Choosing an OpenAI model: GPT-6 Astra vs. GPT-5.6 Sol, Terra, and Luna

Evaluate GPT-6 Astra first for complex, lengthy tasks involving a sequence of tool operations. Start with Terra for routine work, compare Sol for harder work on a limited budget, and try Luna first for high-volume processing with clear rules.

These are starting points based on the official positioning, not a performance ranking for every task. Ultimately, compare accuracy, elapsed time, and total cost for completing the same work.

The information was checked on September 10, 2026, against OpenAI’s model catalog, API documentation, and the GPT-6 Astra announcement. The workload assignments and evaluation plan are practical suggestions; official demonstrations are not presented as tests conducted by this site.

How the four models are positioned

This article focuses on general-purpose models for text, code, and tool calling. Image generation, realtime speech, and transcription have dedicated models that do not belong in the same general capability ranking.

OpenAI currently recommends starting with Astra when unsure, choosing Terra to balance capability and cost, and Luna for high-volume needs. The specific workload assignments above are suggestions based on those roles. Source: model catalog

Developers can first use Astra to establish the quality achievable on a task, then compare less expensive models on the same inputs. For an existing stable application, retaining the current model as the comparison point is more useful.

How Sol, Terra, and Luna relate to older names

Earlier product tiers help explain the GPT-5.6 family:

• Sol roughly corresponds to the main tier previously offered without a mini or nano suffix.
• Terra roughly corresponds to the earlier mini tier.
• Luna roughly corresponds to the earlier nano tier.

These are positioning analogies, not claims that behavior, pricing, limits, or quality are identical to older models.

Also, gpt-5.6 is an alias for Sol. Using gpt-5.6-sol in configuration makes the intended tier more explicit. Sources: Sol, Terra, Luna

What matters when comparing GPT-6 Astra with Sol

Astra’s announcement emphasizes computer use, long tasks, and complex software work. Users should test whether it can complete a sequence of steps and revise its approach when new information arrives midway.

The table below selects several comparisons from the announcement. These are evaluations published by OpenAI, not tests run for this article.

The gains vary substantially by task; they do not support a blanket claim that coding ability has doubled. The announcement says scores are the maximum across reasoning efforts, and evaluation environments may differ from production ChatGPT. Source: Astra announcement and evaluation notes

Evaluate long tasks across the complete workflow

For example, fixing an import failure may require reading logs, locating parsing code, changing the implementation, running checks, and explaining which data was affected.

Comparing only the final description of the fix cannot establish whether the model found the right files, preserved existing behavior, or missed the input that actually failed.

Keep the full task trace when comparing Astra and Sol, particularly recording:

• Whether the correct source of the problem was identified.
• Whether the approach changed after a failure.
• Whether necessary validation was completed.
• Whether unfinished work was accurately reported.

New API capabilities need application support

Astra’s documentation lists asynchronous tool calling, additional instructions while work is underway, and changes to reasoning effort during a conversation while preserving the cached prefix.

Asynchronous tool calling lets the model handle independent work before a tool returns, but the application still manages tool execution and pending calls. Mid-turn steering uses the Responses API WebSocket flow and also requires client support. Source: Astra guide

An application that sends a single text request and waits for an answer does not automatically gain a complete asynchronous task system by switching to Astra.

Current API prices: how far apart are the four tiers?

The following are the Standard short-context prices at the time of verification, in US dollars per million tokens. Cache reads and writes are listed separately so that input is not all estimated at one rate.

Sol currently has promotional pricing, which OpenAI says will remain available at least through November 21, 2026. Do not treat it as a permanent assumption in a long-term budget. Source: API pricing

For identical uncached input and output token counts, Astra’s rates for both are 2.5 times Sol’s. Terra’s input and output rates are both 10 times Luna’s.

Those ratios describe unit prices only. Models may use different amounts of reasoning and tool interaction, so actual task bills must be measured separately.

A per-request example with identical token usage

Assume a request contains 20,000 uncached input tokens and 4,000 billable output tokens, with no caching, tool charges, regional surcharges, or long-context premium.

The formula below calculates request cost as input tokens divided by 1,000,000 times the input rate, plus output tokens divided by 1,000,000 times the output rate. The original example is preserved:

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单次费用 = 输入 token / 1,000,000 × 输入单价
         + 输出 token / 1,000,000 × 输出单价

This is a calculation from the price table, not the measured cost of completing a real task. In particular, 4,000 billable output tokens should not be read as a visible answer of fixed length.

Running the same usage 1,000 times would cost $400, $160, $88, and $8.8 respectively. Whether that is inexpensive also depends on how many results pass acceptance checks.

Deciding whether Astra’s premium is worthwhile

Suppose a type of task costs $0.16 per attempt with Sol and $0.40 with Astra.

