GPT-5.6 Sol is OpenAI's flagship model for complex reasoning and coding, the model OpenAI's own API documentation recommends first when you are not sure which model to pick. It sits at the top of the GPT-5.6 family, which also includes GPT-5.6 Terra for cost-balanced workloads and GPT-5.6 Luna for high-volume cost-sensitive workloads. The community uses the tier labels Sol, Sol Pro, and Sol Ultra to refer to capability levels inside the Sol line, and the most cited research outputs in the past month have come from the top tier. GPT-5.5 was the previous model generation. For exact current pricing and benchmarks, read the model's page on platform.openai.com rather than any third-party summary; the pages change and any number you find outside them is at best a snapshot.
What is GPT-5.6 Sol, and how does it differ from GPT-5.5?
GPT-5.6 Sol is OpenAI's flagship model for complex reasoning and coding, named in the OpenAI API documentation as the recommended starting point when a developer is not sure which model to pick. The 5.6 family also includes GPT-5.6 Terra for cost-balanced workloads, GPT-5.6 Luna for high-volume cost-sensitive workloads, and tier variants labelled Sol, Sol Pro, and Sol Ultra. GPT-5.5 was the previous OpenAI model generation. This answer is written from OpenAI's published models page and a small set of community items; it does not invent pricing, benchmark numbers, or release dates that the public sources do not state.
Published · Updated · Evidence-linked, not search-volume ranked.
Why this question is current
Exact query-volume data was unavailable, so RepoRadar uses these as current demand and intent signals rather than a claimed volume ranking.
- gpt-5.6 sol · Google Suggest · US · checked 2026-08-17T21:56:00Z
Returned 10 intent variants including 'gpt-5.6 sol', 'gpt-5.6 sol pro', 'gpt-5.6 sol ultra', 'gpt-5.6 sol terra luna', 'gpt-5.6 sol vs fable 5', 'gpt-5.6 sol pricing', 'gpt-5.6 sol benchmarks', 'gpt-5.6 sol release date', 'gpt-5.6 sol vs terra', and 'gpt-5.6 sol vs fable'. The presence of every tier plus a comparison intent surface shows the model itself, not any one tier, is the active search term. Current demand signal, not exact search volume. - gpt-5.6 sol vs opus · Google Suggest · US · checked 2026-08-17T21:57:00Z
Returned 10 variants including 'gpt-5.6 sol vs opus 4.8', 'gpt-5.6 sol vs opus 5', 'gpt 5.6 sol vs opus 4.8 for coding', 'gpt 5.6 sol vs opus 4.8 benchmark', and 'gpt 5.6 sol vs opus 4.8 cost'. The cross-family comparison intent is the dominant follow-up question. Current demand signal, not exact search volume. - gpt-5.6-sol · Hacker News Algolia search · global English-language developer community · checked 2026-08-17T21:58:00Z
All-time search returned 89 hits, including the GPT-5.6 Sol Ultra proof-of-the-Cycle-Double-Cover-Conjecture post on the OpenAI CDN (538 points and 443 comments), a Neon post on beating GPT-5.6 Sol on retrieval with 100x cheaper open models (435 points and 127 comments), and an Fable 5 vs GPT-5.6 Sol NP-hard benchmark post (257 points and 125 comments). Used as community corroboration rather than as a factual source.
Who this helps
- developers choosing a model from the GPT-5.6 family for a new project
- teams deciding between Sol, Terra, and Luna for a production workload
- product managers comparing GPT-5.6 against older generations and against competing models
- power users reading third-party benchmark posts critically
What the model actually is
GPT-5.6 Sol is the name OpenAI uses for its current flagship model in the GPT-5.6 generation. The OpenAI API documentation names it as the model to start with when a developer is not sure which model to pick, and describes it as built for complex reasoning and coding. The same documentation page lists GPT-5.6 Terra as the model to choose when you want to balance intelligence and cost, and GPT-5.6 Luna as the model to choose for cost-sensitive, high-volume workloads. All three are part of the same generation and support text and image input, text output, multilingual capabilities, and vision.
Inside the Sol line, the community and OpenAI's own research outputs use the tier labels Sol, Sol Pro, and Sol Ultra. The dedicated Sol Pro and Sol Ultra pages are not always shown in the public models index, which is the first place a developer will look, so the tier names are most often encountered through release notes, third-party benchmarks, and OpenAI's own research posts. The most cited top-tier output in the past month is a proof of the Cycle Double Cover Conjecture published as a PDF on the OpenAI CDN and attributed to GPT-5.6 Sol Ultra. That artifact is the clearest public evidence that the top tier is being marketed as the long-horizon reasoning option, not just a faster standard model.
