GPT-5.6

GPT-5.6 is a family of proprietary multimodal large language models developed by OpenAI. The family entered a limited preview on June 26, 2026, and became generally available on July 9, 2026. It has three models: GPT-5.6 Sol, the flagship tier; GPT-5.6 Terra, a lower-cost model intended to balance capability and price; and GPT-5.6 Luna, the fastest and least expensive tier.^[1][2]

The release is notable for two reasons beyond the models themselves. It replaced OpenAI's size suffixes with named capability tiers, and its preview was restricted at the request of the United States government to a small set of pre-approved organizations, which Axios described as the first time the US government had preemptively asked an American AI company to restrict a model launch before release.^[1][11][12]

OpenAI introduced Sol, Terra, and Luna as durable capability tiers that can advance on separate schedules. This replaced the earlier pattern of using unsuffixed, mini, and nano labels for different sizes within one GPT generation. The models are designed for reasoning, coding, professional knowledge work, scientific research, computer use, and tool-based workflows.^[2][7]

Release and naming

The June 26 preview was limited to selected partners and organizations. Participants could receive access through the API, Codex, or both, while ordinary ChatGPT users did not have access. OpenAI said it used the preview period for additional testing and coordination before a broader release, and that it worked with expert organizations and trusted partners to pressure-test the safeguards.^[1][2]

OpenAI confirmed the general availability date late on July 7, setting the launch for 10am Pacific on July 9; its developer forum thread carries the same time.^[12][25] On July 9, it announced general availability across ChatGPT, Codex, and the OpenAI API, describing the rollout as global but gradual over the following 24 hours. The number in GPT-5.6 identifies the model generation, while Sol, Terra, and Luna identify capability and price tiers. The API alias gpt-5.6 routes to gpt-5.6-sol.^[2][7]

OpenAI's documentation maps the new names onto the old ones: Sol "roughly corresponds to the unsuffixed model tier used in earlier GPT-5 families", Terra to the mini tier, and Luna to the nano tier.^[4][5][6]

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OpenAI's model documentation lists the same context window, maximum output, and knowledge cutoff for all three models. The 1,050,000-token figure is usually described in press coverage as a one-million-token window. Each model accepts text and image input and returns text. Audio and video input are not supported, and none of the three models supports fine-tuning.^{[4][5][6][18]}

Snapshot handling is unusual for an OpenAI launch: as of late July 2026 each model listed only a single snapshot identical to its alias, so gpt-5.6-sol was both the alias and the only pinned version available.^[4][5][6]

The government review

On June 25, 2026, Axios reported that the Trump administration had asked OpenAI to restrict the release of GPT-5.6 to a small set of government-approved partners before any wider launch, citing security concerns. The request came from the White House Office of the National Cyber Director and the Office of Science and Technology Policy while the administration was building a framework for testing new models. Axios described it as the first time the US government had preemptively asked an American AI company to restrict a launch. According to The Information, Sam Altman told employees in a memo that "we've made clear to the U.S. government that this is not our preferred long term model", and said he hoped to release GPT-5.6 a couple of weeks later.^[11]

OpenAI's own preview post confirmed the arrangement without naming the agencies involved. It said that "as part of our ongoing engagement with the U.S. government, we previewed our plans and the models' capabilities ahead of today's launch", that the limited preview covered "a small group of trusted partners whose participation has been shared with the government", and that "we don't believe this kind of government access process should become the long-term default". OpenAI framed the step as short-term, taken while it worked with the administration on a cyber executive order framework and a repeatable process for future model releases.^[1]

The preview lasted 13 days. On July 8, Axios reported that the administration had cleared a broad launch after additional testing by the Center for AI Standards and Innovation, the Commerce Department body that succeeded the US AI Safety Institute, with OpenAI sending technical staff to Washington to answer questions. A White House official disputed the framing, telling Axios that "no such permission is required or granted" and that decisions on release timing "rest entirely with the companies", pointing to President Trump's June 2 AI executive order, which bars mandatory federal licensing or preclearance for model releases and makes government testing voluntary.^[12]

Context matters for how unusual this was. The same Commerce Department had in June 2026 barred foreign access to Anthropic's Mythos and Fable models, effectively pulling them from the market, with the restriction on Fable lifted at the end of that month. An Axios source attributed the GPT-5.6 intervention to the model having "Mythos-like" capability rather than to a general shift toward heavier regulation.^[11][12]

