GPT-5.6 Sol vs Terra vs Luna Explained: Speed, API Cost, Reasoning Depth, Performance Differences, and Which Model to Choose

GPT-5.6 Sol, Terra, and Luna belong to the same OpenAI model generation, although they occupy different capability tiers whose prices, response characteristics, and recommended workloads allow organizations to separate difficult professional reasoning from routine production processing and high-volume automation.

All three models provide a 1,050,000-token context window, support as many as 128,000 output tokens, share the same February 16, 2026 knowledge cutoff, accept text and image input, and offer the same reasoning-effort range from nonethrough max.

The principal difference therefore does not concern which model accepts the largest prompt or exposes the longest list of tools, because the distinction appears in the quality of the underlying model, the expected response speed, the token price, and the complexity that each tier can handle reliably before retries or human corrections become necessary.

Sol provides the highest capability for difficult coding, research, computer use, science, cybersecurity, design, and consequential professional analysis, while Terra offers a lower-cost balance for normal production work and Luna concentrates on fast, inexpensive processing at substantial scale.

The appropriate model should consequently be selected according to the cost of an incorrect or incomplete result rather than according to model prestige, because assigning every request to Sol creates unnecessary expense while forcing complex work onto Luna may produce retries whose total cost exceeds the original saving.

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The Three GPT-5.6 Models Share One Technical Foundation While Serving Different Capability Tiers.

OpenAI structures GPT-5.6 around Sol, Terra, and Luna, with Sol acting as the flagship model, Terra serving as the balanced production tier, and Luna providing the fastest and least expensive option for predictable or repetitive workloads.

The unsuffixed gpt-5.6 API alias resolves to GPT-5.6 Sol, which means that developers who request the generation without specifying a tier receive the family’s highest-capability model rather than the middle or lowest-cost option.

Terra and Luna are complete models within the same generation rather than temporary reductions in Sol’s reasoning effort, so choosing Luna at max does not convert it into Sol and selecting Sol at none does not reduce it to the same underlying capability as Luna.

The shared generation gives organizations a consistent API structure, tool set, context limit, and reasoning interface across all three models, which allows requests to move between tiers without requiring a separate application architecture for every workload.

A production system can consequently assign classification and extraction to Luna, route ordinary professional analysis to Terra, and reserve Sol for difficult exceptions while preserving the same tool definitions, structured-output schemas, and conversation format.

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Core GPT-5.6 Model Differences.

Area	GPT-5.6 Sol	GPT-5.6 Terra	GPT-5.6 Luna
API identifier	gpt-5.6-sol	gpt-5.6-terra	gpt-5.6-luna
Position in the family	Flagship capability tier	Balanced production tier	Fastest and lowest-cost tier
Context window	1,050,000 tokens	1,050,000 tokens	1,050,000 tokens
Maximum output	128,000 tokens	128,000 tokens	128,000 tokens
Knowledge cutoff	February 16, 2026	February 16, 2026	February 16, 2026
Reasoning range	none through max	none through max	none through max
Text input	Supported	Supported	Supported
Image input	Supported	Supported	Supported
Function calling	Supported	Supported	Supported
Structured outputs	Supported	Supported	Supported
Main workload	Difficult professional and technical work	Everyday production applications	Predictable high-volume processing

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Luna Provides the Highest Speed Priority While Terra Balances Latency and Sol Prioritizes Capability.

OpenAI identifies Luna as the fastest GPT-5.6 tier, while Terra is designed to balance intelligence, response speed, and operating cost and Sol places greater emphasis on the quality of the completed result.

The speed comparison should not be interpreted as a guaranteed response time measured in fixed milliseconds, because actual latency depends on prompt length, output length, reasoning effort, tool usage, service tier, provider load, and whether the request activates Pro or multi-agent execution.

A Luna request using max reasoning may take longer than a Sol request using none, while a Sol response that invokes several tools and performs extensive verification may require considerably more time than a direct Terra answer.

Time to first token and total completion time may also move differently, because a model can begin streaming quickly while producing a long answer slowly, whereas another configuration may spend more time reasoning before returning a concise final result.

Priority processing can reduce infrastructure-related delay for applications that accept twice the standard token prices, while Batch and Flex processing reduce cost for workloads whose completion does not need to occur interactively.

Speed should therefore be evaluated through representative requests whose prompts, tools, outputs, and reasoning settings match the final application rather than through one short conversational test.

