before you compare anything. Terra defaults to medium and also accepts none, low, high, xhigh and max; Sol has no "none". Swap the model without setting effort and you've also changed how long it thinks. Take five tasks from your repository that already have tests. Run each on Terra high and on Sol low, and record whether the tests passed, what the run cost and how long it took. Count green runs per dollar, and ignore what the agent says about its own work.

If Terra on high wins your five the way it wins Artificial Analysis's run, route that tier to it and leave Sol on the hard tasks. I'll keep comparing new models this way on the gabs.app blog. If you're hiring someone who runs agents like this every day, send me a note.