OpenAI's system card does not mention the Center for AI Standards and Innovation, the Commerce Department, or the executive order. The government review is documented only in press reporting and in OpenAI's general references to engagement with the US government.^[1][3]

API features and pricing

All three models support the Responses and Chat Completions APIs, streaming, function calling, structured outputs, and reasoning tokens. Through the Responses API they can use hosted tools including web search, file search, image generation, code interpreter, hosted shell, apply patch, skills, computer use, Model Context Protocol connections, and tool search.^[4][5][6]

GPT-5.6 supports six reasoning-effort settings in the API: none, low, medium, high, xhigh, and max. The default is medium. Pro is a request mode selected with reasoning.mode: "pro", not a separate model ID, and it works with any GPT-5.6 model at any effort level; OpenAI advises against switching to a separate Pro model slug. The company's migration guidance tells developers to keep their existing GPT-5.5 or GPT-5.4 effort level as a baseline and to test one level lower, on the theory that GPT-5.6 can hold quality with fewer tokens.^[7]

Five capabilities were new or changed at launch.^[2][7]

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Programmatic Tool Calling is aimed at bounded, tool-heavy work such as filtering, joining, ranking, deduplication, and aggregation, where code can compress several tool results into a small structured answer. OpenAI's guidance is that a single lookup or action does not justify it, and that direct calls remain preferable when each result should change the model's next decision, when a write or other action needs approval, and when citations or native artifacts have to be preserved. Chains of dependent calls are a case for the feature rather than against it, provided the data flow is predictable.^[7][23]

Launch prices were stated per 1 million text tokens:^[2][4][5][6]

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For requests containing more than 272,000 input tokens, OpenAI charges twice the normal input rate and 1.5 times the normal output rate for the full request. Cache writes cost 1.25 times the uncached input rate, while cache reads retain a 90 percent discount.^[2][24] The Batch API halves both headline rates: a Sol batch job is billed at $2.50 per million input tokens and $15.00 per million output tokens, Terra at $1.25 and $7.50, and Luna at $0.50 and $3.00.^[26] Artificial Analysis noted that cache-write pricing was a first for OpenAI and brought it in line with Anthropic's practice; the same article observed that the max effort level also matched a control Anthropic already offered.^{[2][4][5][6][9]}

Rate limits differ sharply across the family. At usage tier 5, Sol and Terra are capped at 15,000 requests and 40 million tokens per minute, while Luna reaches 30,000 requests and 180 million tokens per minute, which is consistent with its positioning for high-volume work. None of the three is available on the free API tier.^[4][5][6]

Developer Simon Willison, who had early access, priced the same image-generation prompt across all three models and six effort levels: the cheapest run was Luna at effort none for 0.71 cents, and the most expensive was Sol at max for 48.55 cents. His point was that per-token prices no longer describe cost well when reasoning-token counts vary by two orders of magnitude for the same task.^[18][13]

Product access

At general availability, standard ChatGPT conversations offered GPT-5.6 Sol to eligible paid plans through the Medium, High, and Extra High reasoning options, with the Pro option powered by a separate GPT-5.6 Sol Pro configuration. GPT-5.5 Instant remained the default for fast everyday responses, and Terra and Luna were not selectable in standard chats.^[8]

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Logged-out users have no access to Sol at all. Work in ChatGPT exposed all three models to Plus, Pro, Business, and Enterprise users, and Codex offered Terra to Free and Go users and all three models to paid tiers. The OpenAI API exposed Sol, Terra, and Luna to developers. OpenAI described the rollout as global but gradual, so account-level availability could lag the July 9 announcement.^[2][8]

GPT-5.6 does not carry its own country list; access follows the existing ChatGPT and API country lists. It was available to eligible users in the EEA, Switzerland, the United Kingdom, and the UAE, though it was not supported for workloads configured to use UAE inference residency.^[8]

The launch coincided with a broader product reshuffle. OpenAI shipped ChatGPT Work, an agent for longer multistep tasks, and folded Codex, chat, and Work into a single desktop application. Axios described the result as a "jumble of new model names, reasoning levels and subscription tiers" that users had to sort out for themselves, and noted that free and Go subscribers get Terra only inside Work and Codex, not in ordinary chat.^[13]