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Practical Speed Positioning Across the GPT-5.6 Family.

Speed Consideration	GPT-5.6 Sol	GPT-5.6 Terra	GPT-5.6 Luna
Relative positioning	Capability-first	Balanced	Fastest
Best interactive role	Difficult analysis requiring high confidence	General professional and agent workflows	Narrow and predictable user-facing requests
Effect of high reasoning	Can become substantially slower during deep verification	Adds moderate latency to balanced execution	May remove part of Luna’s normal speed advantage
Effect of tools	Longer when several operations and checks are required	Suitable for normal multi-tool production work	Suitable for simple and deterministic tool sequences
High-volume suitability	Expensive for routine traffic	Appropriate when Luna quality is insufficient	Designed for large request volumes
Priority processing	Available at higher rates	Available at higher rates	Available at higher rates
Batch and Flex use	Expensive but suitable for high-value offline work	Balanced offline processing	Lowest-cost large-scale processing

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Reasoning Depth Depends on Both the Effort Setting and the Capability of the Selected Model.

Sol, Terra, and Luna support none, low, medium, high, xhigh, and max, which allows developers to control how much reasoning the selected model performs without changing its identifier.

The effort setting determines the amount of analytical work assigned to the request, although the underlying model determines the quality ceiling within which that work occurs.

Sol at medium retains the reasoning capacity of the flagship model even though it spends less time exploring than Sol at max, while Luna at max may investigate more thoroughly than Luna at medium without matching Sol on difficult scientific, security, computer-use, or long-context tasks.

The none setting suits extraction, rewriting, classification, routing, schema conversion, and predictable tool selection, because those operations often benefit more from low latency and stable formatting than from prolonged internal analysis.

Medium and high reasoning fit most professional workflows, while xhigh and max should be reserved for assignments in which additional exploration, verification, and correction produce a measurable increase in accepted results.

Raising every request to max can increase latency and token consumption without improving a bounded task, which makes reasoning effort another routing variable rather than a universal quality switch.

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GPT-5.6 Reasoning Effort by Workload Type.

Reasoning Effort	Appropriate Work	Typical Operational Effect
none	Extraction, formatting, classification, routing, and schema conversion	Lowest reasoning latency and expenditure
low	Straightforward summaries, bounded coding, and simple document processing	Limited analysis with controlled cost
medium	General professional work, routine agents, and ordinary research	Balanced reasoning and response time
high	Difficult coding, planning, synthesis, and multi-step tool workflows	Greater investigation and verification
xhigh	Architecture, consequential analysis, advanced debugging, and complex review	Extensive reasoning with higher latency
max	Hardest quality-first requests where failure costs exceed token expense	Highest standard reasoning allocation
Pro mode	Additional aggregated work before one final answer	Greater token use and longer completion time
Multi-agent execution	Parallel investigation across independent workstreams	Higher aggregate cost with potentially shorter elapsed time

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Pro Mode Adds More Computation Without Removing the Differences Between Sol, Terra, and Luna.

GPT-5.6 Pro is an execution mode rather than a separate model identifier, which means that developers retain Sol, Terra, or Luna as the selected model while enabling additional model work through the Responses API.

A Terra Pro request therefore performs more aggregation and verification than an ordinary Terra request, although it remains based on Terra rather than inheriting Sol’s complete capability profile.

Luna Pro can provide additional checking for inexpensive workflows whose individual decisions matter, although difficult work may still require escalation because greater computation cannot fully compensate for a lower underlying capability tier.

Sol Pro represents the highest-quality single-model GPT-5.6 configuration, which makes it appropriate for difficult optimization, consequential professional review, complex debugging, and analytical assignments in which one carefully verified answer matters more than response speed.

The additional work is billed at the selected model’s ordinary token rates, which gives Terra Pro and Luna Pro lower per-token costs than Sol Pro even though their total token consumption may rise substantially compared with standard execution.

Pro mode should consequently be evaluated against a higher model tier, because Terra Pro may outperform ordinary Terra while still costing more than a concise Sol request that reaches the correct result without repeated aggregation.

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How Pro Mode Changes the Three GPT-5.6 Tiers.

