GPT-6 API Pricing: Astra, Sol, Luna & What You’ll Actually Pay

GPT-6 API pricing starts at $0.10 input and $0.50 output per million tokens for Luna. GPT-6 Sol costs $2 input and $10 output, while Astra costs $10 input and $50 output at OpenAI Standard short-context rates. Choosing a model is only the first step: caching, output volume, long context, and repeated agent calls determine what a workflow actually costs.

A low input rate can hide an expensive application. Long conversations accumulate context, reasoning adds billable output, and retries multiply requests. The useful comparison is therefore both price per token and cost per successfully completed task—with the exact model, processing mode, and context tier kept consistent.

LinkModel lists GPT-6 Astra, Sol, and Luna at 25% below the corresponding OpenAI Standard token rates in the pricing schedules compared here. Pay-as-you-go billing, no minimum spend, and no monthly platform fee help teams start small. Its unified API and billing also simplify access across supported models, while the Playground lets developers compare outputs before integrating.

Updated October 22, 2026: Expanded GPT-6 Astra, Sol, and Luna pricing tables, added LinkModel’s 25% discount against corresponding OpenAI Standard token rates, and refreshed request-cost examples, long-context calculations, and charts.

GPT-6 API Pricing at a Glance

Luna has the lowest listed token prices, Sol sits between Luna and Astra, and Astra carries the highest rates. Start with the table below, then check the context tier and any additional billing categories your application uses.

All prices are USD per 1 million tokens, for text-token usage with no more than 272K input tokens.

OpenAI rates follow the corresponding official model documentation. LinkModel rates follow its published pricing schedules supplied for this comparison. The listed LinkModel rates are 25% lower than the matching Standard rates; this is not a comparison with every available processing mode.

This guide covers GPT-6 Sol, not GPT-6.1 Sol. They are separate models, and their cached-input rates should not be interchanged. For a broader view of model generations and billing categories, our GPT API pricing guide provides additional context.

Cached Input and Cache-Write Prices

Caching has separate rates from ordinary input. The short-context schedules used in this comparison are:

Cache-write retention matters. OpenAI’s detailed pricing distinguishes retention options, while the LinkModel schedule used here labels a single cache-write category. Confirm the applicable retention period and route support before treating two cache-write prices as equivalent services.

A cached-input discount also applies only to eligible cached tokens. It does not automatically reduce output, tool charges, or the entire invoice by the same percentage.

GPT-6 Astra vs Sol vs Luna: Which Model Should You Budget For?

Choose the least expensive model that consistently meets your acceptance criteria, then escalate harder work when needed. Token prices establish the starting cost; your task results determine whether that price is economical.

OpenAI’s model positioning supports the broad distinction, but your data, tools, and acceptance tests should determine the final choice.

Our model-selection guide explains how to build that evaluation. For coding-specific alternatives, compare the options in our coding-model API guide against the same tasks.

Is Astra Five Times More Expensive Than Sol?

Astra’s Standard short-context uncached input and output rates are five times GPT-6 Sol’s rates. The same ratio applies to their corresponding LinkModel input and output prices.

That does not mean every completed task costs five times more. Token consumption, retries, cache use, and tool activity can differ.

The older 2.5× comparison refers to GPT-5.6 Sol, not GPT-6 Sol. Historical results involving GPT-5.6 Sol should retain that full model name. Our dedicated Astra versus GPT-5.6 comparison covers that earlier pairing.

How Much Does a GPT-6 API Request Cost?

Multiply each billable token category by its rate, divide by one million, then add any separately billed charges.

For a request without caching:

Request token cost = (input tokens × input rate + billable output tokens × output rate) ÷ 1,000,000

Use billable output from the provider’s usage records. Visible answer length alone may not capture all charged output, including reasoning where applicable.

Example: 100K Input and 10K Output Tokens

The following calculated example uses 100,000 uncached input tokens and 10,000 billable output tokens per request. It excludes cache writes, tools, retries, and other charges.

For Astra on LinkModel, the calculation is:

- Input: 100,000 ÷ 1,000,000 × $7.50 = $0.75

- Output: 10,000 ÷ 1,000,000 × $37.50 = $0.375

- Total: $1.125

These are rate-based estimates, not measured invoices.

Example: 1,000 Identical Requests

Scaling the same workload to 1,000 requests produces:

The percentage saving stays the same, but the dollar value depends on workload size and model choice.

For monthly planning, multiply the relevant per-request estimate by expected request volume. For agent products, estimate calls per workflow as well: 1,000 workflows do not necessarily mean 1,000 API calls.

Our broader AI API price comparison can help place these estimates alongside other model options.

What Happens Above 272K Input Tokens?

A request with more than 272K input tokens moves to the long-context tier for the full request. It is not priced progressively, with only the excess tokens charged more.

The applicable input and cache rates double, while output rates increase by 50%. LinkModel’s schedule states that input used for tier selection includes cached input.

Long-Context Input and Output Prices

All figures below are USD per million tokens for requests with more than 272K input tokens.

Long-Context Cache Prices

As with short-context caching, compare matching retention conditions before selecting a route.

Astra Cost Example: 271K vs 273K Input Tokens

Consider two Astra requests with 20,000 output tokens and no caching:

At OpenAI Standard rates, the first request costs $2.71 for input plus $1 for output. The second costs $5.46 for input plus $1.50 for output.

