GPT-5.6 Is Right here: Sol, Terra, and Luna Pricing & Benchmarks

For twelve days, the most effective AI fashions on the planet existed and virtually no person might contact them.

That ends now! GPT-5.6 Sol, Terra, and Luna go public right this moment! The fashions are accessible by all customers (no subscription required)

That is the total breakdown of what’s on provide: three fashions, 4 costs, one precedent, and a functionality desk that ought to assist you choose the suitable mannequin. Palms-on outcomes observe the second entry opens.

One Era, Three Fashions

GPT-5.6 retires OpenAI’s naming chaos for good. The quantity marks the era. This makes it straightforward to categorise, so the following Luna enchancment gained’t power a whole-family rename.

Sol is the flagship, constructed for the toughest 10 p.c of labor: long-horizon coding brokers, safety analysis, deep scientific evaluation. The brand new reasoning controls reside right here.

Terra is the workhorse and the plain migration goal. GPT-5.5-class high quality at half the worth, geared toward manufacturing quantity: assist, inside instruments, doc pipelines.

Luna is the pace tier, and quietly the sleeper of the launch. The most affordable mannequin within the household lands close to GPT-5.5 on a number of checks. Extra on why that issues under.

gpt-5.6-sol, gpt-5.6-terra, and gpt-5.6-luna are their respective names within the API. This would possibly look like a small change on paper. But it surely’s a giant one for any coder who has tried protecting monitor of o3, o4-mini, GPT-4 Turbo, and 4o abruptly.

Pricing: 4 Methods to Pay

Three fashions, however 4 costs, as a result of launch week surfaced a wrinkle.

Sol Quick is the brand new form right here: the identical flagship mind served from Cerebras {hardware} at as much as 750 tokens per second, for two.5x the usual fee. Pace as an specific paid tier, moderately than a queue lottery, is one thing OpenAI has by no means bought earlier than. In case your product is latency-bound, this line merchandise alone modifications what’s viable.

The quieter pricing story is caching, and agent builders ought to care extra about it than the headline charges:

Express cache breakpoints, so that you management what will get cached as an alternative of guessing

A 30-minute minimal cache life

Cache writes billed at 1.25x the uncached enter fee

Cache reads preserve the 90% low cost

For long-running brokers that re-read the identical context lots of of instances, that low cost compounds into an order-of-magnitude reduce on enter prices. Construction your prompts now: secure context earlier than the breakpoint, unstable enter after.

Capabilities: Max Effort, Extremely Mode, and a Sleeper Hit

OpenAI is holding the expanded analysis suite for the GA system card, however the preview numbers already sketch the image. Two new controls headline Sol:

Max reasoning effort, a brand new ceiling that offers Sol probably the most time to suppose by an issue.

Extremely mode, which fits previous the single-agent paradigm solely. Sol spins up subagents and coordinates them to parallelize advanced work.

On benchmarks, the standout claims:

Terminal-Bench 2.1: Sol units a brand new cutting-edge on command-line workflows demanding planning, iteration, and gear coordination.

GeneBench v1: Sol beats GPT-5.5 on long-horizon genomics and quantitative biology analyses, utilizing fewer tokens to do it.

ExploitBench: Sol is aggressive with Mythos Preview at roughly a 3rd of the output tokens.

The household impact: Sol and Terra set new highs throughout the board, whereas Luna performs close to GPT-5.5 on a number of checks regardless of being the most cost effective factor on the worth sheet.

That final bullet level is the sleeper. Final era’s flagship high quality is now accessible at $1 per million enter tokens. The sample throughout the entire household isn’t simply “smarter,” it’s smarter per token and per greenback. Effectivity is the precise headline.

The Functionality No one Anticipated within the Price range Tier

Right here’s the system card element that received buried below the supply drama, and it deserves its personal part.

All three fashions, not simply Sol, are labeled at OpenAI’s “Excessive” danger degree for cyber and organic functionality. On inside capture-the-flag safety testing:

To offer you a perspective, these fashions are on half with the Mythos “Fable 5” class of Claude.

That’s the corporate’s personal framing, and the technique follows: get the potential into defenders’ arms, make offensive misuse tough, unsure, and detectable.

5 Layers Deep: The Safeguard Stack

The protection structure transport with 5.6 is probably the most elaborate OpenAI has described publicly, with configurations matched to every tier’s functionality. The design assumption is blunt: no single safeguard survives a decided, adaptive attacker.

Right here is how the method went:

Skilled refusals. The mannequin itself declines prohibited cyber help, together with disguised or jailbroken requests.

Actual-time classifiers. Cyber and bio misuse detectors consider output because it generates.

Reasoning-model overview. Excessive-risk generations pause mid-stream whereas a bigger mannequin critiques the total context. Disallowed output by no means reaches the consumer.

Account-level alerts. Flagged exercise triggers overview throughout conversations, which is how OpenAI distinguishes a safety researcher from a persistent unhealthy actor.

Differentiated entry and fast response. Essentially the most delicate capabilities aren’t on by default, and newly found jailbreaks feed a reproduce-assess-patch loop.

One caveat that I’ve acknowledged whereas testing the fashions is that generally official work generally will get blocked or slowed, particularly in the kind of immediate that are within the gray space (nothing fishy however non benign both).

The Household vs GPT-5.5 at a Look

Palms-On: 5 Assessments, One Rule

Specs are guarantees. Utilization is proof.

Each check under targets a selected declare from OpenAI’s bulletins.