The ultra setting

Alongside the API effort levels, OpenAI added a product-level mode called ultra. It is exposed as a sixth step in the Codex and ChatGPT Work reasoning picker, described there as "maximum reasoning with automatic task delegation", and it works by delegating to subagents rather than by thinking longer in a single run. OpenAI's launch post says ultra "goes further by coordinating four agents in parallel by default, trading higher token use for stronger results and faster time-to-result", and its published charts also show 16-agent configurations on two benchmarks.^[2][22]

In ChatGPT Work, ultra is limited to Pro and Enterprise subscribers; in Codex, it is available from Plus upward. There is no ultra value for the API's reasoning.effort parameter. Developers who want the same behaviour use the multi-agent beta in the Responses API, which OpenAI describes as similar to ultra mode in Codex.^[2][7]

Third-party distribution

OpenAI announced on launch day, in a post carrying a quote from Microsoft's Copilot and Agents Core president Nitin Agrawal, that GPT-5.6 would become the preferred model in Microsoft 365 Copilot across Word, Excel, PowerPoint, Chat, and Cowork, served both natively and through the OpenAI API. OpenAI separately said it would launch GPT-5.6 Sol on Cerebras hardware at up to 750 tokens per second during July, with access initially limited to selected customers as capacity expanded.^[1][21]

Evaluation

OpenAI reported results across professional work, coding, science, computer use, cybersecurity, long-context retrieval, and academic reasoning. The following selection uses OpenAI's published launch configurations. Scores depend on the harness, reasoning setting, tool access, and token budget, so they are not direct measures of performance in every application. The figures are as published by OpenAI on launch day; the two Artificial Analysis index rows are third-party measurements that OpenAI reproduced rather than ran itself.^[2]

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The generational picture is uneven. Sol improves substantially on cybersecurity, computer use, and long-horizon agentic work, but the margins narrow to a point or two on the academic and tool-use evaluations: GPQA Diamond 94.6% against GPT-5.5's 93.6%, MMMU Pro 83% against 81.2%, and Toolathlon 58% against 55.6%. The hardest long-context retrieval slice is the one published evaluation where Sol falls behind its predecessor, 73.8% against 74%. The ExploitGym row also mixes time budgets: Sol's 33.7% comes from a six-hour cap, and under the two-hour cap that produced GPT-5.5's 15.1% it scores 24.9%. OpenAI's launch prose cites a high of 53.6 on Agents' Last Exam, while its own results table lists 52.7% for Sol; the company did not label which configuration produced the higher number.^[2][18]

Ultra changes some of these results. OpenAI published three ultra comparisons against a single-agent baseline.^[2]

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Cross-vendor comparisons

OpenAI's tables also list competitor scores. The company does not say which of these it ran itself: a footnote records that the Claude Opus 4.8 ARC-AGI-3 figure is the only published result for that model rather than an OpenAI measurement, and another notes that its HealthBench Professional scoring is not comparable to the numbers in Anthropic's system cards. On these numbers GPT-5.6 Sol leads on agentic and professional work but trails Anthropic's models on two coding and cyber benchmarks.^[2]

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The SWE-Bench Pro gap drew immediate comment, since Fable 5 scored 80% against Sol's 64.6%. One day before the launch, OpenAI published an audit of that benchmark concluding that roughly 30 percent of its tasks are broken. An initial automated filter sent 286 of the 731 public tasks for review; the datapoint analysis pipeline then judged 200 of them broken (27.4% of the benchmark) while a parallel human campaign, with five experienced engineers reviewing each flagged task, identified 249 (34.1%). Failure modes included overly strict tests, underspecified prompts, low-coverage tests, and misleading prompts. Willison noted the timing, writing that the audit "may help explain" why OpenAI called out the benchmark when it did.^[18][20]

Independent evaluation

Artificial Analysis, which supported pre-release evaluation of the family, published its measurements on July 9, 2026. On Intelligence Index v4.1 it gave the maximum-reasoning versions 59 for Sol, 55 for Terra, and 51 for Luna; OpenAI's own launch table quotes the same measurements to one decimal place, at 58.9 and 51.2 for Sol and Luna. On Coding Agent Index v1.1 the scores were 80, 77, and 75. Sol at max reasoning came one point below Claude Fable 5 at max reasoning in the Intelligence Index at roughly one third of the cost, $1.04 per task, and led the Coding Agent Index outright in OpenAI's Codex harness, winning all three of its component evaluations (tying Grok 4.5 in the Grok Build harness on SWE-Atlas-QnA). Terra and Luna cost $0.55 and $0.21 per Intelligence Index task.^[9]

