GPT-6.1 Terra vs Claude Opus 5.5: What OpenAI's Own Benchmarks Actually Claim

OpenAI introduced GPT-6.1 Terra on October 26, 2026, and the number that traveled furthest was not a capability score, it was a cost multiple. OpenAI says Terra is over three times as token efficient as Claude Opus 5.5, costs about one fourth as much per token, and lands at roughly nine times lower cost per completed task, all measured at maximum reasoning effort on OpenAI's own evaluation harness. That is a specific, aggressive claim about a competitor's flagship model, and before it shapes a client's model selection it deserves the same question Contra Collective asks about every vendor benchmark: what was actually measured, who measured it, and what does the number leave out.

Terra is a pricing move wearing a capability launch's clothes

The headline specs are plain: $1 per million input tokens, $0.05 per million cached input tokens, $5 per million output tokens, which OpenAI says is 50 percent lower than both GPT-5.6 Terra and GPT-6.1 Sol. OpenAI positions Terra for computer use, visual reasoning, coding, and everyday office work, the kind of thing where a model fills out a form, reads an incoming invoice into accounting software, or interprets a technical drawing. It is available now in ChatGPT Work and Codex for Plus, Pro, Enterprise, and Edu subscribers, and through the API as gpt-6.1-terra. OpenAI's own guidance still points coding heavy teams toward GPT-6.1 Sol or GPT-6 Astra for the hardest tasks, which tells you plainly where OpenAI thinks Terra's ceiling actually sits.

The number that improved the most is error rate, not raw score

Measured against its own predecessor, Terra's gains are real and specific. Error rate on OpenAI's internal error inducing prompt set fell from 53.6 percent to 16.7 percent at low reasoning effort compared with GPT-5.6 Terra, and dropped a further 18 points to 4.3 percent at maximum effort, which OpenAI says nearly matches GPT-6 Astra and GPT-6.1 Sol. On Agents' Last Exam, a benchmark built around professional computer use tasks such as turning an architectural drawing into a 3D model, Terra scored 15 points higher than GPT-5.6 Terra at medium effort while costing about a third as much. On Terminal-Bench 4.0, which scores command line coding agents, Terra solved 50.3 percent of tasks at maximum effort against 21.5 percent for its predecessor. Every one of those numbers is a comparison against OpenAI's own prior model. None of them is a head to head result against Claude Opus 5.5.

Where the Opus 5.5 numbers actually come from

The three times token efficiency, one fourth per token cost, and nine times per task cost figures against Opus 5.5 come entirely from OpenAI's own release materials, measured at maximum reasoning effort, with no published methodology describing how the task set was built or how Opus 5.5 was configured on Anthropic's side. That does not make the numbers false. It makes them a vendor's self reported comparison, which sits in a different evidentiary category than a third party evaluation run on both models under matched conditions by a group with no stake in the outcome. As of this writing, groups like Epoch AI and Artificial Analysis have not published their own Terra versus Opus 5.5 numbers, and that gap is exactly where the real answer will come from once it exists.

What the safety classification quietly tells you

OpenAI classifies Terra as meeting the Critical threshold for cybersecurity under its Preparedness Framework, carries over protections built for GPT-6 Astra, and reports stronger resistance to prompt injection than GPT-5.6 Terra. Pairing a cheap, fast, computer use model with that classification is a signal worth reading on its own. A model priced to run constantly, wired into desktop automation, reading screens and filling forms at scale, is precisely the deployment shape where a prompt injection failure causes real damage, and OpenAI's own safety framing suggests it knows that.

What this means if you are actually choosing between them

If the workload is agentic computer use at volume, high frequency, lower complexity tasks across a large user base, Terra's pricing changes the unit economics of that workload on its own merits, independent of how the Opus 5.5 comparison eventually holds up. If the workload sits at the hard end of coding or multi step reasoning, OpenAI's own documentation points toward Sol or Astra, not Terra, which makes the Opus 5.5 comparison largely beside the point for that decision. The number actually worth tracking over the coming weeks is not OpenAI's claim, it is whichever independent lab publishes the first matched evaluation of both models, because that will be the first number in this entire comparison that was not produced by the company selling the product.

FAQ

Is GPT-6.1 Terra available through the API today? Yes, as gpt-6.1-terra, alongside availability in ChatGPT Work and Codex for Plus, Pro, Enterprise, and Edu subscribers as of its October 26, 2026 launch.

Did Anthropic respond to OpenAI's cost comparison against Opus 5.5? Not publicly as of this writing. Every number in that comparison comes from OpenAI's own published materials.

Should a coding heavy team switch to Terra to save money? OpenAI itself still recommends GPT-6.1 Sol or GPT-6 Astra for the most demanding coding work, so the honest reading of OpenAI's own guidance is that Terra is a cost play for high volume, lower complexity agentic work rather than a replacement for frontier coding models.

Does Terra match GPT-6 Astra's context window? OpenAI has not published a context window figure for Terra in its initial announcement. Confirm the current figure against live documentation before sizing a production deployment around it.

When this applies to your stack

A vendor benchmark is a starting point for a model selection conversation, not the end of one, especially when the comparison is self reported against a competitor who has not yet had the chance to respond with its own numbers. Contra Collective helps engineering teams run their own matched evaluations across model providers before a production agent pipeline gets committed to a single vendor's cost claims.