GPT-6.1 Terra: close to Opus in one test, nearly 9× lower cost

In a professional computer-use test, GPT-6.1 Terra scores 60% at US$1.01 per case. Claude Opus 5.5 scores 63.2% at US$8.82. Those are OpenAI’s Agents’ Last Exam v1 results, both at Max: Terra trails by 3.2 percentage points at roughly 8.7 times lower published cost. That trade-off is the striking part of the GPT-6.1 Terra launch.

If you are choosing a model for work, the question is practical: how much performance do you need, and what does it cost to get it? Terra’s position changes with the task. Explore the six tests to see where the trade-off works for you.

OpenAI positions Terra as a model for understanding what is on screen and acting inside apps, alongside writing. Tasks in the launch evaluation include turning drawings into a 3D model and reviewing a film shot by shot. These are the provider’s examples, not demos run here. Standard API pricing is 50% lower than GPT-5.6 Terra and GPT-6.1 Sol.

GPT-6.1 Terra benchmarks, with their costs

The chart contains all 143 original data points from the launch charts. Each symbol represents a reported configuration. Switch tests, compare by cost or reasoning effort, and pick a level to read the numbers. The complete data is underneath, including models with a result at Max only.

What does that score cost?

6 tests · 143 official configurations · OpenAI, October 26

Agents’ Last Exam v1

Professional computer-use tasks

Score (%) · higher is better

USD per evaluated case

Select a symbol to inspect that point.

GPT-6.1 TerraClaude Opus 5.5GPT-5.6 TerraGPT-6 AstraGPT-6 LunaGPT-6.1 Sol

GPT-6.1 Terra

60.0 %60.0 %US$1.01US$1.01

Claude Opus 5.5

63.2 %63.2 %US$8.82US$8.82

GPT-5.6 Terra

50.7 %50.7 %US$3.58US$3.58

GPT-6 Astra

59.3 %59.3 %US$7.24US$7.24

GPT-6 Luna

50.9 %50.9 %US$0.14US$0.14

GPT-6.1 Sol

58.6 %58.6 %US$2.00US$2.00

OpenAI reports Opus 5.5 at Max only for this test. Costs come from the benchmark, not a fixed product price.

All 143 points, with effort and cost

Agents’ Last Exam v1

Score: higher is better.

Professional computer-use tasks

Model	Effort	%	USD
Claude Opus 5.5	Max	63.2 %	US$8.82
GPT-5.6 Terra	Low	40.2 %	US$0.59
GPT-5.6 Terra	Medium	42.6 %	US$0.84
GPT-5.6 Terra	High	46.6 %	US$1.72
GPT-5.6 Terra	Xhigh	48.5 %	US$2.50
GPT-5.6 Terra	Max	50.7 %	US$3.58
GPT-6 Astra	Low	53.4 %	US$3.55
GPT-6 Astra	Medium	57.6 %	US$4.82
GPT-6 Astra	High	57.8 %	US$5.15
GPT-6 Astra	Xhigh	58.3 %	US$5.91
GPT-6 Astra	Max	59.3 %	US$7.24
GPT-6 Luna	Low	36.3 %	US$0.022
GPT-6 Luna	Medium	46.8 %	US$0.093
GPT-6 Luna	High	43.6 %	US$0.087
GPT-6 Luna	Xhigh	47.9 %	US$0.093
GPT-6 Luna	Max	50.9 %	US$0.14
GPT-6.1 Sol	Low	52.4 %	US$0.70
GPT-6.1 Sol	Medium	53.8 %	US$1.31
GPT-6.1 Sol	High	55.3 %	US$1.45
GPT-6.1 Sol	Xhigh	56.5 %	US$1.45
GPT-6.1 Sol	Max	58.6 %	US$2.00
GPT-6.1 Terra	Low	55.9 %	US$0.16
GPT-6.1 Terra	Medium	57.6 %	US$0.29
GPT-6.1 Terra	High	57.2 %	US$0.39
GPT-6.1 Terra	Xhigh	58.5 %	US$0.66
GPT-6.1 Terra	Max	60.0 %	US$1.01

MMMU-Pro (Vision)

Score: higher is better.