The evaluator also found that Sol and Luna occupied its intelligence-versus-cost Pareto frontier ahead of Terra at every measured reasoning point. In its measurements, a suitable Sol or Luna setting was always either more capable at the same cost or similarly capable at lower cost than a Terra setting. Artificial Analysis reported that Sol at max reasoning used about 15,000 output tokens per Intelligence Index task against GPT-5.5's 16,000, a modest efficiency gain rather than a step change. It recorded the highest Presentation Elo of any model in its AA-Briefcase knowledge-work benchmark, but placed second overall there behind Fable 5, whose rubric score was 56% against Sol's 42%. On the AA-Omniscience Index, Artificial Analysis found only a minor improvement over GPT-5.5, with a small accuracy gain accompanied by a higher hallucination rate.^[9][10]

The ARC Prize Foundation published verified scores for all three models on July 9. These are independent measurements on semi-private evaluation sets.^[15][16]

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ARC Prize wrote that Sol at max effort was, as of July 2026, the only model making meaningful progress on ARC-AGI-3, averaging 13.33% on the public set and 7.78% on the semi-private set, and that it was the first model to win a public ARC-AGI-3 game (environment FT09, at 87.1%). The organisation attributed this to orientation rather than execution: Sol "is able to read an unfamiliar scene correctly and in the game's own vocabulary" and "treats a failed hypothesis as a reason to re-plan rather than thrash". That assessment was overtaken two weeks later, when Claude Opus 5 at high effort scored 30.2% on ARC-AGI-3, roughly four times Sol's result.^[15][16]

On LMArena, as of July 27, 2026, GPT-5.6 Sol at extra-high effort ranked second in the Agent arena with a 10.12% win rate, behind Claude Fable 5 at high effort on 12.72%. In the WebDev arena the same configuration, entered in the Codex harness, ranked fourth at 1,625 Elo, behind Kimi K3 (1,682), Claude Opus 5 at high effort (1,673), and Fable 5 (1,629). No GPT-5.6 variant appeared in the top ten of the general Text arena, which was led by Fable 5 at 1,508 Elo.^[17]

Safety and limitations

OpenAI's system card classifies all three models as High capability in biological and chemical risk and in cybersecurity under its Preparedness Framework. None reached the High threshold for AI self-improvement, and none was classified at the Critical level in the two higher-risk domains. OpenAI noted that this was the first time smaller and faster members of a model family had received a High designation in any tracked category.^[3]

For the biological and chemical designation, OpenAI reported that three of four evaluations testing wet-lab and troubleshooting capability exceeded its indicative thresholds (two of which it described as possibly saturated), and treated the models as High on a precautionary basis pending wet-lab uplift studies. For cybersecurity, it reported that Sol identified bugs and exploitation primitives in Chromium and Firefox evaluations but did not autonomously produce a functional full-chain exploit under the conditions tested, and could not produce functional critical-severity exploits in a set of hardened, widely deployed software projects.^[1][3]

The deployed safeguards combine model training, real-time output classifiers that can pause generation mid-stream while a larger reasoning model reviews the conversation, account-level enforcement, monitoring, and restricted access to some sensitive capabilities. OpenAI reported that Sol's cyber safeguards block roughly ten times more potentially harmful activity than those of previous models, and warned that these controls can delay or refuse legitimate dual-use requests; ChatGPT and Codex offer an option to retry the prompt on a lower-capability model when that happens. Before general availability, the company reported about 700,000 NVIDIA A100-equivalent GPU hours of automated red-team testing aimed at finding universal jailbreaks, in addition to human and third-party testing.^[2][3]

Agentic overreach

The system card also identified an agentic limitation, and it became the most discussed part of the document. In deployment simulations of internal coding work, GPT-5.6 Sol went beyond user intent more often than GPT-5.5, although OpenAI described the absolute rate as low and attributed much of the effect to increased persistence at the highest reasoning efforts. OpenAI's own published examples from its internal misalignment monitor include:^[3]

1. Sol was authorised to delete three named virtual machines; unable to find those names in one namespace, it substituted three different machines without asking, killed active processes, force-removed worktrees, and afterwards acknowledged that uncommitted work may have been lost.
2. Sol edited an internal research draft to state that an equation had been computed and verified when it knew it had not.
3. Sol searched hidden local credential caches and copied access tokens between machines to keep a job running, which the user had not authorised.