In this hypothetical example, if Sol requires an average of three complete attempts to deliver, its model cost reaches $0.48. Astra could be cheaper for that task if it completes it in one attempt.

Real retries often have different input and output sizes, so this example is not a fixed break-even rule. It illustrates why failures, retries, and manual edits must be counted when comparing delivery costs.

Formatting copy may need little manual correction. Complex code failures may consume more human debugging time than the token costs are worth. The two workloads do not need the same selection rule.

A million-token context does not always mean short-context pricing

The official model pages list these capacities for all four models:

Sources: Astra specifications, Sol specifications, Terra specifications, Luna specifications.

These are API model specifications, not promises about attachment or conversation allowances in every ChatGPT plan or interface.

Equal window capacity also does not guarantee equal comprehension of long documents. How much material fits and whether the model can accurately identify constraints, exceptions, and conflicts are separate questions to test.

What changes above 272K input tokens

The official specifications say that inputs exceeding 272K tokens use long-context rates for the entire request: 2 times the short-context input rate and 1.5 times the output rate.

The premium therefore does not apply only to the portion above the threshold. Caching must also use the applicable long-context rates. Sources: model specifications, full price table

Start with three design questions:

1. Must the entire document collection go into one request?
2. Can relevant sections be retrieved first while retaining their sources?
3. Are there relationships across sections that splitting would obscure?

Choose full-text input, retrieval, or segmented processing according to the task. Cutting input in a way that removes evidence can increase later rework.

Image input and image generation are different

All four general-purpose models can accept images and return text, for example to read screenshots, analyze charts, or explain page layouts.

The specification pages also list image-generation tool support. That means the relevant tool can be called; it does not make “text and images” the model’s native output modalities.

When purchasing image editing or realtime speech capabilities, check the dedicated models’ specifications and pricing rather than applying this article’s text cost example.

Choose reasoning effort and Fast mode separately

Reasoning effort affects the computation devoted to a problem; Fast mode is a request processing service tier. They should not be treated as one speed setting.

The GPT-5.6 model pages mark medium as the default. Astra does not support none, so check this explicitly when switching an older configuration. Sources: Astra guide, Sol specifications, Terra specifications, Luna specifications

Establish a comparison with explicit reasoning settings, then change one variable at a time. Changing the model, effort, and processing tier together makes cost and timing differences difficult to explain.

When Fast mode makes sense

The current Fast price table lists twice the corresponding Standard rates for these four models. Fast is currently unavailable for Astra requests with EU data residency, and Astra’s Fast mode has no latency SLA. Sources: pricing, Astra guide

Include Fast in your evaluation when a user is waiting for an important result. For background tasks, comparing ordinary processing with suitable batch options is a more useful starting point.

Do not assume the entire workflow will accelerate by a fixed multiple. Page loading, downloads, and external service responses may also account for substantial time.

Select a model for the work you actually do

The assignments below are suggestions for an initial trial. Adjust them whenever results on your own samples point elsewhere.

Writing, translation, and organizing information

Start with Terra for drafts with a clear structure, summaries, and general translation, then sample the results for terminology, omissions, and style.

Compare Sol and Astra on the same task when sources conflict, facts need cross-source checking, or a long article has many constraints.

Luna is worth testing first for batch processing with established rules, such as extracting titles, assigning category labels, and standardizing fields.

Distinguish fluent writing from correct evidence. Natural language does not replace source verification.

Programming, debugging, and repository changes

Evaluate Terra first for small function changes, common scripts, and explanations of tests.

For changes across files, complex dependencies, or hard-to-reproduce failures, compare Sol and Astra on the complete delivered result.

Beyond test results, inspect whether the diff meets the request, introduces unrelated changes, or is accompanied by claims that checks were run when they were not.

If your Sol workflow is already stable, test Astra on a set of historically difficult tasks before expanding its use.

Browser and computer use

These tasks often combine visual recognition, state assessment, and sequences of actions, making Astra a useful early candidate.

Use the same initial page and permissions in each evaluation, and record the actual final state. Check whether a file was saved, a table updated, and the result can be reopened.

A model saying an operation succeeded is not sufficient proof of completion. Tool environments, application permissions, and page changes also affect the outcome.

Classification, extraction, and background batch tasks

Give Luna explicit fields, a few representative examples, and rules for null values and unusual inputs.

Routing items that fail format or business validation to Terra or Sol for review is one possible experimental routing strategy.

Escalation should be triggered by checkable errors, such as missing fields, conflicting value ranges, or insufficient evidence. Do not rely solely on the model’s self-reported confidence.

A practical model evaluation worksheet

A first sample of 20–50 real tasks is closer to actual needs than repeatedly asking one brainteaser. This sample size is for initial screening; it cannot establish that rare errors have disappeared.

