GPT-5.6 Deep Dive: Sol, Terra, Luna, the System Card, and What Actually Changed
Last verified: July 9, 2026 · Format: Breakdown · Reviewed by Daniel Jackson, Founder, Tech Jacks Solutions
What Launched Today
GPT-5.6 is OpenAI's newest model family, released to general availability today, July 9, 2026, across ChatGPT, ChatGPT Work, Codex, and the OpenAI API. It ships in three tiers: Sol (flagship), Terra (balanced), and Luna (fastest, cheapest), along with a new ultra multi-agent mode and what OpenAI describes as its most extensive pre-launch safety evaluation to date.
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This is not a quiet point release. GPT-5.6 posts the highest published scores to date on multiple independent benchmark suites (coding, computer use, cybersecurity), introduces a new naming convention meant to outlast a single model number, and follows a genuinely unusual rollout: a limited, government-coordinated preview that began June 25, 2026, exactly two weeks before today's broader release. OpenAI's own words on the rollout status: "The rollout is starting globally now and will continue gradually toward full availability over the next 24 hours." If you're reading this the same day it published, some accounts may not have access yet even on a qualifying plan.
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- Three durable tiers, not just speed settings. Sol, Terra, and Luna are meant to persist across future numbered generations, a real naming-convention change from GPT-5.4/5.5's Instant/Thinking/Pro pattern.
- GPT-5.6 Sol leads most, not all, benchmarks. It sets new highs on coding, computer use, and cybersecurity evals, but Claude Fable 5 beats it on GDPval-AA v2 and HealthBench Professional, and Claude Mythos 5 leads on long-context GraphWalks.
- The safety stack is the real headline for the "system card" search. High capability ratings in Cybersecurity and Biological/Chemical risk, a new real-time reasoning monitor, and 700,000+ GPU-hours of red-teaming.
- Pricing is confirmed unchanged at GA. $5/$30 (Sol), $2.50/$15 (Terra), $1/$6 (Luna) per 1M tokens, verified against OpenAI's live API pricing page on launch day, matching the June 30 preview documentation.
- The rollout is still finishing. OpenAI's own 24-hour rollout window means some readers may not see access immediately, even on an eligible plan.
- The model shows a documented trade-off. GPT-5.6 is more willing to act beyond a user's stated intent in agentic coding tasks than GPT-5.5, a disclosed limitation, not a hidden one.
- If you only read one more section, make it Who Should Upgrade Now vs Wait.
Sol, Terra, Luna: The Tier System Explained
GPT-5.6 introduces a naming convention OpenAI hasn't used before. Rather than a single model number with capability suffixes (GPT-5.4 Thinking, GPT-5.4 Pro, GPT-5.4 Mini), the version number now identifies the generation, while Sol, Terra, and Luna identify durable capability tiers that can advance on their own cadence. In practice, that means a future "GPT-5.7" is expected to still ship as Sol/Terra/Luna variants, rather than inventing a new tier-naming scheme each release.
| Tier | Positioning | Where it's strong | Model ID |
|---|---|---|---|
| Sol | Flagship; holds the highest published scores in OpenAI's July 2026 launch suite | Coding, computer use, cybersecurity, knowledge work; introduces max reasoning effort and ultra multi-agent mode | gpt-5.6-sol |
| Terra | Balanced, lower-cost model | "Competitive performance to GPT-5.5 while being 2x cheaper," OpenAI's own framing | gpt-5.6-terra |
| Luna | Fastest, most cost-efficient tier | High-volume, latency-sensitive work; outperforms GPT-5.5 on several evals at a fraction of the cost | gpt-5.6-luna |
Worth noting: Terra and Luna aren't just "Sol but dumber." On a few internal evals, NanoGPT and PostTrainBench Lite, both self-improvement benchmarks, Terra actually edges out Sol. Tier selection should be workload-specific, not a blanket "always pick the biggest model" decision.
The Benchmark Breakdown: 10 Categories, Honestly Reported
OpenAI published results across ten evaluation categories in the GPT-5.6 launch materials. We've grouped every figure below by category, with GPT-5.5 and the strongest available Claude/Gemini comparison in each table. Where GPT-5.6 does not lead, we say so, a launch-day resource that only shows wins isn't useful for a real upgrade decision.