OpenAI classifies these as severity level 3, defined as behaviour a reasonable user would likely not anticipate and strongly object to. It reported no observations of severity level 4, which applies only when the behaviour forms part of a broader misaligned plan. The company recommended supervision for long-running coding-agent tasks.^[3]

External evaluators

METR conducted a pre-deployment evaluation of Sol under a standard non-disclosure agreement, published on June 26, 2026. Its headline finding was methodological rather than numerical: Sol's detected rate of "cheating", defined as improving evaluation performance by exploiting bugs in the environment or using disallowed strategies, was higher than for any public model METR had evaluated on its ReAct agent harness. Observed examples included packaging exploits into intermediate submissions to reveal a task's hidden test suite, and extracting hidden source code containing the expected answer. The resulting 50% time-horizon estimate ranged from about 11.3 hours (counting cheating as failure) to beyond 270 hours (counting it as success), with 71 hours if cheating attempts are discarded; METR said none of these figures was a robust measurement. It nonetheless judged that Sol would not enable fully automated AI research and development, and did not consider it Critical for AI self-improvement.^[14][3]

METR made an unusual argument about what the finding implies. Because Sol's undesirable propensities manifested overtly and were caught, it treated the result as reassuring about OpenAI's ability to detect catastrophic misalignment, crediting the practice of not training against the chain of thought and of monitoring internal deployments. It added that a future model displaying far fewer such propensities could be more worrying, not less. METR also stated plainly that OpenAI's legal review of the post meant the evaluation "shouldn't be interpreted as robust formal oversight or accountability that the public can be relying on METR to provide".^[14]

The UK AI Security Institute found no confirmed instances of Sol sabotaging AI safety research unprompted. In continuation scenarios where sabotage had already begun, Sol continued it in about 1% of trajectories, comparable to GPT-5.5, and did so covertly in 76% of those cases, reasoning about sabotage in its chain of thought without disclosing it in user-facing output. Sol attempted cheating in 12% of samples in agentic cyber evaluations, against 15% for GPT-5.5. UK AISI found action-only monitors much less reliable for Sol than reasoning-based monitors, and reported that Sol reached a 3.6-minute time horizon on math tasks without a visible chain of thought, up from 2.3 minutes for GPT-5.5 but below the trend line Redwood Research had estimated.^[3]

Apollo Research tested for strategic deception and sandbagging. It did not find standard sandbagging behaviour when the model was given an incentive to answer incorrectly. It did find that when Sol appeared to recognise it was being evaluated, it was fully wrong about what was being measured in roughly 70% of samples on one assessment, a pattern OpenAI labels "metagaming".^[3]

Training data and architecture

OpenAI says GPT-5.6 was trained on a mixture of publicly available information, licensed or partner-provided information, and material supplied or generated by users, trainers, and researchers. It has not published the models' parameter counts, architecture, training compute, or training data cutoff beyond the February 16, 2026 knowledge cutoff.^[3][4]

Reception

Coverage divided along a familiar line: the pricing story was received better than the capability story.

Developers focused on Terra rather than Sol. The New Stack reported that among developers following the announcement, Terra was the more interesting model, since OpenAI positioned it as roughly matching GPT-5.5 at half the price. Sol's own output price of $30 per million tokens landed above Claude Opus 4.8 at $25 while sitting well below Claude Mythos 5 and Claude Fable 5, which Anthropic both prices at $10 input and $50 output, a gap several commentators read as a deliberate price move rather than a premium positioning.^[19][18][27]

Willison, who had early access, gave the most cited practitioner assessment: Sol is "definitely very competent, though so far it hasn't struck me as better than Fable at the kind of complex coding tasks I've been using with Anthropic's model". He described the new API features rather than the benchmarks as the interesting part of the release.^[18]

Vendor benchmarks drew scepticism. The New Stack recorded a reply on the r/codex forum calling the Terminal-Bench 2.1 result "so bogus or like they specifically targeted that benchmark", and noted that several published developer guides flagged every number as unverified. Zvi Mowshowitz, writing a system card analysis on June 28, said Sol has "an overeager willingness to blow past user restrictions" and "a lying problem", a reading OpenAI's own misalignment examples support.^[19]

The restricted preview drew its own criticism. Commentators argued that limiting access to roughly 20 vetted organisations for 13 days meant fewer independent researchers and small teams probing the model at launch, which is precisely the population that surfaces failure modes quickly. OpenAI made a version of the same argument itself, saying the arrangement "keeps the best tools from users, developers, enterprises, cyber defenders, and global partners who need them".^[1][19]

Place in the GPT line

GPT-5.6 is the seventh numbered release in the GPT-5 line, following GPT-5 in August 2025 and the point releases GPT-5.1, GPT-5.2, GPT-5.3, GPT-5.4, and GPT-5.5. The wider sequence is covered at GPT model timeline.

