GPT-6.1 Terra: half the price, what it changes for you

On October 26, 2026, OpenAI shipped GPT-6.1 Terra and announced API prices 50% below the previous version. The next day, one of its engineers summed up in a single sentence how to choose between their four models. The real question is therefore no longer « which model is best? » but « which one costs me the least for what I actually need to do? ». Here are the official figures and three student-sized calculations.

In short: GPT-6.1 Terra now costs $1.00 per million input tokens and $5.00 per million output tokens, against $2.00 and $12.00 for GPT-5.6 Terra. The « half the price » shorthand is accurate on input, but it undersells the cut on output (−58%) and on cached input (−75%). The model is limited to Plus, Pro, Enterprise and Edu subscribers in ChatGPT Work, to every tier in Codex, and to the API — no free account. OpenAI claims it matches its large Astra model at one-tenth the price per token, on its own benchmarks. A safety addendum places it at the « Critical » cybersecurity threshold.

What OpenAI announced on October 26

The announcement went up on October 26, 2026 on OpenAI’s developer forum, under the title « GPT-6.1 Terra: delivering fast and capable computer use at a lower price ». Its opening sentence sets the agenda: « GPT-6.1 Terra understands what’s on screen, takes action across apps, writes naturally, and is a more capable model for your whole workday with the efficiency to take on more of it. » It reads a screen, acts inside applications, writes better, burns fewer tokens.

Two numbers come with the launch: standard API prices 50% below GPT-5.6 Terra, and performance comparable to GPT-6 Astra — the in-house flagship — at one-tenth the price per token. The same day, Anthropic filed its Form S-1 with the SEC. The timing is probably not a coincidence.

The official rates: where « half the price » holds, and where it does not

Prices from OpenAI’s pricing page, checked on October 28, 2026, per million tokens:

Model	Input	Cached input	Output
gpt-6.1-terra	$1.00	$0.05	$5.00
gpt-5.6-terra	$2.00	$0.20	$12.00
gpt-6.1-sol	$2.00	$0.10	$10.00
gpt-6-astra	$10.00	$1.00	$50.00
gpt-6-luna	$0.10	$0.01	$0.50

Run the arithmetic yourself. Input is indeed halved, cached input divided by four. But output goes from $12.00 to $5.00, a 58% cut, not 50%. So OpenAI’s « half the price » is conservative on the line that costs the most: on a reasoning model, output is what builds the bill.

Two useful details. In Batch mode (a batch of requests, answers within 24 hours) and in Flex mode, the rates are halved again: $0.50 / $0.025 / $2.50. And The Decoder lists $10 on output for GPT-5.6 Terra where the official page and Next both show $12. When in doubt, the vendor’s own pricing page wins.

What Terra does better

A warning: every gain announced comes from OpenAI’s internal benchmarks. These are the vendor’s numbers. On Terminal-Bench 4.0, where the model has to complete tasks inside a terminal, the success rate goes from 21.5% to 50.3% — at maximum reasoning effort, The Decoder specifies. On an internal set of trick questions, Next reports an error rate at low effort falling from 53.6% to 16.7%, and dropping 18 points at maximum effort to reach 4.3%. Terra also finishes tasks more than three times faster than both Astra and GPT-6.1 Sol.

One third-party observation: according to The Decoder, the firm Artificial Analysis finds that at low effort, Terra offers the market’s best ratio of intelligence to cost per task.

Astra, Sol, Terra, Luna: Ted Sanders’s rule

On October 27, Ted Sanders, of OpenAI, offered a rule of thumb for finding your way around: « If you care about quality only, pick Astra. If you’re a bit price sensitive, pick Sol. If you’re very price sensitive, pick Terra or Luna. »

He adds a caveat that holds across the whole field: size explains most of the gaps, but a newer, smaller model can beat an older, larger one thanks to training data or architecture. Terra is his own illustration: it beats the larger Astra on technical communication and matches it on image handling and computer use, while trailing elsewhere.

Where the rule breaks down: it assumes you pay per token — on a ChatGPT subscription, you are not picking by the cent — and that the model succeeds on the first try.

Where you can actually use it: not on a free account

GPT-6.1 Terra is available to Plus, Pro, Enterprise and Edu subscribers in ChatGPT Work, across every subscription tier in Codex, and through the API as gpt-6.1-terra. On a free account, no.

On the API side, the developer documentation lists the accepted reasoning effort levels: none, low, medium (the default), high, xhigh and max. The none mode, which neither Astra nor GPT-6.1 Sol accepts, re-enables temperature and top_p. This is the most effective and least known cost lever: a reasoning model bills its thinking tokens at the output rate, and moving from max to low can divide your bill several times over.

