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TL;DR
OpenAI released GPT-6.1 Terra on October 26, 2026, pricing API use at $1 per million input tokens and $5 per million output tokens. Artificial Analysis found its low- and medium-effort settings offer strong cost efficiency on its benchmark, while higher effort costs more and still scores below leading models. The comparison depends on task and effort level; token prices alone do not show the cost of completing work.
OpenAI released GPT-6.1 Terra on October 26, pitching it as a lower-cost model for routine communication and computer-based work. The model is priced at $1 per million input tokens and $5 per million output tokens, while independent benchmark figures from Artificial Analysis suggest its low- and medium-effort settings can complete evaluated tasks at comparatively low cost, though Terra does not match the highest-scoring models.
Terra is available through the API under the model name gpt-6.1-terra, as well as in ChatGPT Work and Codex for Plus, Pro, Enterprise and Edu users, according to the launch report. OpenAI says its API rates are 50% lower than GPT-5.6 Terra’s; cached input is listed at $0.05 per million tokens. The company describes Terra as a fast, efficient option for work such as reading invoices and entering information into accounting software.
Artificial Analysis’s evaluation gives Terra a 48.1 Intelligence Index score at maximum effort, at an average cost of $0.40 per task across its benchmark. The same figures list GPT-6.1 Sol at 51.8 and $0.72 per task, while Claude Opus 5.5 scores 57.6 at $5.98 per task. Those are benchmark averages, not guaranteed prices or outcomes for an individual customer’s workload.
Terra’s six effort settings produce a trade-off between score and cost. Artificial Analysis measured results ranging from 33.5 points and $0.107 per task in non-reasoning mode to 48.1 points and $0.40 at maximum effort. Low effort scored 36.2 at $0.055 per task. The launch report says low and medium settings sit on the benchmark’s cost-efficiency frontier; higher settings cost more without offering the same efficiency by that measure.
Infographic 01 · The effort curve
One model, six settings, a 7-fold cost range.
GPT-6.1 Terra at each effort level, as measured by Artificial Analysis. Low and medium sit on its cost-efficiency frontier: no tested model is both cheaper and better.
Score · Intelligence Index
independent higher is better
Cost per finished task
note the last step shorter is cheaper
All six settings
| Effort | Index | Cost / task | Output tokens / task | Hallucination rate |
|---|---|---|---|---|
| Non-reasoning | Index 33.5 | Cost $0.107 | Tokens 4,737 | Hallucination 72% |
| Low | Index 36.2 | Cost $0.055 | Tokens 2,681 | Hallucination 46% |
| Medium | Index 41.3 | Cost $0.095 | Tokens 5,835 | Hallucination 44% |
| High | Index 43.9 | Cost $0.134 | Tokens 9,027 | Hallucination 43% |
| Xhigh | Index 45.4 | Cost $0.194 | Tokens 14,551 | Hallucination 42% |
| Max | Index 48.1 | Cost $0.403 | Tokens 35,096 | Hallucination 38% |
Hallucination rate in AA-Omniscience: how often the model answers anyway when it does not know. Lower is better.
The first steps up are cheap. The last one doubles the bill.Assessment: Thorsten Meyer
Sources: Artificial Analysis model pages (retrieved 26 Oct 2026); Artificial Analysis launch article (26 Oct 2026). Cost per task is the average across the index’s evaluation tasks.
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Task Costs Reshape Model Choice
The launch highlights a practical issue for companies buying AI services: the cost per completed task can differ from the cost implied by token rates. A cheaper model per token may generate more output, while a pricier one may use fewer tokens to finish the same benchmark task. Artificial Analysis’s reported comparison found GPT-6 Luna’s per-token rates were lower, but Terra at low effort had a lower average task cost in its evaluation.
That distinction matters for teams handling large volumes of routine work, where small differences in cost per successful task can affect budgets and model selection. Terra may offer a middle option between a small model and OpenAI’s higher-capability offerings. Yet its benchmark score trails leading models, and OpenAI itself says Sol and Astra remain better choices for the most complex coding tasks. Buyers will need to test their own workloads, including the cost of retries, human review and mistakes.
