OpenAI Launches GPT-5.6 Sol/Terra/Luna, Codex Becomes ChatGPT Superapp

On any other day, the launch of a surprisingly good/competitive Muse Spark 1.1 from Meta Superintelligence Labs, including, for the first time, in the Meta Model API (signaling high confidence for broad usage and third party testing which is bearing out in their sister models), would deserve title story status, but they had the misfortune of going up against a mainline frontier model launch:

As previewed a couple weeks ago before government approval, 5.6 comes in three new sizes, Sol, Terra and Luna, corresponding to the sizes of Sun, Earth and Moon, as an alternative to the more literary sizing of Claude variants, and a new ultra effort level, “our highest-capability setting, coordinating multiple agents across parallel workstreams to finish complex tasks faster”:

max gives GPT‑5.6 even more time than xhigh to reason and explore alternatives, run checks, and revise its approach. ultra goes further by coordinating four agents in parallel by default, trading higher token use for stronger results and faster time-to-result on demanding tasks.

On multiple benchmarks (not just the ones featured here), 5.6 both achieves higher performance at lower cost than Fable or Opus.

“Terra performs just above Fable 5, while Luna outperforms Opus 4.8; each does so in roughly one-third of the time, with about half as many output tokens, and at approximately one-quarter the estimated cost. It also sets new state-of-the-art results on Terminal‑Bench 2.1 and DeepSWE, which test complex command-line workflows and long-horizon engineering in real codebases.”

There are also harder-to-benchmark improvements in computer use, presentation/document generation, and scientific research that should nevertheless be taken very seriously.

As we predicted in April, the newly launched ChatGPT Work and Codex desktop app update today is probably the penultimate step for OpenAI’s superapp strategy (the last open question is what happens to the agentic browser….)

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AI Twitter Recap

OpenAI launched a new three-model GPT‑5.6 family and simultaneously expanded the product stack around it.

OpenAI announced GPT‑5.6 Sol, Terra, and Luna rolling out across ChatGPT, Codex, and the API via @OpenAI and @OpenAIDevs

In ChatGPT, Plus, Pro, Business, and Enterprise users get access to GPT‑5.6 Sol through medium+ effort settings, while Pro and Enterprise can select GPT‑5.6 Pro for highest-quality results on complex tasks, per @OpenAI

API pricing introduced a tiered lineup: Sol $5 / $30 per million input/output tokens, Terra $2.5 / $15, Luna $1 / $6, with cache-write pricing added for the first time and 90% cache-read discount retained, according to @ArtificialAnlys

OpenAI framed the family around a price-performance ladder: Sol = flagship/highest ceiling, Terra = GPT‑5.5-like capability at lower cost, Luna = fastest/cheapest high-volume option, via @OpenAIDevs

The launch bundled major app-layer changes: ChatGPT Work, a new desktop app merging Codex + ChatGPT, Sites beta, programmatic tool calling, and multi-agent beta in the Responses API, via @OpenAI, @OpenAIDevs, and @OpenAIDevs

Official claims and benchmark results

OpenAI’s official message emphasized strong agentic/coding performance, better artifact quality, and improved economics.

Sam Altman called it “obviously the best model we have ever produced” in the launch post, linking the release blog, via @sama

Altman also highlighted enterprise economics: “5.6 sol is a huge step forward for dollars-per-task,” via @sama

Greg Brockman said the goal is “the best price for any level of target performance” and the highest possible ceiling, via @gdb

OpenAI claimed GPT‑5.6 Sol sets a new high of 53.6 on Agents’ Last Exam, beating Claude Fable 5 adaptive by 13.1 points; at medium reasoning it beats Fable by 11.4 points at roughly one-quarter the estimated cost, while Terra and Luna also outperform Fable at around one-sixteenth the cost, via @OpenAI

OpenAI said GPT‑5.6 improves artifact quality across presentations, documents, and spreadsheets, with outputs exportable into existing enterprise tools, via @OpenAI

OpenAI positioned GPT‑5.6 as state of the art for reasoning through complex tasks and for producing materials matched to templates, reference files, and preferred style inside ChatGPT Work, via @OpenAI

OpenAI also said GPT‑5.6 is its most capable model yet on cyber and bio-related tasks, with some API calls potentially blocked or paused for extra safety review in dual-use areas, via @OpenAIDevs

OpenAI highlighted better Computer Use performance: faster, more token-efficient, support for batching and parallel operations across multi-step tasks, plus picture-in-picture supervision, via @OpenAIDevs

Independent evaluations and third-party measurements

Independent evals broadly placed Sol near or at the frontier, especially on coding-agent workloads, while also surfacing caveats.