Configuration	Practical Position	Appropriate Workload
Luna standard	Fast and inexpensive routine processing	Extraction, classification, routing, and predictable transformations
Luna Pro	Additional checking at Luna token rates	High-volume decisions that remain bounded but require more consistency
Terra standard	Balanced professional production	Writing, coding, document work, and operational agents
Terra Pro	Higher-reliability execution below Sol token rates	Consequential routine work that benefits from additional verification
Sol standard	Flagship reasoning and professional capability	Difficult coding, research, science, design, and computer use
Sol Pro	Highest-quality single-model GPT-5.6 execution	Complex final answers where failure and rework are expensive

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Standard API Pricing Creates a Consistent Ratio Between the Three Capability Tiers.

GPT-5.6 Sol costs $5 per million standard input tokens, $0.50 per million cached input tokens, $6.25 per million cache-write tokens, and $30 per million output tokens.

Terra costs exactly half as much as Sol across the same short-context categories, while Luna costs one fifth of Sol’s rates and two fifths of Terra’s rates.

The ratio allows organizations to estimate the immediate price effect of model routing, because moving a request from Sol to Terra halves the token rate while moving from Sol to Luna reduces it by 80 percent.

Output pricing deserves particular attention because generated tokens are substantially more expensive than input tokens, which means that long reports, code files, structured records, and extensive explanations create larger model differences than short answers.

Prompt caching reduces repeated input costs when the same instructions, schemas, examples, or reference material are reused, although writing a new cached prefix creates a separate charge whose value depends on future reuse.

A cheaper model does not always produce the cheapest completed workflow, because failed attempts, repeated tool calls, human corrections, and later escalation may consume more than the initial saving.

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Standard Short-Context API Pricing per One Million Tokens.

Pricing Category	GPT-5.6 Sol	GPT-5.6 Terra	GPT-5.6 Luna
Input	$5.00	$2.50	$1.00
Cached input	$0.50	$0.25	$0.10
Cache write	$6.25	$3.125	$1.25
Output	$30.00	$15.00	$6.00
Relative cost against Sol	100%	50%	20%
Relative cost against Terra	200%	100%	40%

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Long-Context Pricing Increases the Complete Request Cost After 272,000 Input Tokens.

All three GPT-5.6 models support more than one million context tokens, although requests containing more than 272,000 input tokens move to long-context rates for the complete request.

Long-context input costs twice the ordinary short-context rate, while output rises to one and a half times the standard short-context price.

The threshold affects model selection because a request containing several hundred thousand tokens may become expensive even when most of the input is cached, while a long generated report adds the higher output rate on top of the expanded input charge.

Luna provides the lowest numerical long-context price, although its weaker performance on difficult retrieval and reasoning tasks may create missed evidence, incorrect connections, or additional synthesis passes.

Terra offers a middle position when large documents require substantial professional interpretation but do not justify Sol’s flagship rate, while Sol remains appropriate when a missed clause, scientific relationship, security issue, or architectural dependency would create a high correction cost.

Retrieval, segmentation, summarization, and explicit caching should therefore be compared with sending the complete collection, because a one-million-token capacity does not imply that every available document belongs in every request.

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Long-Context API Pricing per One Million Tokens.

Pricing Category	GPT-5.6 Sol	GPT-5.6 Terra	GPT-5.6 Luna
Input	$10.00	$5.00	$2.00
Cached input	$1.00	$0.50	$0.20
Cache write	$12.50	$6.25	$2.50
Output	$45.00	$22.50	$9.00
Long-context threshold	More than 272,000 input tokens	More than 272,000 input tokens	More than 272,000 input tokens
Maximum context	1,050,000 tokens	1,050,000 tokens	1,050,000 tokens

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Batch, Flex, and Priority Processing Change Latency and Cost Without Changing Model Capability.

Batch and Flex processing reduce GPT-5.6 token prices by half, which makes them suitable for document enrichment, evaluation, offline extraction, classification, synthetic-data preparation, and other workloads that do not require an immediate interactive response.

Priority processing doubles the ordinary short-context token rates while providing higher scheduling priority, which suits user-facing or operational systems whose waiting time carries greater business cost than the additional inference expense.

These service tiers do not convert Luna into Terra or Terra into Sol, because they change scheduling and billing conditions rather than the intelligence of the selected model.

A Luna Priority request remains based on Luna even though it costs the same short-context input and output rates as standard Terra, while a Terra Priority request reaches Sol’s standard rates without acquiring Sol’s flagship reasoning capability.

Batch Sol may cost the same per token as standard Terra, which creates an alternative for high-value offline work whose completion can be delayed but whose quality requirements remain high.

The service tier should consequently be chosen after the model tier, because paying Priority prices for an unsuitable model may produce faster delivery of an output that still requires correction.