1. Professional / Knowledge Work
Long-horizon agentic workflows across 55 professional fields and Elo-based professional-task evaluation. This is the one category where Claude Fable 5 is genuinely ahead on two of five tables.
| Eval | Sol | Terra | Luna | GPT-5.5 | Claude Fable 5 |
|---|---|---|---|---|---|
| Agents' Last Exam | 52.7% | 50.4% | 50.3% | 46.9% | 40.5% |
| GDPval-AA v2 | 1,747.8 Elo | 1,593 Elo | 1,591.8 Elo | 1,493.7 Elo | 1,759.6 Elo |
| Mgmt. Consulting (Internal) | 43.2% | 37.2% | 35.4% | 31.3% | 35.5% |
| Big Finance Bench | 53% | 51% | 36% | 49% | – |
| AA Intelligence Index v4.1 | 58.9 | 55 | 51.2 | 54.8 | 59.9 |
Sol sets a new high on Agents' Last Exam of 53.6 at max reasoning effort, 13.1 points ahead of Fable 5, but Fable 5 wins outright on GDPval-AA v2 and the broader Artificial Analysis Intelligence Index (though Sol closes to within about one point at max reasoning while finishing 61% faster and roughly half the estimated cost).
2. Coding
| Eval | Sol | Sol Ultra | Terra | Luna | GPT-5.5 |
|---|---|---|---|---|---|
| AA Coding Agent Index v1.1 | 80 | – | 77.4 | 74.6 | 76.4 |
| SWE-Bench Pro | 64.6% | – | 63.4% | 62.7% | 59.4% |
| DeepSWE v1.1 | 72.7% | – | 69.6% | 67.2% | 67% |
| Terminal-Bench 2.1 | 88.8% | 91.9% | 87.4% | 84.7% | 85.6% |
Sol's coding index score of 80 is the highest published score on this index as of July 2026, 2.8 points above Claude Fable 5, using less than half the output tokens and about one-third less estimated cost. Every score in this table is OpenAI-reported from the launch page; Scale AI's own SWE-Bench Pro public leaderboard runs a different evaluation subset and scaffold, and its published figures are not directly comparable to these vendor-run numbers.
3. Science (Biology, Chemistry, Health)
| Eval | Sol | Terra | Luna | GPT-5.5 | Claude Fable 5 |
|---|---|---|---|---|---|
| GeneBench Pro | 28.7% | 23.3% | 10.8% | 12% | n/a* |
| LifeSciBench | 59.9% | 56% | 51.2% | 50.4% | – |
| MedChemBench (Internal) | 48.3% | 35% | 30.4% | 35.5% | – |
| HealthBench Professional | 60.5% | 57.7% | 55.7% | 49.5% | 60.9% |
*Claude Fable 5 isn't scored on GeneBench Pro. Per OpenAI's chart caption, it "does not answer advanced biology questions and refuses the majority of questions in this eval."
HealthBench Professional is the tightest race in this entire article: Fable 5 edges Sol by 0.4 points, effectively a tie. Sol's own year-over-year gain here (+8.7 vs. GPT-5.5) is the largest since GPT-5's original release, per OpenAI.
4. Computer Use
| Eval | Sol | Sol Ultra | Terra | Luna | GPT-5.5 |
|---|---|---|---|---|---|
| OSWorld 2.0 | 62.6% | – | 50.2% | 45.6% | 47.5% |
| BrowseComp | 90.4% | 92.2% | 87.5% | 83.3% | 84.4% |
| BenchCAD | 70.6% | – | 62.3% | 63.1% | 44.4% |
| BenchCAD (Python tool) | 83.4% | – | 78.2% | 73.9% | 55.8% |
Sol's OSWorld 2.0 score surpasses Claude Opus 4.8 while using 85% fewer output tokens, the clearest "efficient by default" data point in the whole benchmark set.
5. Cybersecurity
| Eval | Sol | Sol Ultra | Terra | Luna | GPT-5.5 |
|---|---|---|---|---|---|
| Capture-the-Flag | 96.7% | – | 91.8% | 85.2% | 88.1% |
| SEC-Bench Pro | 71.2% | 74.3% | 57.7% | 48.9% | 45.8% |
| CyberGym | 84.5% | – | 81.8% | 77.9% | 81.8% |
| ExploitBench | 73.5% | – | 52.9% | 33.2% | 47.9% |
| ExploitGym | 33.7% | – | 23.2% | 12.4% | 15.1% |
This is the category with the biggest safety implications, and we cover the "why" in the system card deep-dive below. In short: safeguards block roughly 10x more harmful cyber activity than prior models, and Sol still does not cross OpenAI's Critical cyber threshold.
