GPT-6 Sol vs. Luna vs. Astra: Pricing, Purpose, and Trade-Offs

OpenAI’s reported GPT-6 family is built around a familiar production decision: pay for maximum capability when a task demands it, or lower the per-request bill when the workload must run at scale. Astra is the premium option; Sol is positioned as the practical middle tier; and Luna is the low-cost model for high-throughput tasks.

API pricing at a glance

API prices below are per 1 million tokens.

Model	Input	Output	Best fit
GPT-6 Astra	$10	$50	Difficult reasoning, high-stakes coding, and demanding agentic work
GPT-6 Sol	$2	$10	Professional workflows, coding, computer use, and accuracy-sensitive production tasks
GPT-6 Luna	$0.10	$0.50	High-volume classification, extraction, routing, chat, and other routine automation

GPT-6 Astra’s reported $10 input and $50 output pricing places it firmly at the premium end of the lineup. 17 Sol is one-fifth of Astra’s price for both input and output, while Luna is one-hundredth of Astra’s headline price. That spread gives teams room to match a model to the economics of each request rather than using one expensive model everywhere.

Which GPT-6 tier fits each job?

GPT-6 Astra: reserve it for the hardest work

Astra is the choice when the cost of a wrong, incomplete, or weak response outweighs token spend. That can include complex reasoning, difficult coding tasks, and agents that must handle multi-step work with less room for error.

The key trade-off is simple: Astra costs five times more than Sol per input or output token. It is most defensible when its additional capability materially improves the outcome—not merely because it is the top-tier model.

GPT-6 Sol: the general production workhorse

Sol occupies the middle of the lineup. At $2 per million input tokens and $10 per million output tokens, it is aimed at professional work that needs more capability than a lightweight routing or extraction model, without Astra’s premium cost.

For many teams, that makes Sol the natural starting point for coding assistance, internal knowledge workflows, computer-use automation, and customer-facing applications where response quality matters. Escalating only difficult cases to Astra can help keep a production system’s blended cost under control.

GPT-6 Luna: optimize for volume

Luna’s $0.10 input and $0.50 output pricing changes the economics of workloads that generate very large request counts. Typical uses include tagging, classification, data extraction, summarization, intent routing, and first-pass support automation.

Luna is not necessarily the right choice for every task. Its value is strongest where a workflow can tolerate a less costly model or has validation, rules, and escalation paths around it. A common architecture is to use Luna for the majority of routine requests, then send ambiguous or high-value cases to Sol or Astra.

What the price cuts mean

OpenAI attributes the lower Sol and Luna prices to improvements in caching and inference, and reports a 50% reduction versus GPT-5.6 promotional pricing. Sol moves from $4/$20 to $2/$10 per million input/output tokens, while Luna moves from $0.20/$1.20 to $0.10/$0.50.

The wider market is moving in the same direction. Anthropic lists Claude Opus 5.5 at $4 per million input tokens and $20 per million output tokens, compared with $5/$25 for Claude Opus 5. 12 Anthropic also says Opus 5.5 is intended for long-running agentic coding and knowledge work. 2

For buyers, headline token prices are only part of the calculation. Model reliability, output length, cache usage, latency, and the number of retries can all affect the cost of completing a real task. A cheaper model that requires repeated corrections may not be cheaper in practice.

How cloud platforms could benefit

Lower-cost model tiers can make AI features viable in applications that previously could not meet a cost target. For cloud platforms, more deployable AI workloads can translate into greater use of surrounding infrastructure and services, such as data systems, identity, security, monitoring, and application hosting.

The strongest near-term opportunity is model routing: businesses can use a lower-cost tier for routine work and reserve premium capacity for exceptions. That approach is particularly useful for large support, document-processing, and software-development workflows.

Availability and reseller pricing should still be checked directly with each provider. The available material does not establish that Sol and Luna are offered through every cloud marketplace or that their provider-specific commercial terms match OpenAI’s direct API pricing.

The practical decision framework

Choose a model by starting with the workload rather than the model label:

• Use Astra when the task is difficult, consequential, or expensive to get wrong.
• Use Sol when you need strong general performance for everyday enterprise and developer workflows.
• Use Luna when volume, latency, and cost per request matter most—and when a fallback path is available for hard cases.

The important change is not just that a new model tier exists. It is that the price gap between premium reasoning and mass-market automation is now wide enough to support deliberate model routing. Teams that measure quality, latency, and full task cost across all three tiers will be better positioned than teams that default every request to the most capable model.