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Short-Context Processing Rates per One Million Tokens.

Processing Tier	Sol Input	Sol Output	Terra Input	Terra Output	Luna Input	Luna Output
Batch or Flex	$2.50	$15.00	$1.25	$7.50	$0.50	$3.00
Standard	$5.00	$30.00	$2.50	$15.00	$1.00	$6.00
Priority	$10.00	$60.00	$5.00	$30.00	$2.00	$12.00

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Published Evaluations Show Small Differences on Some Tasks and Large Separations on Others.

OpenAI’s launch evaluations show that Terra and Luna remain close to Sol on several general coding and professional benchmarks, while the difference becomes wider in advanced mathematics, scientific reasoning, cybersecurity, computer use, browsing, and difficult long-context retrieval.

SWE-Bench Pro places all three models within a comparatively narrow range, which suggests that routine repository work may not require Sol when the task, tests, and acceptance criteria are well defined.

OSWorld, GeneBench Pro, FrontierMath, and SEC-Bench Pro produce larger separations, which indicates that Sol’s higher price becomes easier to justify when the work demands visual interaction, scientific interpretation, advanced quantitative reasoning, or security analysis.

Terra often retains much of Sol’s performance at half the token rate, which makes it a practical default for organizations that need broad GPT-5.6 capability without placing every production request on the flagship tier.

Luna remains competitive on some general tasks while falling more sharply on demanding long-context and specialist evaluations, which reinforces its role as a high-volume model rather than a universal replacement for Terra.

The figures should be treated as OpenAI’s controlled evaluations rather than as guarantees of production behavior, because real applications introduce different prompts, tools, context distributions, failure costs, and review standards.

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Selected OpenAI Evaluations Across Sol, Terra, and Luna.

Evaluation	GPT-5.6 Sol	GPT-5.6 Terra	GPT-5.6 Luna
Agents’ Last Exam	52.7%	50.4%	50.3%
GDPval-AA v2	1,747.8 Elo	1,593.0 Elo	1,591.8 Elo
Artificial Analysis Intelligence Index	58.9	55.0	51.2
SWE-Bench Pro	64.6%	63.4%	62.7%
Terminal-Bench 2.1	88.8%	87.4%	84.7%
BrowseComp	90.4%	87.5%	83.3%
OSWorld 2.0	62.6%	50.2%	45.6%
FrontierMath Tier 4	83.0%	68.3%	58.5%
GeneBench Pro	28.7%	23.3%	10.8%
SEC-Bench Pro	71.2%	57.7%	48.9%
OpenAI MRCR v2 at 512K–1M	73.8%	72.5%	41.3%
GraphWalks at 1M	77.1%	71.2%	51.2%

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Sol Should Be Chosen When Complexity, Ambiguity, or Failure Cost Exceeds the Price Difference.

GPT-5.6 Sol is the appropriate starting model when the request requires sustained judgment across several stages, particularly when the model must investigate incomplete evidence, operate unfamiliar interfaces, revise a plan after failures, or verify a consequential result.

Complex software architecture, repository-wide migrations, difficult debugging, advanced scientific work, cybersecurity analysis, high-value financial or legal review, and visual application design all fit Sol’s capability-first position.

Sol also suits workflows in which a lower-tier error creates substantial downstream work, because the difference between $15 and $30 per million output tokens may remain small compared with the cost of a missed security defect, incorrect contract interpretation, failed deployment, or unreliable financial conclusion.

Computer-use agents benefit from Sol when interfaces are ambiguous or change during the workflow, while Terra may remain adequate for stable applications whose controls and success conditions are already known.

Final review represents another suitable Sol role, because work initially completed by Terra or Luna can be escalated when the organization needs a more capable model to inspect assumptions, identify missed evidence, and verify the final deliverable.

Sol should not become the default for every short task, however, because predictable extraction or formatting rarely uses the capability that creates its higher price.

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Workloads That Most Strongly Justify GPT-5.6 Sol.

