GPT-6.1 Terra: Capabilities, Parameters, Pricing, and Model Comparison

GPT-6.1 Terra is the GPT-6 family model aimed at the practical middle ground: fast visual reasoning, precise computer use, and clear writing at a lower token price than GPT-6.1 Sol and GPT-6 Astra. The official model page lists a 1.05M-token context window, 922K maximum input, 128K maximum output, text and image input, and six reasoning settings from none through max.

This article answers the questions developers usually ask first: what GPT-6.1 Terra can do, which parameters matter in production, how its price compares with nearby models, where it has an advantage, and when another model is a better choice. The specifications and prices below are from the official OpenAI model and pricing pages checked on 27 October 2026. OpenAI does not publish Terra's parameter count, training-compute total, or a neutral cross-provider benchmark table, so this guide does not invent them.

You can call Terra through an OpenAI-compatible gateway such as Modelflare with the model ID gpt-6.1-terra. Disclosure: I work on Modelflare. The canonical guide lives on modelflare.dev.

GPT-6.1 Terra at a glance

The endpoint distinction matters. GPT-6.1 Terra supports Chat Completions, but OpenAI says tool calling requires the Responses API. A migration that only changes the model string can therefore pass a text-only smoke test while failing when the application invokes a tool.

A million-token context is a working budget, not a promise of perfect recall

The 1.05M context window is useful for source-heavy tasks: a product catalog, a long technical drawing set, a collection of screenshots, or a multi-file change request can stay in one request. The 922K maximum input leaves room for the model's reasoning and response. It does not guarantee that every detail will be equally salient. Production prompts should still label source priority, preserve identifiers, and ask for explicit uncertainty when an image or scan is difficult to read.

OpenAI's pricing page defines short context as up to 272K input tokens. Above that threshold, Terra's standard rates become $2 per million input tokens and $7.50 per million output tokens for the full request. A 400K-input request with 20K output therefore has a token estimate of about $0.95 before tools: 400K × $2 plus 20K × $7.50, divided by one million. This threshold is easy to miss in a cost model that only stores one input rate.

Reasoning effort is a latency and quality control

Terra is unusual in the current flagship group because it supports none as well as low, medium, high, xhigh, and max. Start at medium for visual interpretation and computer-use plans. Use low or none for deterministic rewrites, extraction, and short transformations where extra deliberation is not buying measurable quality. Move to high or xhigh when the task contains competing constraints, ambiguous screen state, or a long chain of actions. max is for cases where correctness is worth more time and token use.

Reasoning effort is not a benchmark score. It is a request control. Compare settings on representative tasks, recording success rate, retries, latency, and total tokens rather than assuming that the highest setting is always best.

Vision and computer use are the differentiating combination

OpenAI positions Terra for matching product photos to catalog entries, comparing technical drawings, acting on application screens, and drafting or revising text. That combination favors workflows where the model must interpret a visual state and then produce a precise textual or tool action. Examples include checking a dashboard screenshot against an expected KPI, filling a form from a source document, triaging a UI bug, or normalizing a supplier lookbook into catalog rows.

The limitation is operational: computer use is an action surface. Add confirmation gates for destructive clicks, keep a browser or desktop session observable, and require the model to restate the target record before a write. A strong screen interpretation does not remove the need for application authorization and audit logs.

GPT-6.1 Terra compared with nearby models

The comparison is about published contracts and workload fit, not a claim that one provider wins every benchmark. The official Terra page does not publish a parameter count or a cross-provider score. A fair evaluation should use the same images, screen states, acceptance checks, reasoning setting, and tool budget for every candidate.

Where GPT-6.1 Terra has a clear advantage

Visual-to-action workflows. Terra is a good default when the input is a screenshot, diagram, or image and the output is a concise explanation or a bounded computer action. It connects visual interpretation with writing and tool use without paying Sol or Astra's standard token rates.

Cost-sensitive multimodal agents. At $1/$5 per million short-context input/output tokens, Terra is half the price of Sol and one tenth the input price of Astra. Cached input is $0.05 per million, and Batch or Flex prices are 50% of Standard. These differences matter when an agent repeatedly reads the same catalog, policy, or UI context.

Reasoning control. Six effort levels make it easier to route quick edits and difficult screen plans through one model ID. Teams can start with medium, then raise effort only for cases that fail a quality check.

Clear deliverables. OpenAI describes Terra's writing as clear and natural. That is useful for proposals, incident notes, product copy, and explanations that must put the result first and keep supporting detail readable.

When Terra is not the best choice

Choose Astra when a task is an unusually difficult end-to-end research, coding, or professional workflow and the cost of a wrong decision dominates token price. Choose Sol when the workload is a long-running complex agent and you need a tier positioned closer to Astra. Choose Luna for high-volume extraction, classification, and structured transformations with limited visual judgment.

Claude Sonnet 5.5 is a credible alternative when the existing stack depends on Claude's adaptive thinking or provider integrations. Gemini 3.8 Flash can be attractive for long-horizon software and agent workloads while its introductory price is active, but its published maximum output is 64K rather than Terra's 128K. Check expiry dates before putting an introductory rate into a long-term budget.

Do not choose Terra solely because it has a million-token context. If the workflow needs audio output, a provider-specific tool, a regional residency guarantee, or a tested framework integration, those constraints should decide the shortlist first.

A practical evaluation protocol

1. Build a 30–50 task set from real screenshots, documents, and screen workflows. Keep private data redacted and preserve the same task order across models.
2. Test none, medium, and high on Terra. Record task success, correction count, latency, input tokens, output tokens, and tool calls.
3. Compare Terra with the intended alternatives using the same acceptance checks. Do not compare a Terra screenshot task with another model's text-only task.
4. Add a cost sheet with the 272K long-context threshold, cache reads, cache writes, Batch/Flex discounts, and tool-call fees.
5. Roll out with bounded permissions. Require confirmation before irreversible computer actions and keep a fallback model for failure recovery.

Is GPT-6.1 Terra a reasoning model?

Yes. It supports six reasoning.effort values: none, low, medium, high, xhigh, and max. medium is the documented default.

How large is GPT-6.1 Terra's context window?

The published context window is 1,050,000 tokens. The maximum input is 922,000 tokens and the maximum output is 128,000 tokens.

What is the GPT-6.1 Terra API price?

Standard short-context pricing is $1 per million input tokens and $5 per million output tokens. Cached input is $0.05 per million. Above 272K input tokens, the published long-context rates are $2 input and $7.50 output per million tokens.

Does GPT-6.1 Terra support function calling?

Yes. The model page lists function calling and structured outputs. OpenAI states that tool calling requires the Responses API.

How many parameters does GPT-6.1 Terra have?

OpenAI has not published a verified parameter count on the model page reviewed for this article. Treat third-party estimates as estimates, not as model specifications.

Is GPT-6.1 Terra better than GPT-6.1 Sol?

Neither is universally better. Terra is cheaper and tuned for fast visual reasoning, computer use, and clear writing. Sol is the higher-priced option for complex, repeated work positioned nearer Astra. Test the exact workflow before switching.

Sources and update policy

• OpenAI GPT-6.1 Terra model page
• OpenAI GPT-6 guide
• OpenAI API pricing
• OpenAI model selection guide
• Claude Sonnet 5.5 model page
• Gemini 3.8 Flash pricing

Prices, availability, limits, and model snapshots can change. Recheck the provider pages and Modelflare pricing before making a production routing or budget decision.

Business inquiries: business@modelflare.dev