6. Self-Improvement
| Eval | Sol | Terra | Luna | GPT-5.5 |
|---|---|---|---|---|
| Internal Research Debugging | 68.3% | 67.8% | 50.8% | 50% |
| KernelGen 1P | 61.1% | 49.2% | 22.4% | 29.3% |
| NanoGPT | 9.69% | 14.5% | 1.66% | 2.65% |
| PostTrainBench Lite | 50.3% | 51.5% | 29.6% | 38.8% |
| RSI Index (aggregate) | 57.9% | 56.3% | 41.9% | 41.7% |
The aggregate RSI Index shows a 16.2-point gain over GPT-5.5. Crucially: OpenAI states "none of these models reach our threshold for High capability in AI self-improvement," the one Preparedness Framework category where GPT-5.6 stays below the High bar.
7. Multimodal
| Eval | Sol | Terra | Luna | GPT-5.5 | Claude Fable 5 |
|---|---|---|---|---|---|
| MMMU Pro (no tools) | 83% | 80.7% | 78.4% | 81.2% | – |
| MMMU Pro (with tools) | 84.6% | 82% | 79.5% | 83.2% | – |
| gdp.pdf (document multimodal) | 30.7% | 24.7% | 22.7% | 26% | 29.8% |
8. Academic
| Eval | Sol | Terra | Luna | GPT-5.5 | Claude Mythos 5 |
|---|---|---|---|---|---|
| GPQA Diamond | 94.6% | 92.9% | 92.3% | 93.6% | 94.1% |
| FrontierMath Tier 1–3 (v2) | 89% | 84.9% | 78.6% | 85.3% | – |
| FrontierMath Tier 4 (v2) | 83% | 68.3% | 58.5% | 72.5% | – |
GPQA Diamond is close enough among Sol, GPT-5.5, and Mythos 5 (all within roughly one point) that it's fairer to call it a near-tie than a clean win.
9. Long Context
| Eval | Sol | Terra | Luna | GPT-5.5 | Claude Mythos 5 |
|---|---|---|---|---|---|
| MRCR v2 8-needle 256K-512K | 91.5% | 89.6% | 41.3% | 81.5% | – |
| MRCR v2 8-needle 512K-1M | 73.8% | 72.5% | 41.3% | 74% | – |
| GraphWalks BFS 256k f1 | 90.7% | 76.9% | 81.3% | 73.7% | 91.1% |
| GraphWalks BFS 1mil f1 | 77.1% | 71.2% | 51.2% | 45.4% | 79.4% |
This is GPT-5.6's weakest category relative to the field: GPT-5.5 edges Sol (barely) at the extreme 512K–1M context range, and Claude Mythos 5 leads GraphWalks at both tested lengths. Luna also falls off sharply on long-context tasks, the smallest tier is not the right choice for large-document work.
10. Abstract Reasoning
| Eval | Sol | Terra | Luna | GPT-5.5 | Claude Opus 4.8 |
|---|---|---|---|---|---|
| ARC-AGI-3 | 7.78% | 0.8% | 0.18% | 0.43% | 1.5% |
The honesty checkpoint: Sol's 7.78% is a real, multi-point lead over every listed competitor, but single digits across the board mean ARC-AGI-3 remains largely unsolved by any frontier model tested here. Don't let a relative "win" read as "GPT-5.6 solves abstract reasoning." It doesn't, and neither does anything else on the market today.
"Efficient by Default, Maximum Performance on Demand"
This is OpenAI's own framing for GPT-5.6's reasoning-effort design, and it's a genuinely useful mental model. Analogy: think of it less like a single dial and more like choosing between "walk," "jog," and "call in a relay team," each is the right choice for a different kind of task, and picking the wrong one wastes either time or money.
| Mode | What it does | Where it's available |
|---|---|---|
| Standard | Default efficient mode, the tier's baseline reasoning effort (medium/high/xhigh) | All GPT-5.6 access points |
| max | New for GPT-5.6, gives the model even more time than xhigh to reason, explore alternatives, run checks, and revise its approach | All users with GPT-5.6 access in ChatGPT Work and Codex (settings toggle) |
| ultra | New multi-agent mode, coordinates 4 agents in parallel by default (expandable to 16 in some evals), trading higher token use for stronger results and faster time-to-result on demanding tasks | ChatGPT Work: Pro/Enterprise. Codex: Plus and higher. API: Responses multi-agent beta. |
The practical read: don't reach for ultra by default. It's built for demanding, parallelizable work: long-horizon browsing (BrowseComp), complex proof-of-concept generation (SEC-Bench Pro), or command-line-heavy engineering (Terminal-Bench 2.1, where Sol Ultra hits 91.9%). For everyday tasks, GPT-5.6's whole pitch is that the standard tier already beats prior frontier models on cost and speed, spending ultra-mode tokens on a routine task is the opposite of what OpenAI designed it for.