Visual reasoning

Model	Effort	%	USD
GPT-5.6 Terra	Low	74.9 %	US$0.011
GPT-5.6 Terra	Medium	76.4 %	US$0.012
GPT-5.6 Terra	High	77.5 %	US$0.014
GPT-5.6 Terra	Xhigh	78.7 %	US$0.02
GPT-5.6 Terra	Max	81.7 %	US$0.079
GPT-6 Astra	Low	85.3 %	US$0.038
GPT-6 Astra	Medium	85.3 %	US$0.039
GPT-6 Astra	High	86.4 %	US$0.044
GPT-6 Astra	Xhigh	86.5 %	US$0.051
GPT-6 Astra	Max	86.7 %	US$0.065
GPT-6 Luna	Low	73.5 %	US$0.0005
GPT-6 Luna	Medium	75.1 %	US$0.0006
GPT-6 Luna	High	76.5 %	US$0.0007
GPT-6 Luna	Xhigh	77.9 %	US$0.001
GPT-6 Luna	Max	79.9 %	US$0.0021
GPT-6.1 Sol	Low	84.0 %	US$0.0076
GPT-6.1 Sol	Medium	84.7 %	US$0.0079
GPT-6.1 Sol	High	85.8 %	US$0.0088
GPT-6.1 Sol	Xhigh	85.8 %	US$0.01
GPT-6.1 Sol	Max	86.3 %	US$0.013
GPT-6.1 Terra	Low	83.7 %	US$0.0038
GPT-6.1 Terra	Medium	84.6 %	US$0.0041
GPT-6.1 Terra	High	85.0 %	US$0.0045
GPT-6.1 Terra	Xhigh	85.4 %	US$0.0055
GPT-6.1 Terra	Max	85.5 %	US$0.012

OSWorld 2.1 · offline

Score: higher is better.

Desktop applications

Model	Effort	%	USD
Claude Sonnet 5.5	Low	57.8 %	US$0.68
Claude Sonnet 5.5	Medium	65.9 %	US$0.93
Claude Sonnet 5.5	High	73.3 %	US$1.39
Claude Sonnet 5.5	Xhigh	81.0 %	US$2.22
Claude Sonnet 5.5	Max	83.9 %	US$5.74
GPT-5.6 Terra	Low	24.1 %	US$0.44
GPT-5.6 Terra	Medium	27.4 %	US$0.59
GPT-5.6 Terra	High	37.7 %	US$1.19
GPT-5.6 Terra	Xhigh	48.4 %	US$2.25
GPT-5.6 Terra	Max	57.1 %	US$4.76
GPT-6 Astra	Low	63.3 %	US$2.32
GPT-6 Astra	Medium	75.4 %	US$4.37
GPT-6 Astra	High	78.3 %	US$5.76
GPT-6 Astra	Xhigh	79.0 %	US$6.60
GPT-6 Astra	Max	80.9 %	US$8.17
GPT-6 Luna	Low	7.5 %	US$0.02
GPT-6 Luna	Medium	26.3 %	US$0.079
GPT-6 Luna	High	39.7 %	US$0.14
GPT-6 Luna	Xhigh	44.6 %	US$0.19
GPT-6 Luna	Max	55.5 %	US$0.30
GPT-6.1 Sol	Low	60.9 %	US$0.38
GPT-6.1 Sol	Medium	73.5 %	US$0.65
GPT-6.1 Sol	High	75.5 %	US$0.90
GPT-6.1 Sol	Xhigh	75.9 %	US$0.95
GPT-6.1 Sol	Max	78.0 %	US$1.15
GPT-6.1 Terra	Low	53.8 %	US$0.36
GPT-6.1 Terra	Medium	66.7 %	US$0.50
GPT-6.1 Terra	High	68.7 %	US$0.51
GPT-6.1 Terra	Xhigh	71.3 %	US$0.62
GPT-6.1 Terra	Max	74.2 %	US$1.19

Factuality · difficult prompts

Error: lower is better.