Workload	Why Sol Fits	Recommended Starting Effort
Complex software architecture	Several systems and constraints must be reconciled	high or xhigh
Repository-wide migration	Dependencies, sequencing, tests, and regressions require sustained planning	high
Difficult root-cause investigation	Competing hypotheses must be tested and revised	high or xhigh
Scientific analysis	Technical evidence and quantitative relationships require specialist reasoning	high
Cybersecurity review	Missed vulnerabilities and incorrect conclusions carry high consequence	high or xhigh
Consequential legal or financial analysis	Errors may create regulatory, contractual, or monetary exposure	high or Pro
Ambiguous computer use	The model must interpret changing interface states	high
Professional design and frontend review	Rendered output requires visual inspection and revision	high
Final verification of lower-tier work	A more capable model examines assumptions and missed issues	high or Pro

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Terra Provides the Broadest Balance for Everyday Professional and Production Work.

GPT-5.6 Terra is likely to serve as the most practical default for organizations that need substantial reasoning, coding, document, and tool-calling capability while controlling the cost of routine production traffic.

Its token rates are half of Sol’s, while OpenAI’s evaluations show that the performance gap remains comparatively narrow on several coding, agent, and general professional tasks.

Terra suits everyday analysis, professional writing, document and presentation production, routine coding, pull-request review, operational agents, customer service, spreadsheet work, and research whose sources and acceptance criteria are reasonably defined.

The model can also lead ordinary multi-tool workflows in which the sequence contains some ambiguity, while Luna handles predictable subtasks and Sol receives unresolved exceptions.

Terra’s $15-per-million output price creates substantial savings for applications that generate reports, correspondence, code, or structured records at scale, particularly when Sol’s additional capability would not change the acceptance rate.

A production team should move from Terra to Sol when repeated failures, unresolved contradictions, difficult visual reasoning, specialist scientific work, or high-consequence decisions demonstrate that the balanced tier is no longer sufficient.

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Workloads That Fit GPT-5.6 Terra.

Workload	Why Terra Fits	Recommended Starting Effort
Everyday professional writing	Substantial quality without flagship output pricing	medium
Routine coding and code review	Strong repository performance with controlled cost	high
Document generation	Handles structured reports, presentations, and business materials	medium or high
Spreadsheet analysis	Supports formulas, tables, explanations, and normal reconciliation	medium or high
Customer-service agents	Balances reasoning, tool use, and high request volume	medium
Operational workflows	Handles moderate ambiguity and several business tools	medium or high
Defined research tasks	Sources and completion criteria remain reasonably clear	high
Sol-led subagents	Completes substantial supporting work at lower rates	medium or high
Long-form production output	Cuts Sol output pricing by half	medium

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Luna Should Be Chosen When Volume, Predictability, and Response Speed Dominate the Workload.

GPT-5.6 Luna is designed for large numbers of requests whose instructions, output schemas, and success criteria are narrow enough that flagship reasoning would create unnecessary expense.

Classification, routing, extraction, normalization, short summaries, schema conversion, record enrichment, search-result filtering, and predictable tool selection fit Luna’s low-cost and high-speed position.

The model also works as a supporting agent inside larger workflows, because it can gather evidence, clean data, prepare records, or classify requests before Terra or Sol performs the main analysis.

Luna’s $6-per-million output price allows applications to produce large quantities of structured text at one fifth of Sol’s output rate, while its Batch price reduces the figure to $3 per million tokens for offline work.

The model requires additional evaluation when prompts contain subtle instructions, extensive contradictory documents, advanced technical reasoning, scientific evidence, cybersecurity analysis, or complex computer-use interactions.

A Luna-first system should establish escalation criteria before deployment, because low confidence, failed validation, repeated tool errors, or unusually large context may indicate that Terra or Sol will produce a lower cost per accepted result.

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Workloads That Fit GPT-5.6 Luna.

Workload	Why Luna Fits	Recommended Starting Effort
Classification	Narrow labels and stable schemas reward speed and low cost	none or low
Request routing	Determines which workflow or model should receive a task	none
Data extraction	Converts documents or messages into structured fields	none or low
Record normalization	Applies predictable formatting and schema rules	none
Short summarization	Produces concise outputs from bounded material	low
Search-result filtering	Removes irrelevant evidence before higher-tier analysis	low
Low-cost subagents	Collects or organizes supporting information	low or medium
Large batch processing	Handles offline transformations at the lowest family rates	none or low
Predictable tool selection	Invokes stable functions under defined conditions	low
High-volume user interaction	Serves narrow conversational flows with low latency	low

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A Cascading Router Can Combine All Three Models More Economically Than a Single-Tier Deployment.

The GPT-5.6 family is structured so that Sol, Terra, and Luna can participate in one application, which allows the system to spend flagship tokens only when the request demonstrates enough difficulty or consequence to justify them.