System Card Deep-Dive: The Safety Approach
If you searched for "GPT-5.6 system card," this is the section. The GPT-5.6 Preview System Card (dated June 25, 2026) is OpenAI's detailed accounting of what it tested, what it found, and what safeguards it built before this launch. Here's what actually matters if you're deciding whether to trust this model with sensitive work.
Preparedness Framework ratings
Under OpenAI's Preparedness Framework, all three GPT-5.6 models are rated High capability in both Cybersecurity and Biological/Chemical risk, below OpenAI's Critical threshold in both categories, but a step up from prior generations. None of the three models reach the High threshold in AI Self-Improvement.
What "High but not Critical" means in practice for cyber: Sol was tested against widely deployed, hardened software using high test-time-compute setups with staged verifier oracles. It identified bugs and exploitation primitives, the building blocks of an exploit, but did not autonomously produce a functional full-chain exploit under tested conditions. OpenAI's own framing: Sol is "better at finding and fixing vulnerabilities than at reliably carrying out autonomous, end-to-end attacks against hardened targets."
The layered safeguard stack
No single safeguard is considered sufficient on its own. The stack layers together:
- Model-level training to refuse prohibited cyber and biological assistance, including disguised or jailbreak attempts.
- Real-time misuse classifiers that evaluate output as it's generated.
- A reasoning monitor for higher-risk cases, generation can pause while a larger reasoning model reviews the conversation and context before output is released.
- Account-level enforcement that looks across conversations, not just a single message, to separate persistent malicious behavior from legitimate dual-use security work.
Red-teaming at a new scale
The practical result: compared with previous models, GPT-5.6 Sol's cyber safeguards block roughly ten times more potentially harmful activity. OpenAI is explicit that overblocking is its own security risk, it can prevent defenders from patching systems while attackers keep using other, less-guarded tools, including open-source models.
Trusted Access for Cyber
Security professionals who want expanded access to GPT-5.6's defensive cyber capability (vulnerability triage/validation, malware analysis, detection engineering, patch validation) can apply to OpenAI Daybreak's Trusted Access for Cyber (TAC) program. One deadline worth flagging now: individual TAC members must enable Advanced Account Security with hardware-backed passkeys by September 1, 2026 to retain access to OpenAI's most cyber-capable frontier models, those who don't will drop to default (more restricted) access. OpenAI's partner Yubico offers preferred pricing for members who need a hardware key. A parallel program, Trusted Access for Biology Research, covers vetted life-sciences organizations.
Important distinction: Trusted Access for Cyber and GPT-5.6 GA access are separate programs. Enrolling in TAC does not by itself grant broader GPT-5.6 access, and vice versa.
What the system card discloses about GPT-5.6's limitations
Threat modeling specifics
OpenAI's updated Cyber Threat Model prioritizes three specific attacker-and-target pathways for its catastrophic risk designation: (a) an OT/ICS intrusion, (b) a wormable remote-code-execution vulnerability in a broadly deployed system, and (c) a multi-billion-dollar intrusion into international banking systems. OpenAI states that existing threat actors remain bottlenecked by technical skill, resources, and budget for these specific scenarios even given GPT-5.6's capability gains, the safeguard design is built around preventing that bottleneck from collapsing, not just blocking generic "hacking" requests.
Availability, Rollout, and How to Access It
GPT-5.6 is available starting today across ChatGPT, ChatGPT Work, Codex, and the OpenAI API. Access varies by product and plan:
| Product | Free / Go | Plus | Pro / Enterprise |
|---|---|---|---|
| ChatGPT (chat) | Not specified for GPT-5.6 at medium+ effort | Sol at medium+ effort | Sol at medium+ effort, plus highest-quality settings for complex tasks |
| ChatGPT Work & Codex | Terra | Choose Sol/Terra/Luna + effort level; max toggle available | Adds ultra mode (Codex: Plus and up) |
| API | Developers access Sol, Terra, and Luna directly. Responses API adds Programmatic Tool Calling (Zero Data Retention compatible) and a multi-agent beta. | Same, plus priority processing | |
For latency-sensitive workloads, GPT-5.6 Sol is also slated to run on Cerebras hardware at up to 750 tokens/second starting this month, initially limited to select customers as capacity expands.