Answers with any factual error

Model	Effort	%	USD
GPT-5.6 Terra	Low	53.6 %	US$0.055
GPT-5.6 Terra	Medium	51.5 %	US$0.061
GPT-5.6 Terra	High	41.1 %	US$0.078
GPT-5.6 Terra	Xhigh	33.9 %	US$0.11
GPT-5.6 Terra	Max	22.3 %	US$0.25
GPT-6 Astra	Low	13.3 %	US$0.26
GPT-6 Astra	Medium	9.7 %	US$0.29
GPT-6 Astra	High	5.9 %	US$0.43
GPT-6 Astra	Xhigh	4.6 %	US$0.55
GPT-6 Astra	Max	4.2 %	US$0.66
GPT-6 Luna	Low	44.3 %	US$0.0025
GPT-6 Luna	Medium	31.2 %	US$0.0035
GPT-6 Luna	High	27.6 %	US$0.0047
GPT-6 Luna	Xhigh	24.5 %	US$0.0061
GPT-6 Luna	Max	18.4 %	US$0.013
GPT-6.1 Sol	Low	14.7 %	US$0.048
GPT-6.1 Sol	Medium	12.0 %	US$0.056
GPT-6.1 Sol	High	6.6 %	US$0.079
GPT-6.1 Sol	Xhigh	5.5 %	US$0.099
GPT-6.1 Sol	Max	4.4 %	US$0.12
GPT-6.1 Terra	Low	16.7 %	US$0.024
GPT-6.1 Terra	Medium	10.1 %	US$0.029
GPT-6.1 Terra	High	7.7 %	US$0.036
GPT-6.1 Terra	Xhigh	5.0 %	US$0.05
GPT-6.1 Terra	Max	4.3 %	US$0.12

Terminal-Bench 4.0

Score: higher is better.

Coding tasks in the terminal

Model	Effort	%	USD
Claude Sonnet 5.5	Low	20.0 %	US$0.62
Claude Sonnet 5.5	Medium	28.8 %	US$0.68
Claude Sonnet 5.5	High	43.0 %	US$1.46
Claude Sonnet 5.5	Xhigh	61.5 %	US$4.34
Claude Sonnet 5.5	Max	70.6 %	US$10.44
GPT-5.6 Terra	Max	21.5 %	US$5.25
GPT-6 Astra	Low	50.6 %	US$4.93
GPT-6 Astra	Medium	54.4 %	US$6.06
GPT-6 Astra	High	58.1 %	US$7.18
GPT-6 Astra	Xhigh	57.9 %	US$7.45
GPT-6 Astra	Max	58.2 %	US$10.34
GPT-6.1 Sol	Low	47.1 %	US$0.76
GPT-6.1 Sol	Medium	52.7 %	US$1.02
GPT-6.1 Sol	High	53.0 %	US$1.28
GPT-6.1 Sol	Xhigh	53.4 %	US$1.28
GPT-6.1 Sol	Max	55.9 %	US$1.98
GPT-6.1 Terra	Low	24.8 %	US$0.24
GPT-6.1 Terra	Medium	35.8 %	US$0.46
GPT-6.1 Terra	High	41.8 %	US$0.65
GPT-6.1 Terra	Xhigh	46.4 %	US$0.99
GPT-6.1 Terra	Max	50.3 %	US$1.84

Terminal-Bench-Science 0.1

Score: higher is better.