Finally, OpenAI’s « Back to School » offer, four free months of ChatGPT Plus to be claimed by October 31, 2026, is reserved for students at eligible US institutions. It does not cover students elsewhere.

What a student project actually costs: three calculations

Per-million-token prices mean nothing to anyone. Let us translate: a dense page of course notes runs about 600 tokens.

Calculation 1 — one quiz session. You paste 20 pages of notes, about 12,000 input tokens, and the model answers in 1,500 tokens. Terra: $0.012 + $0.0075, so 2 cents. Astra: about 20 cents. Luna: 0.2 cents. For revision, the most expensive model in the catalogue costs the price of a sweet.

Calculation 2 — an end-of-semester coding project. Say 300 round trips with an agent over a week, each one re-reading your project: 25,000 input tokens of which 20,000 are served from cache, and 2,000 output. Terra: $0.016 per turn, so $4.80 in total. GPT-5.6 Terra: $11.40. Astra: $51. That is where the cut shows, on repeated agentic use, not on a one-off question.

Calculation 3 — the one that matters. Run calculation 2 again with Luna, ten times cheaper: $0.48. Tempting. Except that if Luna fails half the time where Terra succeeds, you redo the work, you read through broken code, and you lose two hours. The question is not which model is cheapest in dollars, but which one saves you the most hours: on a student budget those amounts are trivial; your time is not.

For comparison, Anthropic’s pricing page lists Claude Opus 5.5 at $4.00 input and $20.00 output: Terra is exactly four times cheaper per token, verifiable on the two official pages. OpenAI further claims, according to The Decoder, a cost per task roughly nine times lower thanks to more frugal token consumption — that second half is not verifiable from outside.

The « Critical » cybersecurity threshold

The least-covered part is the most interesting. On October 26, OpenAI published on its Deployment Safety Hub an addendum to the GPT-6 Astra system card. GPT-6.1 Terra reaches the « Critical » cybersecurity threshold of the Preparedness Framework — the ability to develop functional zero-day exploits in hardened critical systems, or to execute novel end-to-end attack strategies. The model keeps the safeguards built for Astra.

The same document gives honest figures: a 99.99% defender success rate against direct attacks on the instruction hierarchy, and robustness judged substantial against indirect injections — an instruction hidden in a web page or a PDF you ask the model to read. Two signals are less flattering: Terra follows instructions given to its chain of thought in 14.8% of cases against 44.8% for GPT-6.1 Sol, and shows verbalized signs of knowing it is being evaluated in 9.88% of trajectories against 4.35% for Sol.

Nothing dramatic for you: you are not the target. But take the lesson about indirect injection. If you let an agent read web pages, PDFs or code repositories you did not write, their content is not a trustworthy source of instructions.

Our take

The cut is real and structural. This is not a launch promotion, it is the list price. A student coding with an agent can run a semester project for the price of a sandwich. Eighteen months ago, cost was a reason to say no. It no longer is.

The performance figures are still the vendor’s. Terminal-Bench, the error rates, the nine-to-one comparison with Claude Opus 5.5: all of it comes from OpenAI’s internal testing, and Next says so explicitly. The week’s one third-party measurement points the same way — but a corroboration is not a verification.

Price per token is not real cost. Ted Sanders’s rule ranks models by price, not by how many attempts they need. Start small — Terra at low effort, or even Luna — and only move up if the result does not hold. That is the logic of breaking work into short, checkable prompts: the smaller the task you hand over, the less the choice of model matters.

And if you use any of this for graded work: check your institution’s rules on AI use.

FAQ

Is GPT-6.1 Terra available on a free ChatGPT account?

No. The October 26 announcement reserves it for Plus, Pro, Enterprise and Edu subscribers in ChatGPT Work, for every subscription tier in Codex, and for developers through the API.

Should you always pick the cheapest model?

No. Price per token says nothing about how many attempts you will need. Start at the lowest reasoning effort on a mid-tier model, and only move up if the answer does not hold.

Does OpenAI’s « Back to School » offer apply outside the United States?

No. According to OpenAI’s help page, it targets students at eligible US degree-granting institutions, verified through SheerID, and had to be claimed by October 31, 2026. The subscription then renews at $20 per month unless cancelled.

Further reading

• Why AI costs so much to run: where these per-million-token prices come from.
• New AI models in 2026: which one should you study with?: the overview of consumer models.
• The best AI tools for students: the comparison by use case, beyond price.
• The prompts-vibe-coding skill: break a feature into short, checkable prompts.