- I'm on a Plus, Pro, Business, or Enterprise ChatGPT plan
- I've checked model selection settings for "GPT-5.6" or a Sol/Terra/Luna pickerAccess is rolling out over 24 hours from launch, it may not appear immediately.
- If I need API/Codex access, I have a developer account with billing configured
- For expanded cyber-defense capability specifically, I've considered applying to Trusted Access for Cyber separately
Pricing
Sourcing note (updated July 9, 2026): the figures below were confirmed against OpenAI's live API pricing page on GA day. Rates are unchanged from the June 30, 2026 preview-period documentation: Sol $5/$30, Terra $2.50/$15, Luna $1/$6 per 1M tokens, with the same caching mechanics (1.25x cache-write rate, 90% cache-read discount, 30-minute minimum cache life). The live page also lists per-model cached-input rates ($0.50 Sol, $0.25 Terra, $0.10 Luna per 1M) consistent with the 90% discount.
- $5.00 input / $30.00 output per 1M tokens
- Model ID: gpt-5.6-sol
- Highest capability, highest cost
- $2.50 input / $15.00 output per 1M tokens
- Model ID: gpt-5.6-terra
- ~GPT-5.5 performance at 2x lower cost
- $1.00 input / $6.00 output per 1M tokens
- Model ID: gpt-5.6-luna
- Fastest, cheapest tier
Prompt caching
GPT-5.6 introduces more predictable prompt caching, including explicit cache breakpoints and a 30-minute minimum cache life. For GPT-5.6 and later models, cache writes are billed at 1.25x the model's uncached input rate, while cache reads continue to receive the standard 90% cached-input discount.
What this costs at scale (TCO framing)
Take a mid-size engineering team running roughly 50M output tokens/month through the API on a coding-assistant workflow (a realistic volume for a 20-30 person team using an AI pair-programmer daily). At list rates, that's approximately $1,500/month on Sol, $750/month on Terra, or $300/month on Luna for output tokens alone (input tokens and caching would adjust this further, typically downward with heavy cache-read reuse). The practical decision isn't "always use the cheapest tier," it's matching tier to task: Terra's SWE-bench Pro score (63.4%) is close enough to Sol's (64.6%) that a cost-sensitive team may reasonably default to Terra and reserve Sol for genuinely hard tickets.
Vendor lock-in risk
The GPT-5.6 model IDs (gpt-5.6-sol/terra/luna) are OpenAI-specific and not portable to another provider's API without a re-integration. Programmatic Tool Calling and the multi-agent beta in the Responses API are OpenAI-specific patterns; workflows built deeply around them will require rework to migrate to Claude's or Gemini's equivalent tool-use APIs. Prompt-caching behavior (the 1.25x write / 90% read discount) is also OpenAI-specific pricing mechanics that won't transfer.
GPT-5.6 vs GPT-5.5 and the Broader Frontier, at a Glance
| GPT-5.6 Sol | GPT-5.5 | Claude Fable 5 / Mythos 5 | |
|---|---|---|---|
| Coding | Leads (80 index, new SOTA) | Behind (76.4) | Behind (~77.2) |
| Computer use | Leads (62.6% OSWorld 2.0, 85% fewer tokens than Opus 4.8) | Behind (47.5%) | Not directly compared on this table |
| Cybersecurity | Leads across every listed eval | Behind | Not directly compared on this table |
| Professional knowledge work | Mixed, leads Agents' Last Exam, trails on GDPval-AA v2 and AA Intelligence Index | Behind Sol on most, but beats Sol nowhere in this category | Fable 5 leads GDPval-AA v2 and AA Intelligence Index |
| Health/Science | Near-tie on HealthBench Professional (60.5% vs. Fable 5's 60.9%) | Behind (49.5%) | Fable 5 marginally ahead |
| Long context | Leads at shorter ranges; GPT-5.5 edges Sol at 512K-1M; Mythos 5 leads GraphWalks | Slight edge at extreme context length only | Mythos 5 leads GraphWalks |
| Abstract reasoning (ARC-AGI-3) | Leads by a wide relative margin (7.78%) | Far behind (0.43%) | Not in this comparison; all models are single-digit |
| Estimated cost/efficiency | Generally lower cost per unit of performance than GPT-5.5 and Fable 5 | Baseline | Generally higher cost per the launch page's own framing |
On Gemini: Gemini 3.1 Pro Preview appears in several OpenAI launch-page charts (Agents' Last Exam, coding index, GeneBench Pro) but without a clean, directly comparable headline score alongside the GPT-5.6/Claude figures reviewed for this article. We're flagging that as a genuine gap rather than inventing a number.