Input grows by approximately 0.74%, but calculated token cost rises by approximately 87.6% because the entire request changes tiers.

A large context window is a capability limit, not a flat-price allowance. Monitor the context window used by each request, and remove stale logs, duplicated files, and irrelevant history before they become recurring costs.

Standard, Batch, Flex, and Fast: Which Price Applies?

OpenAI’s processing modes have different token rates. Batch and Flex use 50% of Standard rates, while Fast uses twice the applicable Standard rates for these models.

The following table shows short-context uncached input and output rates:

These are OpenAI processing-mode prices, not a statement that LinkModel offers every mode at these rates.

Does LinkModel’s 25% Discount Beat Batch Pricing?

Not on the listed token rates alone. Astra Batch or Flex at $5 input and $25 output is below LinkModel’s compared rates of $7.50 and $37.50.

The choice depends on whether the processing option supports your workload’s timing, availability, and integration requirements. Review the applicable Batch and Flex cost-saving options before treating Standard as your only baseline.

How Caching Changes Your GPT-6 Bill

Caching can reduce the cost of reusable input, but its value depends on eligible cache hits and any write charges. Stable instructions, tool definitions, schemas, and reference prefixes are candidates for reuse.

For Astra on LinkModel, 100,000 tokens charged as ordinary short-context input cost $0.75. The same volume charged as eligible cached input costs $0.075.

That is a 90% reduction for that input category, not a guaranteed 90% reduction in the complete request.

When using prompt caching, track:

- Ordinary input and cached input separately.

- Cache writes and their retention settings.

- Whether changing prefixes prevent reuse.

- Output, tools, and retries that caching does not remove.

Avoid double-counting. If reported total input already includes cached tokens, separate the categories before applying rates. Similarly, do not add reasoning tokens twice if they are already included in billable output.

When Is Astra Worth Its Higher Token Price?

Astra is worth evaluating when a cheaper model’s failures, repeated attempts, or correction needs make the completed result more expensive. A higher token rate alone does not establish higher—or lower—task cost.

Use the same task set, tools, acceptance criteria, and retry policy when comparing models. Record both token spending and accepted outcomes.

A Practical Break-Even Test

For uncached short-context input and output, Astra’s rates are 5× GPT-6 Sol’s rates. If the input/output composition remains identical, Astra would need approximately 20% of Sol’s token volume to match its direct token cost.

This is a simplified calculation, not a benchmark result. It does not apply unchanged when cache shares, tool charges, context tiers, or processing modes differ.

A more useful production metric is:

API cost per successful task = total API spending across all attempts ÷ accepted successful tasks

If you include runtime or human review, report those separately so readers can distinguish API savings from estimated business value.

The aim is to select the lowest-cost model that satisfies the task—not to make the most expensive model the default.

Use Routing Instead of One Model for Everything

A practical model-routing strategy starts with a lower-cost candidate and escalates when validation fails or the task requires additional capability.

For example, evaluate Luna for a repeatable extraction stage, Sol for a more complex analysis stage, and Astra for unresolved exceptions. The thresholds should come from your own acceptance tests.

A unified API can simplify that architecture, but switching models still requires checking supported parameters and tools. It does not eliminate evaluation work.

What Should You Check Before Switching API Providers?

Compare the route as well as the rate. A discount creates value when the selected integration supports the work you need to complete.

LinkModel’s integration documentation is the place to check supported routes. OpenAI-compatible access can help reuse existing clients, but compatibility does not mean every provider-specific feature behaves identically.

For a wider provider-selection framework, our GPT-6 API providers guide covers additional purchasing considerations.

Are API Charges the Same as ChatGPT or Codex Usage?

No. Subscription allowances are not a conversion table for API prices. A percentage of a weekly allowance, a usage window, or a reset does not establish the dollar cost of an API request.

Our review of user questions found recurring confusion between quota consumption, token prices, and total workflow costs. This guide uses those questions to identify budgeting problems; it does not convert community allowance reports into official API charges.

For an application budget, use the actual billing route, model rates, and usage records. For broader production planning, our AI API pricing comparison provides context beyond subscription anecdotes.

How We Checked These Prices and Calculations

This article compares published rates and calculated examples. It does not claim an independently measured performance advantage or a LinkModel-run success-rate benchmark.

Official pricing and model rules were checked against OpenAI’s Astra documentation, GPT-6 Sol documentation, and Luna documentation. The detailed OpenAI API pricing schedule provides additional processing and caching information.

The LinkModel rates shown here reflect the pricing schedules dated October 22, 2026. Check the selected model on the current LinkModel pricing page before purchasing, including its context tier and cache settings.

Frequently Asked Questions

Choose GPT-6 by Cost per Accepted Result

Start with the model price, calculate your request cost, and validate the result before scaling. Luna, Sol, and Astra occupy different price levels; the right choice depends on which model completes your work reliably.

LinkModel’s compared rates reduce matching Standard token charges by 25%, while flexible billing and unified access simplify testing across supported models. For Astra-specific detail, the Astra pricing guide expands on its long-context economics.

Compare the published GPT-6 rates, test representative requests, and use the resulting usage records to set a realistic budget.