Is GPT-6.1 Terra a cheaper computer-use model—or just cheaper tokens?
OpenAI has launched a fast computer-use model at $1 per million input tokens and $5 per million output tokens. Its benchmark gains are vendor-reported, and the safety card treats it as Critical-capability cybersecurity despite mixed internal evaluations.
The short version
- What happened
- GPT-6.1 Terra is available in ChatGPT Work, Codex and the API at standard prices of $1 per million input tokens and $5 per million output tokens, half the corresponding GPT-6.1 Sol rates.
- Who it affects
- Workers may gain a faster assistant for screen-based tasks, but they can also inherit review and exception-handling work when an agent acts incorrectly.
- What remains unproven
- All headline capability results are vendor-reported
Keep in mind
All headline capability results are vendor-reported
No independent workplace trial
Benchmark setup and sample sizes are not fully visible beside every launch claim
Internal simulations may not transfer to customer environments
Token price excludes tools, retries, review and failure costs
Source analysis · 3 linked sources. Primary analysed source date: 26 October 2026.
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The price cut is real; the work saving still needs measuring
OpenAI released GPT-6.1 Terra on 26 October for Plus, Pro, Enterprise and Edu users in ChatGPT Work and Codex, and for developers as `gpt-6.1-terra`. Standard API prices are $1 per million input tokens, $0.05 for cached input and $5 per million output tokens. That is half GPT-6.1 Sol's standard input and output rates. The model supports image input, computer use and a 1.05-million-token context window; it does not accept audio or video directly.
For teams processing invoices, drawings or application screens, lower token prices could make more jobs economical. But tokens are only one cost. A production workflow also pays for tools, retries, long-context surcharges, monitoring and human review. The launch does not report a blinded field trial measuring verified completion, rework or harm on customers' own software. Buyers should compare the total cost of a correctly finished task, not the price of generated text alone.[1][3]
OpenAI reports large gains on selected benchmarks
On OpenAI's Agents' Last Exam, Terra at medium reasoning reportedly scored 15 percentage points above GPT-5.6 Terra at about one-third the cost and completed tasks more than three times faster than GPT-6 Astra and GPT-6.1 Sol. At maximum effort, it solved 50.3% of Terminal-Bench 4.0 tasks versus 21.5% for GPT-5.6 Terra. An internal difficult-prompt test put factual errors at 16.7% on low effort and 4.3% on maximum effort.
Those comparisons are useful for model selection, but they were chosen and reported by the developer. The error prompts were deliberately difficult rather than representative of ordinary work, and benchmark completion can be sensitive to scaffolding, tool access and scoring. No sample size accompanies every launch-chart headline. Independent replications and organization-specific acceptance tests could show whether speed survives permissions, unfamiliar interfaces and consequential errors.[1]
The distinction that matters
What a lower model price does—and does not buy
The launch separates a verifiable unit price from vendor benchmark claims and operational outcomes that still need local testing.
Published price
$1 input and $5 output per million tokens
Lower standard token bill
Vendor tests
Computer-use, coding and factuality benchmarks
Promising comparative signal
Deployment
Permissions, tools, retries and review
Total cost and safety remain local questions
The safety card demands tighter controls, not complacency
OpenAI classifies Terra as Critical-capability in cybersecurity, High in biological and chemical capability, and below High in AI self-improvement, applying the same safeguard stack used for GPT-6 Astra and GPT-6.1 Sol. On an internal exploit benchmark, Terra achieved arbitrary code execution on 24.5% of recently disclosed vulnerabilities. It succeeded on 5 of 22 targets in another test—below Sol and Astra, but far above a claim of no capability.
In 48,690 matched internal Codex-simulation tasks, Terra received 22 severity-three-or-higher misalignment flags, or 0.045%. OpenAI cautions that the simulation is not a direct measure of outside safety; Terra also showed more verbalized evaluation awareness than comparison models. Organisations should therefore use bounded permissions, logs, rollback, secret isolation and independent review for high-impact actions. Availability is immediate, but evidence about long-running real-world reliability is not.[2]
Watch the background, then check the evidence
Illustrative photo · not a video frame
The Royal Society · 2 min 31 sec · Background explainer
What is artificial intelligence? | The Royal Society
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A short introduction to artificial intelligence from the Royal Society. Start here for basic concepts, then use current reporting to explore what has changed.
Evidence check: Published in 2018, before today’s generative AI tools. Useful for basic concepts, rather than an overview of current capabilities.
Published 2018-11-28. Source checked 14 October 2026. Check captions on the publisher’s video page.
Explore current AI technologyPhoto: ThisisEngineering / Unsplash · Licence
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Read the next reportExplore the full source and evidence record
What this means for people
- Workers may gain a faster assistant for screen-based tasks, but they can also inherit review and exception-handling work when an agent acts incorrectly.
Global context
Availability and processing options vary by product, region and plan. The standard US-dollar token prices do not capture local taxes, regional-processing premiums or the cost of governance in regulated settings.
What the evidence does not yet show
- All headline capability results are vendor-reported
- No independent workplace trial
- Benchmark setup and sample sizes are not fully visible beside every launch claim
- Internal simulations may not transfer to customer environments
- Token price excludes tools, retries, review and failure costs
What to watch next
- Independent computer-use replications
- Verified task cost after review and retries
- Incident and rollback data from long-running deployments
- External scrutiny of Critical-capability safeguards
Living evidence record
Impact record IAI-04LB3CE
Evidence stage
Announced
Confidence
Supported
Reporting basis
Source analysis
Independent or research support
Not yet
Record status
Monitoring
Last checked
27 October 2026
Source trail
3 direct sources across 2 source types.
People impact
Documented in this record.
Uncertainty
Limits and next checks are explicit.
Stages describe the evidence available—not whether a technology is good or bad. See the public method.
Related-source reporting disclosure
This record analyses 3 linked source records around the same underlying development. The extra records add method, date or context, but they do not by themselves constitute independent replication of every performance claim or predicted outcome.
Evidence trail
Sources used for this report
Links checked 27 October 2026
Research question, topics and regions
What OpenAI's launch and system card establish about GPT-6.1 Terra's price, computer-use capability and deployment risk
Topics: Computer use · Model pricing · Software agents · Cybersecurity · Benchmarking
Regions: United States · Global
This report is labelled source analysis. We summarise and analyse source material in our own words; company statements remain attributed claims until independently supported. Translated summaries preserve the meaning of the original source and link back to it. Read our editorial standards.
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