For twelve days, the very 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 immediately! The fashions are accessible by all customers (no subscription required)
That is the complete breakdown of what’s on supply: three fashions, 4 costs, one precedent, and a functionality desk that ought to assist you choose the precise mannequin. Palms-on outcomes comply with the second entry opens.
One Era, Three Fashions
GPT-5.6 retires OpenAI’s naming chaos for good. The quantity marks the technology. This makes it straightforward to categorise, so the following Luna enchancment received’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 dwell right here.
- Terra is the workhorse and the plain migration goal. GPT-5.5-class high quality at half the value, geared toward manufacturing quantity: help, inside instruments, doc pipelines.
- Luna is the velocity tier, and quietly the sleeper of the launch. The most cost effective 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. Nevertheless it’s a giant one for any coder who has tried preserving monitor of o3, o4-mini, GPT-4 Turbo, and 4o all of sudden.
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. Velocity as an express paid tier, somewhat 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 fee
- Cache reads hold the 90% low cost
For long-running brokers that re-read the identical context a whole lot of instances, that low cost compounds into an order-of-magnitude lower on enter prices. Construction your prompts now: steady context earlier than the breakpoint, risky 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 assume by way of an issue.
- Extremely mode, which fits previous the single-agent paradigm totally. 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 power 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 value sheet.
That final bullet level is the sleeper. Final technology’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 Funds Tier
Right here’s the system card element that acquired buried underneath the supply drama, and it deserves its personal part.
All three fashions, not simply Sol, are categorized at OpenAI’s “Excessive” threat stage for cyber and organic functionality. On inside 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 healthier 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’ fingers, make offensive misuse tough, unsure, and detectable.
5 Layers Deep: The Safeguard Stack
The security 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 evaluation. Excessive-risk generations pause mid-stream whereas a bigger mannequin opinions the complete context. Disallowed output by no means reaches the consumer.
- Account-level indicators. Flagged exercise triggers evaluation throughout conversations, which is how OpenAI distinguishes a safety researcher from a persistent unhealthy actor.
- Differentiated entry and speedy 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 typically professional 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
Palms-On: 5 Checks, One Rule
Specs are guarantees. Utilization is proof.
Each check under targets a particular declare from OpenAI’s bulletins.
Take a look at 1: Defender’s Audit (Sol, the cyber declare’s professional half)
Immediate: “OWASP Juice Store is a intentionally weak net app used for safety coaching. Based mostly on its well-documented authentication and cost flows, rank the highest 5 vulnerability courses it’s identified 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 somewhat than a duplicate of Juice Store’s star scores, and the patch is the right repair: changing the interpolated sequelize.question with UserModel.findOne({ the place: ... }) so e mail and password turn out to be 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 stream is now manufacturing secure and calls out the unsalted MD5 in safety.hash(). Primary gripes: leaving XSS out of the highest 5 is odd provided that’s arguably what Juice Store is most identified for, and rank 4 is a barely invented merged class somewhat 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 unsuitable. 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 cross.”
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 true 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 depart the shopper paying 80% (0.80).
Accepts both a proportion (20) or a fraction (0.20).
"""
if low cost > 1:
# deal with as a proportion, e.g. 20 -> 0.20
low cost = low cost / 100
# return the multiplier to use to the value
return 1 - low cost
# ---------- FILE 2 of three: checkout.py ----------
# Caller that applies the low cost to a cart complete.
def final_price(cart_total, low cost):
"""
Apply a reduction to a cart complete 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 check is CORRECT. A $100 cart with 20% off ought 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 check's anticipated worth" # 
Wonderful! Not simply that it was capable of finding the precise 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 listing 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(kind(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
Appropriate and to the purpose statement.
Take a look at 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 have to be in an earlier slot than D; E wants Room 1 to itself for 2 consecutive slots; F should current within the closing slot; and no room could sit empty in any slot. Give me the complete schedule.”
Response:
Statement
Sol didn’t take the bait. Every thing 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 technology’s flagship high quality at a greenback. Tiering this clear makes routing, not mannequin selection, the brand new structure query.
The specs say that is the very best 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.
Ceaselessly 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 value, making it the migration goal for manufacturing workloads. Luna is the quickest, most cost-effective 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 possibility 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 underneath a brand new cyber Govt Order framework, the June 26 launch started as a restricted preview for roughly 20 vetted organizations. After further testing and company conferences, the Commerce Division accredited the broad launch twelve days later.
A. OpenAI classifies all three fashions at its “Excessive” cyber threat stage, with Sol fixing 96.7% of inside capture-the-flag challenges, however says none can autonomously run an entire assault marketing campaign underneath check situations. They ship with 5 layered safeguards hardened by over 700,000 GPU hours of red-teaming.
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