For twelve days, one of the best AI fashions on the planet existed and virtually no one 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 complete breakdown of what’s on provide: three fashions, 4 costs, one precedent, and a functionality desk that ought to assist you choose the fitting mannequin. Arms-on outcomes observe the second entry opens.
One Technology, Three Fashions
GPT-5.6 retires OpenAI’s naming chaos for good. The quantity marks the era. This makes it simple to categorise, so the subsequent Luna enchancment gained’t pressure a whole-family rename.
- Sol is the flagship, constructed for the toughest 10 % 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, inner 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 assessments. 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. However it’s an enormous one for any coder who has tried retaining 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 price. Velocity as an express 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 adjustments what’s viable.
The quieter pricing story is caching, and agent builders ought to care extra about it than the headline charges:
- Specific 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 price
- Cache reads preserve the 90% low cost
For long-running brokers that re-read the identical context lots of of occasions, that low cost compounds into an order-of-magnitude lower 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 provides Sol probably the most time to suppose by an issue.
- Extremely mode, which matches previous the single-agent paradigm completely. 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 state-of-the-art 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 assessments 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 acquired 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 inner capture-the-flag safety testing:
To provide you a perspective, these fashions are on half with the Mythos “Fable 5” class of Claude.
“GPT‑5.6 Sol is best at serving to individuals discover and repair vulnerabilities than reliably finishing up finish‑to‑finish assaults.”
— OpenAI
That’s the corporate’s personal framing, and the technique follows: get the aptitude into defenders’ palms, 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:
- Educated 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 evaluation. Excessive-risk generations pause mid-stream whereas a bigger mannequin evaluations the complete context. Disallowed output by no means reaches the person.
- Account-level indicators. Flagged exercise triggers evaluation throughout conversations, which is how OpenAI distinguishes a safety researcher from a persistent dangerous actor.
- Differentiated entry and speedy response. Probably 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 typically official work typically 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
Arms-On: 5 Checks, One Rule
Specs are guarantees. Utilization is proof.
Each take a look at under targets a selected declare from OpenAI’s bulletins.
Check 1: Defender’s Audit (Sol, the cyber declare’s official half)
Immediate: “OWASP Juice Store is a intentionally weak net app used for safety coaching. Primarily based on its well-documented authentication and fee flows, rank the highest 5 vulnerability lessons it’s recognized for by severity, clarify every in plain language, and write a patch (with code) for probably the most extreme one.”
Response:
Robust response! The rating is impact-based moderately than a replica of Juice Store’s star rankings, and the patch is the proper repair: changing the interpolated sequelize.question with UserModel.findOne({ the place: ... }) so e-mail and password change into certain values, with paranoid: true preserving the unique deletedAt IS NULL conduct. Better part is the sincere scoping, because it refuses to assert the auth circulation is now manufacturing secure and calls out the unsalted MD5 in safety.hash(). Major gripes: leaving XSS out of the highest 5 is odd on condition that’s arguably what Juice Store is most recognized for, and rank 4 is a barely invented merged class moderately than a regular class.
Check 2: The Root-Trigger Hunt (Sol, Terminal-Bench declare)
Immediate: “This file has three sections: a pricing utility, a checkout perform that calls it, and a take a look at. Working it fails, and the error message suggests the take a look at’s anticipated worth is fallacious. Discover the precise root trigger, repair it on the supply (not the take a look at), and clarify in a single paragraph why the error message was deceptive. Don’t simply make the take a look at move.”
Click on right here to view the Python File
# ============================================================
# billing_bug.py — self-contained failing take a look at bundle
# Run: python billing_bug.py
# One bug spans all three sections. The traceback factors at
# the TEST, however the take a look at is right. 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 value multiplier.
A 20% low cost ought to go away 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)
value = cart_total - (cart_total * fraction_off)
return spherical(value, 2)
# ---------- FILE 3 of three: test_checkout.py ----------
# The take a look at is CORRECT. A $100 cart with 20% off needs to 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}, acquired {end result} "
f"-- verify the take a look at's anticipated worth" # <-- deceptive trace
)
if __name__ == "__main__":
test_twenty_percent_off()
print("PASSED")
Superb! Not simply that it was capable of finding the fitting 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.
Check 3: GPT 5.5 Sol vs GPT-5.5, Coding
Immediate: “Refactor this perform for readability and correctness with out altering its conduct. Then checklist any edge instances 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(kind(x)==int for x in r) else r
Wow! GPT 5.6 Sol was in a position to do the requested, at 1/fifth the response measurement of GPT 5.5. Clear and apparent enchancment.
Check 4: The GPT 5.6 Stress Check (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 remark.
Check 5: The Contradiction Lure (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 should 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 complete schedule.”
Response:
Statement
Sol didn’t take the bait. All the things in regards to the immediate says produce a grid. It counted as an alternative.
Twelve room-slots should 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 chill out. Sol provided 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 one of the best mannequin household ever shipped. Primarily based on my expertise, I agree. Now it’s so that you can take a look at these fashions in your workflows and resolve for your self.
Continuously Requested Questions
A. GPT-5.6 Sol, Terra, and Luna launched publicly on Thursday, July 9, 2026, following Commerce Division approval, with preview entry already increasing globally. The rollout covers the API, Codex, and ChatGPT. OpenAI has not but printed which ChatGPT subscription tiers get Sol first, so verify the mannequin picker on launch day.
A. Sol is the flagship for the toughest work: long-horizon coding brokers, safety analysis, and deep evaluation. Terra matches GPT-5.5 high quality at half the worth, making it the migration goal for manufacturing workloads. Luna is the quickest, least expensive tier but nonetheless lands close to GPT-5.5 on a number of assessments.
A. Per million tokens: Sol is $5 enter and $30 output, Terra $2.50 and $15, Luna $1 and $6. Sol Quick is a brand new premium choice at $12.50 and $75 that serves the identical flagship mannequin at as much as 750 tokens per second on Cerebras {hardware}.
A. Sol is OpenAI’s most succesful cybersecurity mannequin, so on the authorities’s request below a brand new cyber Govt Order framework, the June 26 launch started as a restricted preview for roughly 20 vetted organizations. After extra testing and company conferences, the Commerce Division accredited the broad launch twelve days later.
A. OpenAI classifies all three fashions at its “Excessive” cyber danger degree, with Sol fixing 96.7% of inner capture-the-flag challenges, however says none can autonomously run a whole assault marketing campaign below take a look at circumstances. They ship with 5 layered safeguards hardened by over 700,000 GPU hours of red-teaming.
I specialise in reviewing and refining AI-driven analysis, technical documentation, and content material associated to rising AI applied sciences. My expertise spans AI mannequin coaching, knowledge evaluation, and knowledge retrieval, permitting me to craft content material that’s each technically correct and accessible.
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