The results also suggest effort setting is a purchasing decision, not just a technical setting. The reported curve rises from $0.055 per task at low effort to $0.40 at maximum, while the score increases from 36.2 to 48.1. That extra expense may be justified for harder tasks, but the benchmark does not establish that maximum effort is worthwhile for every use case.
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Terra Between Luna and Sol
OpenAI positions Terra between GPT-6 Luna, its smaller model, and larger models including Sol and Astra. The launch report says Terra’s list price is half that of GPT-5.6 Terra, its predecessor, for input and output tokens. OpenAI also says eligible ChatGPT Work and Codex users can handle substantially more tasks under the same allowance than with Sol or Astra; that is the company’s description, not an independently measured allowance comparison.
OpenAI published its own evaluations alongside the launch. On the computer-use benchmark Agents’ Last Exam, the company reports Terra at medium effort scored 57.6% for $0.29 per task, compared with GPT-5.6 Terra at 42.6% and $0.84. OpenAI says Terra at maximum effort costs $1.01 per task on that test, against $8.82 for Claude Opus 5.5, while also reporting a lower Terra score: 60.0% versus 63.2%.
Other company-reported results include a 50.3% score on Terminal-Bench 4.0 at maximum effort, compared with 21.5% for GPT-5.6 Terra’s published result. OpenAI also reports lower error rates on its internal error-inducing prompts and says Terra completes tasks more than three times faster than Astra and Sol. These are OpenAI’s measurements. Artificial Analysis had not published speed measurements for Terra as of the report’s October 26 check.
Benchmark Results Have Limits
The reported figures do not show how Terra will perform across every customer’s tasks. Artificial Analysis’s cost per task is an average across its evaluation set; actual spending can vary with prompts, context length, retries, usage volume and selected effort. The supplied material does not provide the full per-setting results table, so the medium setting’s exact score and cost cannot be independently restated here.
OpenAI’s claims about speed, safeguards and internal error rates are company-reported. The report says OpenAI classifies Terra as meeting the “Critical” threshold in cybersecurity under its Preparedness Framework, but that classification is not an independent assessment. It is also unclear how the model’s real-world error rates compare across different industries or how much human checking is needed for tasks such as invoice processing.
Terra is not the top scorer in the cited Artificial Analysis table. The same results list Xiaomi’s open-weight MiMo-V2.6-Pro at 46.3 points and $0.13 per task, below Terra’s score but at a lower average cost. The best option therefore depends on the required quality, task mix, deployment needs and price—not on a single model ranking.
Test Costs on Real Workloads
The next useful measure will be independent testing of Terra across practical workloads, including task completion, accuracy, speed and total operating cost. Artificial Analysis’s launch-day evaluation offers an initial comparison, but its figures do not settle how Terra performs on each company’s data or software tools.
Organizations considering the model can compare low and medium effort against their current systems, tracking the cost of completed work rather than token use alone. They will also need to account for error review and escalation to a larger model for harder cases. OpenAI’s launch makes Terra available through its listed products and API; the supplied material does not give a date for further independent speed measurements or additional benchmark updates.
Key Questions
What is GPT-6.1 Terra?
GPT-6.1 Terra is an OpenAI model released October 26, 2026, for communication and computer-based work. OpenAI positions it between Luna and its larger models.
How much does GPT-6.1 Terra cost?
OpenAI’s API rates are $1 per million input tokens and $5 per million output tokens, with cached input listed at $0.05 per million tokens. Artificial Analysis reports an average cost of $0.055 per task at low effort and $0.40 at maximum effort on its evaluation set.
Does Terra outperform OpenAI’s larger models?
Not on every cited measure. Artificial Analysis scored Terra at 48.1 at maximum effort, below GPT-6.1 Sol at 51.8 and GPT-6 Astra at 52.7. OpenAI says Sol and Astra remain better choices for its most complex coding tasks.
Why compare cost per task rather than token price?
Token prices do not show how many tokens a model uses to finish a task. Cost per task captures the average spending needed across a benchmark’s evaluations, though real-world costs can differ by workload.
Source: ThorstenMeyerAI.com
