GPT-6 vs. GPT-5.6: Six Models Compared by Use Case and API Cost

The main difference among GPT-6 Astra, Sol, and Luna and GPT-5.6 Sol, Terra, and Luna is where each model sits on the quality, efficiency, and API cost spectrum. Start with GPT-6 Astra for the hardest open-ended work, evaluate GPT-6 Sol when you need a balance of capability and cost, and test GPT-6 Luna for well-scoped tasks at high volume.

Within the GPT-5.6 family, Sol is the flagship, Terra balances intelligence and cost, and Luna targets cost-sensitive workloads. The shared Sol name does not mean GPT-6 Sol and GPT-5.6 Sol occupy the same position within their respective families.

Scope: OpenAI API models, not subscription plans

The specifications and prices below are based on the official OpenAI API model comparison and individual model pages checked on September 24, 2026. Model availability, reasoning settings, usage limits, and charges in ChatGPT or Codex can differ by product, plan, and account.

OpenAI's model selection guide describes Astra as suited to ambiguous problems and deep analysis, Sol as an everyday choice for work requiring judgment, and Luna as efficient for scoped tasks and frequent automation. These roles are a starting point, not a guarantee that one model wins every task.

Six models at a glance

Prices are in US dollars per one million text tokens under Standard processing, excluding tool charges. Cached input, long-context requests, Batch, Flex, and Fast processing can use different rates. Check the current OpenAI API pricing page before deploying a workload.

How do Astra, Sol, Terra, and Luna relate?

The GPT-6 family consists of Astra, Sol, and Luna. Astra targets the most demanding work, Sol balances capability and cost, and Luna emphasizes efficiency. GPT-5.6 uses Sol, Terra, and Luna for its flagship, balanced, and efficient tiers. OpenAI describes GPT-5.6 Terra as roughly corresponding to an earlier mini tier and GPT-5.6 Luna to an earlier nano tier. The API alias gpt-5.6 points to GPT-5.6 Sol.

This table describes roles within each family. It does not claim that models in the same row have equal quality, speed, or features across generations. In particular, GPT-6 Sol is the balanced GPT-6 option, whereas GPT-5.6 Sol is the GPT-5.6 flagship.

GPT-6 Astra: for the hardest end-to-end work

The model ID is gpt-6-astra. OpenAI presents Astra as its most capable model for complex reasoning, software engineering, computer use, research, and document creation. It is the strongest starting candidate when a task requires several steps across files, tools, and uncertain requirements.

• Projects with incomplete requirements that require careful judgment
• Long coding workflows spanning design, implementation, and verification
• Research that must reconcile conflicting sources and explain uncertainty
• Complex work involving documents, data, browsers, and professional software

Astra has the highest listed token prices in this comparison. Yet the GPT-6 model guide reports that in some evaluations it produced stronger results with fewer output tokens than earlier models. That observation does not establish its cost for your workload: compare total cost per accepted result, including retries and review.

GPT-6 Sol: a balance of capability and cost

The model ID is gpt-6-sol. It is designed for complex coding and agentic workflows at a lower listed token price than Astra. Sol is worth testing when the goal is clear but the work still needs judgment, tool use, and a polished result.

• Routine feature development, debugging, code review, and tests
• Writing that requires research and source checks
• Workflows across spreadsheets, documents, browsers, and files
• Multi-step tasks whose ambiguity does not justify the highest-capability model

If balancing quality and cost is your priority, Sol can be a useful comparison baseline. The official model catalog, however, suggests starting with Astra if you have no selection criteria yet. The code generation guide mentions current general-purpose models such as Sol for Codex, while recommending Astra as the starting model for most API-based code generation. Test both on your own tasks before deciding.

GPT-6 Luna: efficient, well-scoped work at scale

The model ID is gpt-6-luna. OpenAI describes it as the most efficient GPT-6 model for focused, high-volume tasks. It has the lowest listed input and output token prices among the six models in this article.

• Extracting and classifying information into a defined format
• Short summaries, style changes, and metadata generation
• Constrained file edits and data cleanup
• Producing drafts at scale for human review

For ambiguous or high-impact work, retries and human corrections can outweigh a low token price. Give Luna narrow instructions, specify the output format, and verify a sample of the results against a clear quality standard.

GPT-5.6 Sol: the GPT-5.6 flagship

The model ID is gpt-5.6-sol, and the gpt-5.6 alias routes to it. Its official model page identifies it as the GPT-5.6 flagship for complex professional work.

For a new application, compare GPT-6 models before settling on GPT-5.6 Sol. An existing application may have prompts, evaluations, and operating targets calibrated to GPT-5.6 Sol, though. Compare identical tasks for quality, latency, token use, and failure rate before changing a production workflow.

The GPT-5.6 model guide highlights its front-end design judgment and tool workflow features. Existing behavior and regression tests can be reasons to keep it in a comparison set.

GPT-5.6 Terra: a middle tier for 5.6 workloads

The model ID is gpt-5.6-terra. The official Terra page positions it as a balance of intelligence and cost, roughly corresponding to an earlier GPT-5 mini tier.