Take a look at 1: Defender’s Audit (Sol, the cyber declare’s official half)

Immediate: “OWASP Juice Store is a intentionally susceptible internet app used for safety coaching. Based mostly on its well-documented authentication and cost flows, rank the highest 5 vulnerability courses it’s recognized for by severity, clarify every in plain language, and write a patch (with code) for probably the most extreme one.”

Response:

Sturdy response! The rating is impact-based moderately than a duplicate of Juice Store’s star rankings, and the patch is the right repair: changing the interpolated sequelize.question with UserModel.findOne({ the place: … }) so e-mail and password develop into sure values, with paranoid: true preserving the unique deletedAt IS NULL habits. Better part is the trustworthy scoping, because it refuses to assert the auth move is now manufacturing secure and calls out the unsalted MD5 in safety.hash(). Essential gripes: leaving XSS out of the highest 5 is odd provided that’s arguably what Juice Store is most recognized for, and rank 4 is a barely invented merged class moderately than a typical class.

Take a look at 2: The Root-Trigger Hunt (Sol, Terminal-Bench declare)

Immediate: “This file has three sections: a pricing utility, a checkout operate that calls it, and a check. Working it fails, and the error message suggests the check’s anticipated worth is mistaken. Discover the precise root trigger, repair it on the supply (not the check), and clarify in a single paragraph why the error message was deceptive. Don’t simply make the check move.”

Click on right here to view the Python File
# ============================================================
# billing_bug.py — self-contained failing check bundle
# Run: python billing_bug.py
# One bug spans all three sections. The traceback factors at
# the TEST, however the check is appropriate. Discover the actual root trigger.
# ============================================================

# ———- FILE 1 of three: pricing.py ———-
# Utility that normalizes a reduction right into a multiplier.
def normalize_discount(low cost):
“””
Convert a reduction right into a worth multiplier.
A 20% low cost ought to depart the shopper paying 80% (0.80).
Accepts both a share (20) or a fraction (0.20).
“””
if low cost > 1:
# deal with as a share, e.g. 20 -> 0.20
low cost = low cost / 100
# return the multiplier to use to the worth
return 1 – low cost

# ———- FILE 2 of three: checkout.py ———-
# Caller that applies the low cost to a cart whole.
def final_price(cart_total, low cost):
“””
Apply a reduction to a cart whole and spherical to 2 decimals.
Caller assumes normalize_discount returns the FRACTION to
subtract (e.g. 0.20), not the multiplier to maintain (0.80).
“””
fraction_off = normalize_discount(low cost)
worth = cart_total – (cart_total * fraction_off)
return spherical(worth, 2)

# ———- FILE 3 of three: test_checkout.py ———-
# The check is CORRECT. A $100 cart with 20% off must be $80.00.
def test_twenty_percent_off():
end result = final_price(100, 20)
anticipated = 80.00
assert end result == anticipated, (
f”test_checkout.py: anticipated {anticipated}, received {end result} ”
f”– examine the check’s anticipated worth” # <– deceptive trace
)

if __name__ == “__main__”:
test_twenty_percent_off()
print(“PASSED”)

Wonderful! Not simply that it was capable of finding the suitable bug, however to try this and provides the decision in such a succinct method. Fashions as used to wordiness of their responses. GPT 5.6 is a breath of recent air I this regard.

Take a look at 3: GPT 5.5 Sol vs GPT-5.5, Coding

Immediate: “Refactor this operate for readability and correctness with out altering its habits. Then checklist any edge circumstances it mishandles.”

def p(d):
r=[]
for i in d:
if i!=None and that i not in r: r.append(i)
return sorted(r) if all(sort(x)==int for x in r) else r

Wow! GPT 5.6 Sol was capable of do the requested, at 1/fifth the response measurement of GPT 5.5. Clear and apparent enchancment.

Take a look at 4: The GPT 5.6 Stress Take a look at (the Sol sleeper declare)

Immediate: “Summarize the next textual content in precisely three bullet factors, then extract each date and greenback determine right into a JSON object with keys “dates” and “quantities”:

Click on right here to view the textual content

Right and to the purpose statement.

Take a look at 5: The Contradiction Entice (Sol, Excessive reasoning declare)

Immediate: “Schedule 6 audio system (A, B, C, D, E, F) throughout 3 rooms and 4 time slots. Constraints: A and B can’t be scheduled in the identical time slot; C have to be in an earlier slot than D; E wants Room 1 to itself for 2 consecutive slots; F should current within the remaining slot; and no room might sit empty in any slot. Give me the total schedule.”

Response:

Commentary

Sol didn’t take the bait. All the things concerning the immediate says produce a grid. It counted as an alternative.

Twelve room-slots have to be stuffed. Six audio system fill six; E’s two-slot declare provides one. Seven of twelve. Inconsistent earlier than scheduling begins.

The inform is what it ignored: A/B, C-before-D, F’s closing slot. Decoys, all of them. Sol discovered the battle between cardinality and protection and argued solely that.

One miss. We requested for the minimal constraint to loosen up. Sol supplied three exits and ranked none, although just one is a single-constraint repair.

The Backside Line

GPT-5.6 are three tales simply in a single. 

The primary is the mannequin household: a flagship that pushes the agentic frontier, a workhorse that halves manufacturing prices, and a funds tier carrying final era’s flagship high quality at a greenback. Tiering this clear makes routing, not mannequin alternative, the brand new structure query.

The specs say that is the most effective mannequin household ever shipped. Based mostly on my expertise, I agree. Now it’s so that you can check these fashions in your workflows and determine for your self. 

Incessantly Requested Questions