GPT-6 Astra vs Sol vs Luna: Which Tier Is Actually Worth Paying For?

Updated 27 October 2026. OpenAI released GPT-6.1 Terra on 26 October: $1 input / $0.05 cached input / $5 output per million tokens, with a 1,050,000-token context window and 128,000 maximum output. It is a fast option for visual reasoning, computer use and natural writing. GPT-6.1 Sol and GPT-6 Astra remain better candidates for the most complex coding; Terra does not replace every frontier model. See OpenAI’s GPT-6.1 Terra announcement and our current ChatGPT model guide.

Scope: the named model comparison below retains its original models and dated examples. Terra is an additional current option, not a silent replacement for either model.

Published 23 September 2026 by Jake Hissitt, Founder of Stob.AI.

Historical scope: The original GPT-6 Sol comparison, score assessments and routing examples below describe the 23 September 2026 lineup. GPT-6.1 Sol has since superseded GPT-6 Sol; use the current routing section for new deployments.

The original GPT-6 release shipped in three tiers with a hundredfold price spread between the cheapest and the most expensive. Astra at $10 in / $50 out, Sol at $2 / $10, and Luna at $0.10 / $0.50. They share the same 1.05 million token context window and 128,000 token output limit, with configurable reasoning controls. Astra starts at low effort; the original Sol and Luna also support none. All three support settings through max.

That makes tier selection the highest-leverage cost decision in an OpenAI-based system. Here is how to make it.

The original three tiers side by side

	GPT-6 Astra	GPT-6 Sol	GPT-6 Luna
Input / 1M	$10.00	$2.00	$0.10
Output / 1M	$50.00	$10.00	$0.50
Cached input / 1M	$1.00	$0.20	$0.01
Cache write / 1M	$12.50	$2.50	$0.125
Above 272K input	2x input and cache, 1.5x output on the whole request	$4 in / $15 out	$0.20 in / $0.75 out
Context	1,050,000	1,050,000	1,050,000
Max output	128,000	128,000	128,000
Best for	The hardest reasoning and research	Coding, agents, general production	Classification, extraction, routing

The 272K surcharge is the trap

Every tier has a long-prompt rule, and Astra's is the harshest: cross 272,000 input tokens and the entire request bills at double the input and cache rate and 1.5 times the output rate. Not the excess. The whole request.

A 300,000-token Astra request costs $6.00 on input alone before a single output token. The same request on Sol costs $1.20, and on Luna $0.06.

If you routinely load large document sets, the correct fix is usually retrieval, not a bigger context window. Send the relevant 20,000 tokens rather than the whole corpus and the tier question largely answers itself.

Where each tier earns its price

Luna: the volume workhorse

At $0.10 / $0.50, Luna is cheap enough to put a frontier-family model in places that previously used keyword rules. Email classification, field extraction, intent routing, deduplication checks, first-pass qualification. It keeps the full 1.05M context, so it can read long inputs cheaply and hand a structured summary to a more expensive model.

That pattern — Luna reads, Sol decides — is the single most effective cost structure we deploy.

Sol: the default

At publication on 23 September, GPT-6 Sol scored level with Astra on reasoning and coding in our comparison while costing a fifth as much. It was our recommended default for coding agents, research workflows and document generation in that original lineup. The reasoning-effort dial from none to max lets one model serve both the cheap and the hard steps.

Astra: the escalation tier

Astra is for the small share of work where Sol at maximum effort still misses: novel research problems, deeply adversarial analysis, the hardest multi-constraint reasoning. Used as an escalation path for a few percent of requests, it is good value. Used as a default, it is a five-fold overspend on work Sol would have completed.

Current routing: where Terra fits

Keep Luna on simple volume, evaluate Terra for everyday writing, visual reasoning and computer use, then escalate difficult coding to GPT-6.1 Sol and proven exceptions to Astra. Terra’s none reasoning setting is available; GPT-6.1 Sol starts at low. Read current rates and cost examples.

The original routing pattern

Luna handles every high-volume, structured step: read, classify, extract, route.
Sol handles the judgement and generation steps, with reasoning effort set per step.
Astra is called only when a validation check fails or a confidence score falls below your threshold.
A human reviews anything that fails twice.

This is the same layered structure we describe in building an AI sales system: cheap models carry volume, expensive models handle exceptions.

Frequently asked questions

Is GPT-6 Astra worth the price over Sol?

Only for the hardest reasoning tasks where Sol at maximum reasoning effort demonstrably fails on your own test set. As a general default it costs five times more for work Sol handles.

How cheap is GPT-6 Luna really?

$0.10 per million input tokens and $0.50 per million output. A million short classification calls of roughly 500 input and 50 output tokens costs about $75.

Do all three tiers have the same context window?

Yes, 1,050,000 tokens with up to 128,000 tokens of output on each tier.

What is the 272K token surcharge?

Prompts above 272,000 input tokens are billed at a higher rate for the whole request. On Astra that is double input and 1.5 times output; Sol moves to $4 / $15 and Luna to $0.20 / $0.75.

Which GPT-6 tier is best for coding?

In the original 23 September comparison, GPT-6 Sol at high reasoning effort matched Astra on our coding score at a fifth of the cost. For current deployments, evaluate GPT-6.1 Sol and test difficult exceptions against Astra.

What to do next

Take your highest-volume workflow and check which tier it currently calls. If it is Astra and the task is structured, move it down a tier and measure the completed-outcome rate. Compare every model and price in our AI model comparison tool.