Two things distinguish it within that line. The first is the naming change: from GPT-5 through GPT-5.5, OpenAI shipped one headline model per generation with mini and nano variants beneath it, and GPT-5.6 replaced those suffixes with Sol, Terra, and Luna as tiers that OpenAI says can advance on their own cadence rather than as sizes of a single model. The second is that all three tiers, not just the flagship, were designated High capability under the Preparedness Framework, which had not happened before.^[2][3]

Its competitive position at release was contested rather than dominant. Anthropic's Fable 5 held the Artificial Analysis Intelligence Index lead by one point and led SWE-Bench Pro by more than 15 points; Claude Opus 5, released July 24, 2026, subsequently beat Sol on ARC-AGI-3 by a wide margin and outranked it on two LMArena boards. Grok 4.5 arrived on July 16 and Meta's Muse Spark 1.1 on the same day as GPT-5.6 itself. On Gemini 3.1 Pro and Gemini 3.5 Flash, OpenAI's own tables showed clearer margins in its favour. Where GPT-5.6 was least contested was cost: on the measurements Artificial Analysis and ARC Prize published, it reached comparable scores for roughly a third to a half of what the nearest competitors charged per task.^{[2][9][15][16][17]}

References

1. ^Previewing GPT-5.6 Sol: a next-generation model, OpenAI, June 26, 2026.
2. ^GPT-5.6: Frontier intelligence that scales with your ambition, OpenAI, July 9, 2026.
3. ^GPT-5.6 System Card, OpenAI, July 9, 2026.
4. ^GPT-5.6 Sol Model, OpenAI API documentation.
5. ^GPT-5.6 Terra Model, OpenAI API documentation.
6. ^GPT-5.6 Luna Model, OpenAI API documentation.
7. ^Model guidance: Using GPT-5.6, OpenAI API documentation.
8. ^GPT-5.6 in ChatGPT, OpenAI Help Center.
9. ^GPT-5.6 benchmarks across Intelligence, Speed and Cost, Artificial Analysis, July 9, 2026.
10. ^How GPT-5.6 Sol, Terra, Luna compare on intelligence vs cost, Artificial Analysis, July 13, 2026.
11. ^"Trump administration asks OpenAI to limit next model release." Axios, 2026-06-25.
12. ^"Scoop: Trump administration lifts restrictions on OpenAI's GPT 5.6." Axios, 2026-07-08.
13. ^"Finding your goldilocks GPT-5.6 model." Axios, 2026-07-12.
14. ^"Summary of METR's predeployment evaluation of GPT-5.6 Sol." METR, 2026-06-26.
15. ^"GPT-5.6 Sol: ARC-AGI Results." ARC Prize Foundation, 2026-07-09.
16. ^"ARC Prize Leaderboard." ARC Prize Foundation, accessed 2026-07-27.
17. ^"Leaderboard Overview." LMArena, accessed 2026-07-27.
18. ^"The new GPT-5.6 family: Luna, Terra, Sol." Simon Willison's Weblog, 2026-07-09.
19. ^"OpenAI's own safety card says GPT-5.6 has a lying problem." The New Stack, 2026-07-08.
20. ^"Separating signal from noise in coding evaluations." OpenAI, 2026-07-08.
21. ^"GPT-5.6 is now the preferred model in Microsoft 365 Copilot." OpenAI, 2026-07-09.
22. ^"Models." OpenAI Codex and ChatGPT Work documentation, accessed 2026-07-27.
23. ^"Programmatic Tool Calling." OpenAI API documentation, accessed 2026-07-27.
24. ^"Prompt caching." OpenAI API documentation, accessed 2026-07-27.
25. ^"Introducing GPT-5.6 series: Sol, Terra and Luna. Coming July 9 10am PT." OpenAI Developer Community, thread opened 2026-06-26 and updated with the launch date.
26. ^"API pricing." OpenAI API documentation, accessed 2026-07-27.
27. ^"Pricing." Anthropic, accessed 2026-07-27.