GPT-6.1 Terra vs GPT-6 Astra: Which OpenAI Model Fits Your Project?

OpenAI reports that GPT-6.1 Terra performs on par with GPT-6 Astra on Agents’ Last Exam, while its standard input and output token rates are one-tenth as high. Astra is designed for difficult tasks that require deeper reasoning. Terra is built to handle more tasks at a lower cost and higher speed. OpenAI released Astra on September 4, 2026 to all, followed by Terra on October 26, 2026.

This guide looks at what each model does well, where it falls short, and what you’ll pay to use it. You’ll also learn which model fits your work and how to test both before deciding.

What each model is built for

GPT-6 Astra: the flagship for hard, long runs

Astra is OpenAI’s most powerful model, designed for difficult tasks that need several steps to complete. It handles complex problems, coding, computer tasks, and research.

Astra works well for multi-step tasks, where one mistake can affect everything that follows. For example, building a full app or changing code across several files without someone checking each step.

GPT-6.1 Terra: the fast, low-cost workhorse

Terra is OpenAI’s faster, lower-cost model for everyday computer tasks. It can understand what’s on the screen, use a computer accurately, and write more clearly. It also improves on GPT-5.6 Terra.

OpenAI aims it at the tasks that fill a workday:

• Drafting proposals
• Interpreting technical drawings
• Filling out forms
• Editing videos
• Reading incoming invoices and entering the details into accounting software

GPT-6.1 Terra vs GPT-6 Astra: specs and pricing

Terra’s standard API input and output rates are one-tenth of Astra’s; all prices below are in USD per million tokens. OpenAI’s pricing page lists the rates for both models. The table below uses the Standard rates for short inputs.

Astra’s context window, output cap, cutoff and effort levels come from its model page. Terra’s figures come from OpenAI’s launch materials.

Terra’s standard rates sit at one-tenth of Astra’s.

Cached input is 20 times cheaper on Terra: $0.05 against $1 per million tokens. Terra also costs half as much as GPT-6.1 Sol per token.

GPT-6 Astra and GPT-6.1 Terra pricing: what the gap costs

The 10-to-1 gap holds across all three workloads below at standard rates, without caching.

The token counts are examples.

For caching, Batch, Flex, and Fast mode rates, see our GPT-6.1 Terra pricing guide.

These estimates use the standard API rates for short inputs. They include all charged output tokens, including reasoning tokens, but exclude caching, tools, retries, and other costs.

Fast mode, batch, long prompts and data residency

Four settings change the bill. The pricing page shows each one through its toggles.

• Fast mode doubles the standard rate. Astra costs $20 input and $100 output. Terra costs $2 and $10.
• Flex and Batch carry lower rates for both models. They suit work that does not need an instant answer.
• Long context prompts cost more. The page’s Long toggle shows the exact rates.
• Data residency and FedRAMP endpoints add a 10% uplift.

Reasoning tokens and effort

Reasoning tokens count as output tokens, so higher effort can increase the cost. Compare the cost of completing each task successfully, not just the per-token price.

For a task with 20,000 input tokens and 5,000 output tokens, Astra costs $0.45 per attempt, while Terra costs $0.045.

Terra remains cheaper as long as it completes the task in fewer than 10 attempts. Higher effort uses more reasoning tokens, which can increase the cost and reduce those savings.

Speed and computer use

On the computer-use tasks reported in OpenAI’s Terra launch post, Terra completed work over three times faster than Astra; this does not guarantee the same speedup on your workload.

• Agents’ Last Exam: This benchmark covers professional tasks, such as turning architectural drawings into a 3D model. At medium effort, Terra scores 15 points higher than GPT-5.6 Terra at about one-third the cost.
• Same benchmark, against Astra: Terra performs on par with Astra at one-tenth its price per token.
• Speed: Terra completes tasks over three times faster than both Astra and GPT-6.1 Sol.
• MMMU-Pro: Terra beats GPT-5.6 Terra at every effort setting. It nearly matches Astra and GPT-6.1 Sol on this visual reasoning test.
• OSWorld 2.1: At medium effort, Terra passes GPT-5.6 Terra’s best score at a fraction of the cost per task.

Astra’s computer-use case rests on OSWorld 2.0. OpenAI says Astra scored higher than GPT-5.6 Sol in about 47% less time per task. Terra’s result uses version 2.1, so the two scores do not compare directly.

Form filling, invoice entry, and video editing are tasks people do every day. Finishing them three times faster means clearing your work queue three times sooner.

Coding performance

Astra leads Terra on coding by 7.6 points. Terminal-Bench 4.0 tests coding agents on command-line problems, such as fixing a webpage whose layout shifts as it loads.

Terra more than doubled its predecessor’s score. OpenAI still tells developers to choose GPT-6.1 Sol or Astra for the most complex coding tasks.

Terra does well on science-style coding, though. On Terminal-Bench-Science 0.1, Terra at max effort beats GPT-6.1 Sol at lower cost.

A practical way to split the work is:

• Use Terra for smaller coding tasks, such as fixing bugs, writing scripts, and making small changes to the interface.
• Use Astra when the work involves several files, major code changes, or long tasks that run without someone checking each step.