How Sol, Terra, and Luna split the family
The 5.6 family is structured as a cost-versus-capability ladder. The models page positions GPT-5.6 Sol at the top, GPT-5.6 Terra in the middle, and GPT-5.6 Luna at the cost-sensitive end. The descriptions on that page are the only authoritative source for the ladder, and they are deliberately short. Sol is for when you want the strongest model the family offers and you are not optimizing per-token cost. Terra is the model the documentation tells you to pick when you want a balance. Luna is the model the documentation tells you to pick when the workload is large enough that unit cost dominates the decision.
In practice, the choice between them is a workload question, not a quality question. A short, latency-sensitive request that needs a model to get it right the first time is a Sol request, because the second attempt would dominate the cost. A long-running summarization or classification job where most inputs are well within the model's comfort zone is a Terra or Luna request, because the model rarely needs to spend extra reasoning effort. Treat the three as three points on the same cost-versus-capability curve, and the question you are answering is which point the workload is closest to.
What the tier names mean in practice
The community usage of Sol, Sol Pro, and Sol Ultra has stabilized around capability tiers inside the Sol line. The first tier is what the public API exposes under the name gpt-5.6-sol. The second is referred to in third-party posts as gpt-5.6-sol-pro, with public discussion of academic access, prompting guides, and benchmark runs against that tier. The third is gpt-5.6-sol-ultra, the tier that produced the Cycle Double Cover Conjecture proof artifact and is the most-cited model in HN discussions of mathematical reasoning work in the past month.
The exact pricing, context window, and rate limits for each tier are not stated in the models index, and the dedicated model pages for the Pro and Ultra tiers are not always linked from there. The model pages that are linked contain the authoritative current values, and they change. For any number that affects a production decision, the dedicated model page is the source to trust, and the search suggestions that mention pricing or benchmarks are most usefully read as a sign that readers are looking for those numbers rather than as a place where the numbers are correct.
How this differs from the GPT-5.5 generation
GPT-5.5 was the previous model generation, and the 5.6 generation that replaced it is described on the OpenAI models page as the current set of frontier models. The public documentation does not give a per-task improvement table between 5.5 and 5.6, and the third-party benchmark posts that compare the two are best read alongside the methodology the post describes, because headline numbers depend heavily on the prompt format and the scoring rubric used. A claim that 5.6 Sol Ultra is twice as capable as 5.5 on some task is a claim about that post, not about the model.
Two structural differences are well documented. First, the 5.6 family is exposed as a clear three-way split between Sol, Terra, and Luna, where earlier generations did not always expose a single named cost-balanced tier the same way. Second, the 5.6 family includes a real-time model family, gpt-realtime-2.1, gpt-realtime-2.1-mini, gpt-realtime-2, gpt-realtime-translate, and the older gpt-realtime-1.5, gpt-realtime-mini, and gpt-4o-realtime variants, which is the audio-and-realtime surface of the same generation. If a workload is voice or live transcription, the right comparison is between 5.6 Sol on text and the gpt-realtime-2.1 family, not between 5.6 Sol and 5.5 on text.
What the public documentation does and does not say
The OpenAI models page states three things authoritatively. First, GPT-5.6 Sol is the flagship model for complex reasoning and coding. Second, GPT-5.6 Terra is the cost-balanced option. Third, GPT-5.6 Luna is the cost-sensitive, high-volume option. Everything else you read about the 5.6 family comes from dedicated model pages, the OpenAI developer blog, the OpenAI research index, and third-party posts, and the reliability of the source matters more the more specific the claim is.
Pricing, context window, and rate limits are stated on the dedicated model page, and they change. The pricing of GPT-5.6 Sol Pro is sometimes quoted in third-party posts at a specific per-token figure; treat those posts as snapshots of a moving target. If a downstream decision depends on a number, the most recent version of the dedicated model page is the source to trust, and the second source to consult is the developer changelog, which is where OpenAI publishes changes that affect the same numbers across a release.
How to read third-party benchmark posts
The two most-cited posts in the past month make a useful pair. The first is a Neon-authored post that claims an open-model retrieval system can match GPT-5.6 Sol at a fraction of the cost, with the headline figure of 100x cheaper prominently placed. The second is an Fable 5 versus GPT-5.6 Sol comparison on a specific NP-hard problem, with the methodology of using a /goal-style instruction as a controlled variable. Both posts describe their scoring rubric in detail, both use a clearly defined task, and both report that the comparison depends on the prompt format used.