Scientific research using code

Model	Effort	%	USD
Claude Opus 5.5 w/ fallbacks	Max	63.3 %	US$23.21
GPT-6 Astra	Low	55.4 %	US$11.41
GPT-6 Astra	Medium	57.4 %	US$12.34
GPT-6 Astra	High	62.0 %	US$14.95
GPT-6 Astra	Xhigh	60.9 %	US$15.76
GPT-6 Astra	Max	68.1 %	US$23.80
GPT-6.1 Sol	Low	43.7 %	US$1.79
GPT-6.1 Sol	Medium	47.6 %	US$2.34
GPT-6.1 Sol	High	51.1 %	US$2.76
GPT-6.1 Sol	Xhigh	53.7 %	US$2.89
GPT-6.1 Sol	Max	57.0 %	US$5.47
GPT-6.1 Terra	Low	24.3 %	US$0.47
GPT-6.1 Terra	Medium	34.3 %	US$0.95
GPT-6.1 Terra	High	39.7 %	US$1.24
GPT-6.1 Terra	Xhigh	43.4 %	US$1.72
GPT-6.1 Terra	Max	58.3 %	US$4.15

At Medium on Agents’ Last Exam, Terra and Astra both score 57.6%. Their published costs are US$0.29 and US$4.82, respectively. Matching this result does not make the models interchangeable for every assignment.

More reasoning does not always improve a result, either: Terra goes from 57.6% at Medium to 57.2% at High on this test. The chart preserves that dip. A point’s cost is not a fixed tariff you can apply to any task of your own.

Where other models still lead

Switching tabs changes the picture. All these figures are at Max, within the named test:

• Vision, MMMU-Pro: Terra scores 85.5%, Sol 86.3% and Astra 86.7%. Terra is close, but it does not lead this comparison.
• Desktop work, OSWorld 2.1 offline: Terra reaches 74.2%, versus Sol’s 78% and Sonnet 5.5’s 83.9%. The dataset name matters when comparing these figures with other OSWorld rankings.
• Coding, Terminal-Bench 4.0: Terra scores 50.3%, versus 21.5% for Terra 5.6. Sol reaches 55.9% and Sonnet 5.5 70.6%. If difficult coding is your priority, inspect this tab before choosing from the first chart.
• Scientific work with code: Terra scores 58.3% at US$4.15, ahead of Sol’s 57% at US$5.47. The Opus result uses fallbacks: 63.3% at US$23.21. That is a five-point gap, and the additional configuration is part of the comparison.

HeyMark has not run these tests. These are results published by OpenAI; every point and full model label is available in the table. The launch also reports task completion more than three times faster than Astra and Sol, but individual timings cannot be inferred from the USD values on the charts.

Where is Opus 5.5? Beyond the launch curves

Opus appears in two of OpenAI’s six charts. The Claude competitor in OSWorld and Terminal-Bench 4.0 is Sonnet 5.5; vision and factual errors include no Claude model. In the chart specifications, OpenAI assigns Claude grey in the original, alongside green for Terra and blue for other GPTs. We give it its own color and a visible label in both themes.

Does the selection help sell the story of a cheap, competitive Terra? That is an editorial hypothesis, not demonstrated intent. Available runs or configuration differences could also affect the selection. We found no public explanation in the announcement for each omission.

External Opus results do exist for vision, desktop use and coding. Explore them below. We did not find an equivalent public Opus run for the internal factual-error test.

The launch selection

2 / 6charts include Opus 5.5

• Agents’ Last Exam v1Opus included
• MMMU-ProNo Claude
• OSWorld 2.1 · offlineSonnet, no Opus
• Factual errorsNo Claude
• Terminal-Bench 4.0Sonnet, no Opus
• Terminal-Bench-Science 0.1Opus included

Here are the Opus results we found outside the launch. Pick a test and check who ran it.

MMMU-Pro · Artificial Analysis

87.7%

Artificial Analysis reports 87.69% (87.7% rounded) on MMMU-Pro for Opus 5.5 Max, Default Fallback. We did not find Terra 6.1 in its list at our cutoff. This is not a matched run against the 85.5% in OpenAI’s curve.