These results show that Astra is better for difficult coding tasks. But they don't prove it will work better with your code. Test both models on changes that involve several files in your project before choosing one.

For a score-by-score look, see our GPT-6.1 Terra benchmarks guide.

Accuracy and hallucinations

Terra’s error rate fell at both effort levels OpenAI reported. OpenAI measured it on an internal benchmark of error-inducing prompts:

• At low effort, the error rate fell from 53.6% with GPT-5.6 Terra to 16.7% with Terra.
• At max effort, the error rate fell 18 points to 4.3%. That nearly matches Astra and GPT-6.1 Sol.

These figures come from an internal test designed to trigger errors, not a measurement of error rates across ordinary customer workloads.

The effort setting moves Terra’s error rate more than the model gap does: 16.7% at low against 4.3% at max.

For customer-facing content, use a higher effort setting in Terra and review the text before it goes live.

Safety and cybersecurity

OpenAI classifies Terra as having Critical-level cybersecurity capabilities and applies safeguards developed for Astra. This classification describes capability—not a guarantee of safe operation.

OpenAI says Terra stays within authorized scope and explains its limits more reliably than GPT-5.6 Terra. It also showed strong resistance in OpenAI’s prompt-injection tests.

OpenAI’s Astra system card says that, with suitable tools and access, the model can discover previously unknown security flaws and develop exploits. OpenAI also monitors externally deployed tool-using inference.

For both models, follow these safety steps:

• Give the model only the access it needs for the task.
• Keep a record of every action it takes.
• Check all changes before releasing them.

Access, availability and limits

GPT-6 Astra

Astra is available through the OpenAI API and Amazon Bedrock; ChatGPT access depends on the plan and product surface, so check current eligibility.

The API ID is gpt-6-astra. OpenAI sells cheaper siblings in the GPT-6 family, including GPT-6 Sol and GPT-6 Luna. Astra is the top-priced tier.

GPT-6.1 Terra

Terra is available to Plus, Pro, Enterprise, and Edu users in ChatGPT Work and Codex. Developers call it through the OpenAI API as gpt-6.1-terra.

OpenAI says you can select Terra in Work or Codex as the rollout reaches your account. If you can’t find Terra in your account, check whether your plan supports it and review your workspace settings.

In ChatGPT Work and Codex, the same usage allowance covers more tasks with Terra than with GPT-5.6 Terra. It covers substantially more than with GPT-6.1 Sol or Astra.

The launch post lists no context window, output cap, or parameter limits. Check the API documentation before you migrate code.

Where the other GPT-6 models fit

Two cheaper GPT-6 tiers sit below Astra. OpenAI’s pricing page lists them at standard rates.

Luna’s standard input and output token rates are one-tenth of Terra’s. OpenAI includes Luna in its Agents’ Last Exam v1 comparison. Test it on representative tasks before using it in production.

Sol costs twice as much as Terra per token. Compare both on your own tasks before you pay for the higher tier.

Choose Astra for difficult tasks that cheaper models may get wrong, which can lead to repeated attempts. Choose Terra when you need to handle many tasks quickly and keep costs low.

Also read: GPT-6.1 Terra vs GPT-6.1 Sol, a comparison of how the two tiers split on price, coding, and computer use.

How to read these benchmarks

Most figures in this guide come from OpenAI’s own launch materials. They show what OpenAI reports, but they do not guarantee the same results for your work.

• Match effort levels before you compare. A low-effort model and a max-effort model are different tests.
• Match benchmark versions. OSWorld 2.0 and 2.1 are not the same exam.
• Test on your own tasks. Twenty to fifty real examples tell you more than any leaderboard.

A 30-minute test you can run

A short test on your own tasks beats any public score.

• Select 20 tasks from your current workload.
• Set the maximum number of errors you can allow.
• Test each task using Terra and Astra at medium effort.
• For each test, note the number of errors and retries, the time taken, and the cost.
• Run the same tasks again using Terra at the highest effort level.
• Choose the cheapest option that keeps errors within your limit.

If Terra at the highest effort level meets your error limit, you can save money with its lower token rate. If it doesn't, you have test results to support choosing Astra.

Which model should you pick?

Pick Terra for volume and routine steps. Pick Astra for the hardest and longest work.

Three example setups

• Automating office tasks: Use Terra for invoices, forms, and email drafts. The monthly cost stays close to $100 for the workload described above.
• Building a product alone: Use Astra to build the main product and fix difficult bugs. Use Terra for writing, small changes, and testing.
• Agency with many clients: Terra can handle everyday client work, while Astra suits portals with complex features.

Conclusion:

Choose by task, then build

Use Terra for everyday tasks and large workloads. Choose Astra if testing shows it delivers better results or finishes tasks fast enough to justify the higher cost.

Key Takeaways:

• Use Terra for regular tasks when you want to save time and money.
• Use Astra for difficult tasks if your tests show that it performs better.
• Compare the full cost of each successful task, including reasoning tokens and repeated attempts.

When you’re ready to build, Emergent offers GPT-6 Astra for creating full-stack applications from plain-language instructions. Start building on Emergent.