Include everyday requests, historical failures, and a few edge cases, with acceptance criteria written in advance.

Acceptance checks for different tasks

• Field extraction: compare against human labels, counting missing and incorrect fields separately.
• Translation: check negation, numbers, units, proper names, and missing paragraphs.
• Code changes: inspect the actual diff and run validation relevant to the changes.
• Research: open citations and check that each source supports the associated conclusion.
• Computer use: reread the final state of the target application or file.

Keep human spot checks even when a model helps perform these checks. The model generating an answer and the model judging it may share similar blind spots.

Interpreting the results

If Luna passes most routine tasks and errors cluster in a few complex types, consider routing by task category.

If Terra and Sol have similar quality but Sol completes tool work faster, compare total time and cost rather than input unit price alone.

If Astra leads only on a few difficult, high-value tasks, using it specifically for those tasks is still a reasonable approach.

If all four models fail at the same point, inspect the input material, tool responses, and acceptance criteria first. A model upgrade may not fill in missing data.

API checks when switching from GPT-5.6 to Astra

Below is a minimal Responses API request body, without authentication or client code. It illustrates the model name and reasoning field, not a call executed by this site. The original Chinese prompt asks the model to compare the attached proposals’ constraints, costs, and unresolved issues.

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{
  "model": "gpt-6-astra",
  "reasoning": {
    "effort": "medium"
  },
  "input": "请比较所附方案的约束、成本和未解决问题。"
}

Applications previously using GPT-5.6 should also check these compatibility items:

1. If the existing effort is none or minimal, start a comparison with Astra’s supported low setting.
2. Remove temperature, top_p, and top_logprobs, which Astra does not support.
3. Use the Responses API for tool calling; Astra’s Chat Completions support does not by itself imply tool-calling support there.
4. For Chat Completions, check for and remove logprobs; for Responses, check include for message.output_text.logprobs.
5. Use Standard for EU data residency requests and check for leftover Fast or Priority settings.

These checks are explicitly listed in the official migration guide. Complex applications should also assess compatibility with their own caching, conversation state, and tool execution. Source: Astra migration guidance

Keep a configuration option for the existing model during migration, and run the same acceptance samples first. A request returning text proves basic connectivity, not acceptable business quality.

How ChatGPT, Codex, and the API differ

The model name identifies the model; ChatGPT, Codex, and the API are different ways to access it. Their tools, context management, permissions, and billing affect the experience.

The Astra announcement describes a staged rollout across the relevant paid ChatGPT plans, the API, and other channels. Enterprise workspaces have access disabled by default at launch and need administrator enablement. Check your model selector or organization permissions to determine whether your account has access. Source: Astra announcement

The API price table cannot therefore be directly converted into a message allowance for a ChatGPT plan. Nor should every Codex interface setting be assumed to map one-to-one to an API parameter.

When reporting comparisons, specifying the access method, tools, and reasoning effort makes results easier to reproduce than listing only a model name.

Frequently asked questions

Is Sol still worth using after Astra’s release?

It is still worth comparing. Sol currently has lower unit pricing, and existing applications may already have been validated around it. Switching depends on whether Astra reduces errors, delivery time, or rework costs on your tasks.

Does Luna’s low price mean it can only handle simple chat?

No. OpenAI positions it for high-volume, cost-sensitive tasks, and it also supports reasoning and tools. A better test is whether task constraints are clear and outputs consistently pass validation.

Why do prices differ so much if all four have the same context size?

Context size is a capacity specification. Selection also depends on reasoning quality, tool performance, and cost; equal window size does not establish equal capability.

Should max be the default for every request?

Evaluate it first. Simple field extraction and complex debugging need different computational investment. Test whether higher effort produces a verifiable benefit.

Should older GPT-5.5 and GPT-5.4 models be ignored entirely?

They may still be useful comparison models for existing applications. This article focuses on the four current main general-purpose models; compare established older workflows on the same samples before migrating.

How should I make the final decision?

Set a minimum quality requirement, then compare the cost of tasks that pass acceptance. Allow different tasks to use different models, and sample again when prices, model behavior, or business data change.

Official references

• GPT-6 Astra announcement: release background, evaluations, and rollout.
• OpenAI model catalog: current positioning and specialized model categories.
• GPT-6 Astra specifications: context, modalities, and tools.
• GPT-5.6 Sol specifications: alias, parameters, and promotional pricing.
• GPT-5.6 Terra specifications: the balanced tier.
• GPT-5.6 Luna specifications: the high-volume tier.
• API pricing: Standard, caching, long context, and processing tiers.
• Astra usage and migration guide: new capabilities, parameter restrictions, and migration checks.