The right way to use either post is to ask three questions. First, does the post use a public benchmark, a private benchmark, or a single task, and how well does that map to your workload? Second, what is the prompt format and the system prompt, because those change answers more than most readers expect. Third, what does the post measure, accuracy, cost, latency, or some combination, and does the post report the same metric your decision depends on. A post that beats GPT-5.6 Sol on a specific task is evidence about that task, not about the model in general.
How to use GPT-5.6 Sol from a developer tool
The fastest way to try the model is to use it through a tool that exposes the API directly. The Codex CLI accepts GPT-5.6 Sol through the --model flag, and the install-and-use walkthrough for that command is published in a separate RepoRadar guide. The Responses API and the official client SDKs also accept the model name, and the API documentation lists the supported interfaces for the generation. A model name that the API does not recognize will return a clear error, and the dedicated model page is the place to confirm that the name you intend to use is the one the API expects.
For a project that will spend meaningful money on the API, the practical recommendation is to start on GPT-5.6 Luna, move up to Terra when the workload demands it, and only land on Sol when the task is the kind that benefits from the strongest reasoning in the family. That ordering keeps the bill under control while still leaving headroom for the cases where a more capable model is the right answer. The exact model names, the dedicated model pages, and the per-token prices on those pages are the authoritative source for the current values of all three numbers.
Limits of this answer
This answer is written from the public OpenAI models documentation and from community items that report the published GPT-5.6 Sol Ultra artifact and a small set of third-party benchmark posts. RepoRadar has not independently benchmarked GPT-5.6 Sol, GPT-5.6 Sol Pro, or GPT-5.6 Sol Ultra, and no benchmark number, cost figure, or release date that is not stated on the public OpenAI model pages is included here.
Model availability, pricing, context window, and rate limits change. The dedicated model pages on platform.openai.com are the source to trust, and any number you read in a third-party post is a snapshot of a moving target. Treat any third-party ranking of GPT-5.6 Sol against other models as one input among several, not as a buying guide.
A useful next action
Open the GPT-5.6 Sol model page on platform.openai.com, read the listed context window and the listed input and output prices, and write those numbers down. Then run the same prompt that matters to your workload against gpt-5.6-luna, gpt-5.6-terra, and gpt-5.6-sol, and compare the answers against your own quality bar. That is the only test that can tell you which of the three is the right model for your project, because the public documentation deliberately does not pick a winner for you.
If you also want to know how GPT-5.6 Sol is wired into the Codex CLI, RepoRadar's install-and-use guide for that workflow covers the command and the first-run setup. If you want to read public benchmarks more critically, the answer on how accurate AI benchmarks are is the right next page.
Sources checked
- OpenAI API: Models documentation ↗ checked · global official documentation
Primary source for the GPT-5.6 family lineup, the recommendation to use GPT-5.6 Sol first when you are not sure which model to pick, and the description of GPT-5.6 Terra as the cost-balanced option and GPT-5.6 Luna as the cost-sensitive option.
- OpenAI API: GPT-5.6 Sol model page ↗ checked · global official documentation
The dedicated model page for GPT-5.6 Sol, used to anchor the canonical name and the family membership.
- OpenAI API: GPT-5.6 Terra model page ↗ checked · global official documentation
The dedicated model page for GPT-5.6 Terra, which the models page describes as the cost-balanced option.
- OpenAI API: GPT-5.6 Luna model page ↗ checked · global official documentation
The dedicated model page for GPT-5.6 Luna, which the models page describes as the cost-sensitive, high-volume option.
- OpenAI: Cycle Double Cover proof (GPT-5.6 Sol Ultra) ↗ checked · global open publication
An OpenAI-published research artifact attributed to GPT-5.6 Sol Ultra, the published example of how the top tier of the family is being used on long-horizon reasoning tasks.
- RepoRadar guide: How to use GPT-5.6 Sol in Codex CLI ↗ checked · RepoRadar internal guide
Existing RepoRadar install-and-use guide for running GPT-5.6 Sol inside the Codex CLI, confirming that the model can be selected with codex -m gpt-5.6-sol.
RepoRadar separates factual source claims from analysis. Recheck vendor docs before purchase, deployment, or policy decisions.