MyTechDailyNews Software Guide: GPT-6.1 Terra capabilities, pricing, context, and endpoints

If a software team wants a compact picture of GPT-6.1 Terra, the listing centers on five practical axes: what it can accept, what it returns, how far it reasons, how fast it is, and how much it costs. The entry model id is gpt-6.1-terra, and the trial path mentioned is a Playground comparison rather than a long narrative.

Software-fit snapshot

Reasoning tier	Highest
Speed profile	Fast
Listed price markers	$1 • $5
Accepted input	Text, Image
Produced output	Text
Model id	gpt-6.1-terra

The same overview frames the model around visual inference, precise computer interaction, and clear natural-language writing, while positioning it as a fast and efficient option for comparison work.

Workloads called out for selection

• Match product photographs to catalog entries.
• Read technical drawings and extract text from them.
• Operate application screens as part of a software workflow.
• Create or revise ordinary text.

From a software standpoint, those examples describe an image-aware text assistant rather than a media producer. The listing gives enough detail to test whether screen or document understanding matters more than output type.

Reasoning controls and tool-call dependency

For reasoning.effort, the listed values are none, low, medium, high, xhigh, and max, with medium marked as default. The overview also states that the Responses API is required for tool calling. Prompting and migration guidance is pointed to the GPT-6 guide.

Those two notes matter during implementation. If a team only needs conversational text generation, the endpoint question may be simple. If a team plans tool use, the endpoint constraint becomes part of architecture review rather than an afterthought.

Context, output ceiling, and knowledge boundary

• The stated limit is a 1,050,000 context window.
• The maximum generated length is 128,000 max output tokens.
• The knowledge cutoff is listed as Jun 01, 2026.
• Reasoning token support is noted in the limits section.

The knowledge boundary is especially relevant for software readers who expect model answers to reflect a fixed information horizon. The context and output ceilings can be read together when estimating whether a workflow can hold large documents or produce long generated text without external chunking.

Text-token cost table and billing notes

Costs depend on token counts or other metrics tied to the model type. Tool-specific models such as search and computer use also incur a fee per tool call; the pricing page has details.

Text token line	Rate per 1M tokens
Input	$1.00
Cached input	$0.05
Cache writes	$1.25
Output	$5.00

The cached input line is described as 5% of the uncached input token rate. Because output is priced higher than input, generated text length is likely to influence cost more than prompt length alone in simple comparisons.

Standard cache-write retention notes

The cache-write amounts in the table correspond to 30-minute retention. For Standard cache writes, the listing says $1.25 per million tokens for 30-minute retention and $2.50 for 12-hour retention before long-context adjustments are applied.

This distinction can affect software teams that expect repeated prompts or reusable context. The shorter retention case is shown as the main table value, while the longer retention case is separated with a higher figure.

Long context, batch, regional, and fast-mode adjustments

1. If a prompt exceeds 272K input tokens, the entire request is billed at 2x input and cache rates and 1.5x output.
2. Batch pricing and Flex pricing are 50% lower than Standard.
3. Where regional processing is available, it adds a 10% premium.
4. Fast mode costs 2x Standard.

Cost situation	Rule from the listing
Standard text work	$1.00 input and $5.00 output per 1M tokens, with $0.05 cached input and $1.25 cache writes in the table.
Cache retention	$1.25 per million tokens for 30-minute retention; $2.50 for 12-hour retention before long-context adjustments.
Prompt over 272K input tokens	Full request at 2x input and cache rates and 1.5x output.
Batch or Flex	50% lower than Standard.
Regional processing	10% premium where available.
Fast mode	2x Standard.

Modality and endpoint matrix

Modality	Software-facing support
Text	Input and output
Image	Input only
Audio	Not supported
Video	Not supported

Endpoint	Path
Live	v1/live/sessions
Chat Completions	v1/chat/completions
Responses	v1/responses
Realtime	v1/realtime

For editors comparing integration options, the endpoint table gives the path names without promising any capability beyond the listed modalities. Text remains the only supported output form, while image enters as an input signal.

Step-by-step evaluation for software users

1. Start from the modality matrix: use the model only when text output is acceptable and image input is enough.
2. Test a representative task in Playground, using the listed comparison path.
3. Set reasoning.effort from the supported values and note whether medium is sufficient.
4. If the plan includes tool calling, move the check to the Responses API.
5. Before estimating cost, identify whether the request may cross 272K input tokens.
6. Compare the Standard baseline against Batch, Flex, Fast mode, and any regional processing add-on.
7. Use the GPT-6 guide when migrating prompts or changing reasoning settings.

FAQ

Can it output images, audio, or video? No. The text table shows Text as input and output, Image as input only, while Audio and Video are not supported.

Does the cached input price apply automatically? The listed relationship is that cached input is 5% of the uncached input token rate; cache writes are a separate line and depend on retention.

What happens if a prompt is very long? Once it exceeds 272K input tokens, the full request uses 2x input and cache rates and 1.5x output.

Is there a cheaper execution class than Standard? The page says Batch and Flex are 50% lower than Standard.

Which endpoint should a tool-using integration target? The overview states that the Responses API is required for tool calling.

Common misconceptions

• Cached input is not the same concept as a cache write: the first is priced at $0.05 per 1M tokens, while Standard cache writes are tied to retention.
• Fast mode is not a cost-neutral speed switch: it is listed as 2x Standard.
• A regional option is not free: where available, it adds a 10% premium.
• The long-context rule is not limited to the overflowing portion: the full request is billed under the multiplier rule above.
• Tool calling is not described as free of constraints: the Responses API is stated as required.

Actionable recommendations

• For catalog, drawing, UI-screen, or text-revision tasks, treat this as a candidate and run a Playground comparison first.
• For developer integrations that require tools, make the Responses API part of the proof of concept from the start.
• For budget-sensitive workloads, model the request around Standard pricing first, then apply Batch or Flex where the 50% lower statement applies.
• For latency experiments, account for the explicit Fast mode cost, 2x Standard.
• For data-residency needs, add the 10% premium into comparison tables only when regional processing is available.
• For prompt migration, follow the GPT-6 guide and keep reasoning.effort changes documented alongside cost impact.

Data citation block

The decision points above come from the listing's capability table, pricing table, modality table, endpoint table, and limit notes. The key identifiers used are GPT-6.1 Terra and gpt-6.1-terra; the price markers are $1 and $5; the context ceiling is a 1,050,000 context window; the output ceiling is 128,000 max output tokens; the knowledge date is Jun 01, 2026.

For a software column, the useful takeaway is not that every table changes, but where the tables intersect: input modality, reasoning controls, tool-call endpoint, and cost multipliers. Those are the four places where a selection decision can be made without adding speculation.