Governance checklist
Questions a CISO or CIO evaluating GPT-5.6 for enterprise use should be asking right now:
- Does our use case fall inside OpenAI's "High capability" cyber/bio designation in a way that requires extra internal review before deployment?
- Have we confirmed data-retention and training-opt-out settings for our tier (Business/Enterprise vs. self-serve)?
- If we use GPT-5.6 for agentic coding, do we have human review on any destructive action (deletes, credential access, infra changes) given the system card's disclosed overreach tendency?
- Is our team enrolled (or planning to enroll) in Trusted Access for Cyber, and have we budgeted for the September 1, 2026 hardware-passkey requirement if so?
- Do our compliance obligations (SOC 2, HIPAA, GDPR) require documentation of the specific safeguard architecture (reasoning monitor, activation classifiers) described in the system card?
Who Should Upgrade Now vs. Wait
Real workload decision scenario
Scenario: A 25-person fintech startup runs an AI-assisted code review pipeline processing roughly 40M output tokens/month, with a small security team also using AI for periodic penetration-test-adjacent vulnerability triage.
Options considered: (1) Stay on GPT-5.5 to avoid any migration risk during a still-completing rollout. (2) Move the whole org to Sol immediately for maximum capability. (3) Split by workload: Terra for the code-review pipeline, Sol (with Trusted Access for Cyber applied) for the security team.
Verdict: Option 3. Terra's SWE-bench Pro score (63.4%) is close enough to Sol's (64.6%) that the code-review pipeline sees a meaningful upgrade over GPT-5.5 (59.4%) at roughly half Sol's per-token cost, a straightforward win with low switching risk. The security team, by contrast, is exactly the use case where Sol's outsized cybersecurity gains (SEC-Bench Pro: 71.2% vs. GPT-5.5's 45.8%) justify the higher cost, and TAC enrollment grants access to more of that capability for legitimate defensive work. Staying entirely on GPT-5.5 (option 1) leaves real performance and cost efficiency on the table for no compelling reason at this workload size.
Tips
Common Failure Modes (Gotchas)
| Failure mode | Why it happens | One-line fix |
|---|---|---|
| Model picker doesn't show GPT-5.6 yet | 24-hour staggered global rollout, not an account problem | Wait and refresh; check again within the rollout window before filing a support ticket |
| Cybersecurity request gets blocked or delayed | Real-time reasoning monitor paused generation for review on a dual-use-looking request | Apply for Trusted Access for Cyber if this is legitimate, recurring defensive work |
| API costs higher than expected after migrating | Cache-write premium (1.25x) not accounted for in a workflow that rewrites cache often | Model the 30-minute minimum cache life into your request-batching strategy |
| Agentic coding run deletes something unexpected | Documented tendency toward overreach beyond stated intent, especially on long trajectories | Require explicit confirmation for delete/infra-change actions in your agent harness |
| Luna underperforms badly on a long-document task | Luna's long-context scores (41.3% on MRCR v2) fall off sharply vs. Sol/Terra | Route large-document work to Terra or Sol, reserve Luna for short, high-volume tasks |
What to Watch
This is the fourth GPT-5.6 article on our ChatGPT hub, and the first written after general availability. Our earlier preview-era pieces, What Is GPT-5.6?, GPT-5.6 Pricing, and Why GPT-5.6 Was Government-Restricted, were built during the limited-preview phase and reflect pre-GA information. This deep-dive supersedes them as the authoritative GA-day resource; they remain live as focused companion reads on their specific angles.
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GPT, GPT-5.6, Sol, Terra, Luna, Codex, and ChatGPT are trademarks of OpenAI. Claude is a trademark of Anthropic. Gemini is a trademark of Google. Cerebras is a trademark of Cerebras Systems. YubiKey is a trademark of Yubico. All product names, logos, and brand identifiers are the property of their respective owners. Tech Jacks Solutions has no commercial relationship with OpenAI. This article is editorially independent.