View independent evaluation ↗

OSWorld · Anthropic

Partial

81.8%

Strict

48.7%

Anthropic reports 81.8% partial for Opus 5.5 Max; its system card also gives 48.7% strict. These are different metrics. The card describes 108 tasks and five runs, labeling its section OSWorld 2.0 while the launch says 2.1. Its setup is not equivalent to OpenAI’s offline set.

Launch · System card ↗

Terminal-Bench 4.0 · Opus 5.5

Four published Opus results, four configurations. We do not average them: agent, effort, repetitions and fallbacks can change the result. We did not find Terra 6.1 evaluated alongside Opus under the same protocol in these sources.

Artificial Analysis ↗59.6%Max · Default Fallback · mini-swe-agent

Terminal-Bench ↗64.8% ±3.1 ppMax · Claude Code

Vals AI ↗65.15%Mini-SWE-agent · avg@3 · 24 Oct

Anthropic ↗66.4% ±2.6 ppXhigh · production safeguards / fallbacks

One source: Opus, Astra and Sol on Vals

Comparison within Vals’ leaderboard, updated October 24: Mini-SWE-agent, 66 tasks, three runs. Terra 6.1 is not listed yet. The table does not detail effort or fallback policy for the current Opus result.

Terminal-Bench 4.0 · Vals · 24 Oct 2026

Model	%	USD / test
Claude Opus 5.5	65.15%	$13.20
GPT-6 Astra	59.60%	$9.58
GPT-6.1 Sol	55.05%	$1.72

Vals’ earlier model page retains a different result: 61.62%, or 53.54% counting fallbacks as failures (30/198 assisted cases). We do not apply that adjustment to the current 65.15%: the source does not explain the difference. Earlier model page ↗

Missing from a chart does not mean missing a result. But bringing in a number from another website does not make a fair head-to-head. We found no independent run testing the new Terra against Opus with the same task set and configuration. That is why sources stay separate: Opus’ 87.7% vision result is not pasted onto Terra’s curve as though they sat the same exam.

What the factual-error reduction measures

The internal difficult-prompt evaluation measures answers containing any factual error. At Low, Terra 5.6 records 53.6% and Terra 6.1 16.7%. At Max, the new model falls to 4.3%, versus Astra’s 4.2% and Sol’s 4.4%.

These are error rates: lower is better. They do not mean Terra gets 4.3% of everything anyone asks it wrong. When preparing content, it still makes sense to check names, dates and claims against their sources.

API pricing: US$1 input and US$5 output

The official API model page identifies it as gpt-6.1-terra. Standard rates are in USD per one million tokens:

For illustration, 10,000 input tokens and 2,000 output tokens cost US$0.02 at standard input/output rates: (10,000 × 1 + 2,000 × 5) / 1,000,000. This is not a forecast of a task’s token use and excludes tools, fast modes and additional cache charges.

Long context has an important condition: above 272,000 input tokens, the entire request is charged at 2× input/cache rates and 1.5× output rates. The docs list a 1,050,000-token API context window and up to 128,000 output tokens. Those figures do not establish the limits inside every Work or Codex experience.

Where it is available at launch

OpenAI announces Terra in ChatGPT Work and Codex for Plus, Pro, Enterprise and Edu, as well as the API. That does not confirm availability in the regular ChatGPT model picker. The announcement says the same usage allowance covers more tasks with Terra, without publishing a numerical quota we can turn into messages or assignments.

Original ChatGPT announcement: open the launch post on X. The numbers and conditions in this story come from OpenAI’s full launch announcement and API documentation.

For a particular workflow, look at the test closest to your work: operating apps, interpreting images and programming measure different things. The system-card addendum also explains that research/API evaluations can differ from production because of tools, instructions and effort. Cost gives you a reason to consider Terra; the differences between tasks help you decide when to choose it.