Luna can classify the request, extract initial information, and estimate complexity before routing ordinary professional work to Terra and sending specialist or unresolved cases to Sol.

Terra can complete the majority of production requests while escalating when validation fails, tool calls repeatedly return errors, conflicting evidence remains unresolved, or the task enters a high-risk category.

Sol can handle difficult exceptions and final consequential reviews, while lower tiers perform independent checks whose results are compared before the workflow is accepted.

Routing criteria may include file count, context size, requested output length, tool count, failed attempts, domain risk, regulatory consequence, confidence, and the estimated cost of human correction.

The system should measure completed-task economics rather than raw model prices, because three failed Luna attempts followed by a Terra correction may cost more than one successful Terra request, while sending every classification task to Sol wastes capability that does not affect the result.

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A Practical GPT-5.6 Cascading Workflow.

Workflow Stage	Recommended Model	Escalation Signal
Request classification	Luna	Ambiguous intent or high-risk subject
Initial extraction	Luna	Missing or contradictory fields
Preliminary research	Luna or Terra	Complex evidence relationships
Main professional analysis	Terra	Failed validation or unresolved contradictions
Routine implementation	Terra	Architecture or repository-wide impact
Difficult exception handling	Sol	Continued failure or highest-consequence decision
Final consequential review	Sol	Pro mode when one aggregated answer requires maximum verification
Independent low-cost check	Luna or Terra	Disagreement with the primary result
High-stakes unresolved case	Sol Pro	Human review when uncertainty remains

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Product Availability Differs Between Standard ChatGPT, ChatGPT Work, Codex, and the OpenAI API.

GPT-5.6 Sol is available in standard ChatGPT conversations through eligible reasoning settings, while Terra and Luna are not ordinary model-picker options in the main consumer chat experience.

GPT-5.5 Instant remains the everyday ChatGPT default, while Sol powers Medium and higher reasoning choices for eligible paid users.

ChatGPT Work provides access to Sol, Terra, and Luna for eligible Plus, Pro, Business, and Enterprise users, allowing professional workflows to select the capability tier and reasoning effort according to the assignment.

Codex provides Terra to Free and Go users, while eligible paid users can access all three GPT-5.6 tiers for software-development work.

The OpenAI API exposes Sol, Terra, and Luna directly, which gives developers the greatest control over routing, effort, Pro mode, caching, service tiers, tool use, and cost monitoring.

Organizations should consequently distinguish between a model’s API availability and its presence in ordinary ChatGPT, because Terra and Luna can be commercially accessible without appearing as standard consumer-chat choices.

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GPT-5.6 Availability Across OpenAI Products.

Product	GPT-5.6 Sol	GPT-5.6 Terra	GPT-5.6 Luna
Standard ChatGPT	Available through eligible reasoning settings	Not selectable	Not selectable
ChatGPT Work	Available on eligible paid plans	Available on eligible paid plans	Available on eligible paid plans
Codex Free and Go	Not generally included	Available	Not generally included
Codex eligible paid plans	Available	Available	Available
OpenAI API	Available	Available	Available
Pro execution	Available through API configuration	Available through API configuration	Available through API configuration
Maximum reasoning effort	Available	Available	Available

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The Appropriate GPT-5.6 Model Depends on the Cost of Failure, Not Only the Price of Tokens.

Sol should be selected when the assignment requires the family’s highest reasoning capacity or when an incorrect result creates greater expense than the difference in model price.

Terra should serve as the default for most professional production applications because it preserves substantial GPT-5.6 capability while cutting Sol’s token rates in half.

Luna should handle predictable high-volume operations whose outputs can be validated cheaply, particularly when latency and unit cost determine whether the application remains economically viable.

Reasoning effort should be calibrated separately from the model tier, because selecting max on every request creates unnecessary latency while choosing a lower tier and compensating with more reasoning does not guarantee flagship-quality results.

Applications with mixed workloads should route requests dynamically, using Luna for routine transformations, Terra for most analysis and implementation, and Sol for difficult exceptions, specialist domains, or final review.

The final decision should be based on cost per accepted completion, which combines token charges, tool usage, latency, retries, failed validation, human correction, and the operational consequence of an incorrect result.

A disciplined GPT-5.6 deployment therefore uses Sol where judgment and reliability dominate, Terra where quality and economics must remain balanced, and Luna where speed, predictability, and scale matter more than the highest available reasoning ceiling.

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