Terra can fit document processing, content generation, analysis, and automation that need more judgment than a simple extraction task. At the listed Standard rates, though, it has the same input price as GPT-6 Sol and a higher output price. Existing system compatibility, response quality, and latency deserve testing before choosing Terra on price alone.

GPT-5.6 Luna: cost-sensitive 5.6 workflows

The model ID is gpt-5.6-luna. The official Luna page describes a model for cost-sensitive, high-volume work, roughly corresponding to an earlier nano tier.

It is a candidate for classification, extraction, and short summaries with clear output criteria. As of September 24, 2026, GPT-6 Luna's listed Standard input and output token prices are lower than GPT-5.6 Luna's. Evaluate GPT-6 Luna first for a new workload, while retaining GPT-5.6 Luna as a comparison when compatibility with an existing 5.6 system matters.

Shared specifications and meaningful differences

A later knowledge cutoff does not mean a model performs better on every question. For current events or changing facts, connect a current source and verify the answer. The context window is also a maximum supported capacity, not a recommendation to send very long prompts: long input may use different pricing.

How should you choose reasoning effort?

Reasoning effort controls how much computation a model can use to work through a problem before producing an answer. Increasing it can raise latency and cost without a useful quality gain on a simple task. GPT-6 Astra does not support none; the other five models listed here do.

OpenAI's selection guidance recommends testing representative inputs and keeping the lightest model and reasoning setting that meet your quality bar. Record reasoning effort alongside the model name when comparing results.

How do you estimate API cost?

For uncached text tokens under Standard short-context pricing, before tool charges, use:

Estimated cost = input tokens ÷ 1,000,000 × input rate + output tokens ÷ 1,000,000 × output rate.

For example, assume one request uses 100,000 input tokens and 20,000 output tokens. This input is below the 272,000-token threshold for long-context pricing, and the output is below the 128,000-token maximum for each model.

This example illustrates rate differences, not a guaranteed bill. Caching, long-context pricing, Batch or Fast processing, search and computer-use tools, and retries can change the total. The listed GPT-5.6 Sol rate is promotional according to its official model page, so recheck it when planning a longer-term budget.

Which model should you test for your use case?

These are candidates for evaluation, not measured performance rankings. The best choice depends on your actual inputs, acceptance criteria, latency target, and full cost per completed task.

A practical way to compare the models

Published specifications explain each model's intended role, but a single generic benchmark cannot select a model for every production workflow. Use a small, representative evaluation set:

1. Collect real tasks. Include frequent cases, difficult edge cases, and long inputs.
2. Define success. Measure accuracy, omissions, output format, evidence, tool success, and the time a reviewer spends correcting results.
3. Record reasoning effort. Compare models at the same supported setting first, then test whether a different setting improves the outcome.
4. Measure whole-task cost. Include tokens, tool calls, retries, latency, and review effort.
5. Choose the lightest passing setup. Prefer the least costly configuration that reliably meets your quality threshold.
6. Re-evaluate changes. Prices, aliases, and model behavior can change; rerun key cases before a major switch.

Frequently asked questions

Which of the six models should I try first?

The official model catalog suggests GPT-6 Astra when you have no selection criteria yet. If you want to balance capability and cost, compare GPT-6 Sol as well. For clear, repetitive tasks, include GPT-6 Luna. Decide using representative results and total cost.

Is GPT-6 Astra always the best choice?

Astra targets the highest capability, but its listed token prices are higher. For a bounded task such as classification or a simple transformation, Sol or Luna may meet the same acceptance criteria for less money. Test that claim on your own data.

Are GPT-6 Sol and GPT-5.6 Sol the same tier?

No. GPT-6 Sol is the balanced option within GPT-6, while GPT-5.6 Sol is the flagship of the GPT-5.6 family. Compare the full model IDs and observed results, not the Sol label alone.

Why is there no GPT-6 Terra?

The listed GPT-6 family contains Astra, Sol, and Luna. Rather than assuming a one-to-one successor for GPT-5.6 Terra, test GPT-6 Sol and Luna against your quality and cost requirements.

Is GPT-6 Luna always better than GPT-5.6 Luna?

No universal result is guaranteed. GPT-6 Luna has lower listed Standard input and output token prices as of the review date, but an existing prompt may behave differently. Compare accuracy, format compliance, latency, and complete task cost.

Are these API prices the same as ChatGPT or Codex subscription charges?

No. The token rates in this article are for the OpenAI API. Check the relevant product and plan for ChatGPT or Codex model access, usage limits, and charges.

Summary

Test GPT-6 Astra for the most demanding and ambiguous work, GPT-6 Sol when capability and cost must be balanced, and GPT-6 Luna for clear tasks at scale. Keep GPT-5.6 Sol, Terra, and Luna in the comparison when an existing 5.6 workflow or evaluation baseline matters.

All six models list the same context window and maximum output, but those numbers alone do not decide the winner. Compare quality, latency, token rates, retries, and reviewer effort on your own representative tasks.

Specifications and prices checked September 24, 2026. Generative AI helped draft and structure this article; model roles, specifications, and prices were checked against public OpenAI documentation. Pricing and availability may change.