@ArtificialAnlys reported GPT‑5.6 Sol (max) scores 59 on its Intelligence Index, 1 point below Claude Fable 5 (max), at about one-third of Fable’s cost per task

On the same analysis, Terra and Luna score 55 and 51 on the Intelligence Index, with ~50% and ~80% lower cost per task than Sol, respectively, via @ArtificialAnlys

Artificial Analysis said Sol leads the Coding Agent Index at 80, ahead of Fable 5 and Opus 4.8, and is also cheaper per task than both on their harnesses, via @ArtificialAnlys

It also noted Sol defines a new Pareto frontier of intelligence vs output tokens, while Terra and Luna are not on that frontier, via @ArtificialAnlys

Artificial Analysis found minor improvement over GPT‑5.5 in AA‑Omniscience but with a higher hallucination rate than GPT‑5.5 max, via @ArtificialAnlys

It reported similar GDPval-AA v2 performance to Claude Fable 5, suggesting comparable ability on economically valuable tasks, via @ArtificialAnlys

@ValsAI ranked GPT‑5.6 #2 on Vals Index and Vals Multimodal Index, saying Fable 5 remains ahead on several benchmarks but GPT‑5.6 is “clearly in the same class”

Vals also said Sol is #1 on CyberBench and Excel Modeling Benchmark, and #1 on Legal Research Bench, ProofBench, SWE-bench, and Terminal-Bench 2.1, adding that Fable had a nearly 100% refusal rate on CyberBench, via @ValsAI

@arcprize said GPT‑5.6 Sol scores 7.8% on ARC‑AGI‑3 and is the first verified frontier model to ever beat an ARC‑AGI‑3 game

@GregKamradt noted 92.5% on ARC‑AGI‑2, calling it SOTA while costing an order of magnitude less than GPT‑5.5 Pro three months earlier

@ArtificialAnlys later reported GPT‑5.6 Sol (max) leads CritPt, a benchmark of unpublished research-level physics problems, by roughly 4 points over Claude Fable 5

@llama_index said day-0 ParseBench results show GPT‑5.6 continues to do well on text and tables but still struggles on charts and layout, and that Luna is ~6× cheaper than Sol with only minor degradations

@jerryjliu0 similarly said ParseBench shows no high-level change versus GPT‑5.5 on tables/text/charts/layout, stressing persistent weakness on complex text layouts, chart transcription, and source-element bounding boxes

Technical details

The technical story of GPT‑5.6 is as much about inference orchestration and token efficiency as raw capability.

OpenAI shipped three model tiers with multiple reasoning effort levels; users discussed Light, Medium, High, Extra High, Ultra, leading to a large configuration matrix, via @rasbt

OpenAI added Programmatic Tool Calling in the Responses API and Multi-agent beta, indicating more explicit support for orchestrated tool use and agent decomposition, via @OpenAIDevs

OpenAI’s app layer now uses Codex as the core of the new Work product, per @sama and @gdb

Several posts stress parallel agents/subagents as a major capability lever; @aidan_mclau explicitly mentions users can increase the number of 5.6 subagents

@LiorOnAI summarized likely drivers as adaptive reasoning, parallel agents, programmatic tool use, and higher token efficiency

Artificial Analysis reported Sol max uses ~15k output tokens per Intelligence Index task vs 16k for GPT‑5.5, and fewer than Opus 4.8, GLM‑5.2, and Gemini 3.5 Flash at comparable intelligence, via @ArtificialAnlys

@OpenRouter said early testing found the 5.6 models more token efficient, lowering both cost and time-to-task completion

The desktop/app layer brought a Chrome extension, revamped in-app browser, authenticated sites, persistent multi-tab sessions, file downloads, and tighter cross-device handoffs, via @OpenAIDevs, @OpenAIDevs, and @OpenAIDevs

Sites entered beta for paid users, offering hosting, storage, and optional auth for GPT-built apps, via @OpenAIDevs and @OpenAIDevs

The “Sol autonomously post-trained Luna” claim

This was the most provocative technical claim around the launch, but its interpretation became contested almost immediately.

Multiple accounts amplified the statement that OpenAI says GPT‑5.6 Sol autonomously post-trained GPT‑5.6 Luna, via @scaling01, @tejalpatwardhan, and @dejavucoder

The claim fueled RSI/autoresearch speculation; @tenobrus said if true as stated, it would be a “pretty large update” for automated researcher timelines

@eliebakouch framed it as OpenAI asking Sol to post-train Luna “with 100k GPUs” for an experiment

@gdb said the implication is easy to overlook for accelerating engineering workflows, reinforcing that OpenAI wants this read as more than a marketing flourish

But skeptical clarifications emerged quickly: @nikolaj2030 asked whether this actually meant Sol completed a small controlled post-training task—modifying a config, editing a scheduler file, and launching a run—rather than end-to-end real-world post-training of Luna

@nrehiew_ interpreted the screenshot similarly: Sol could go from high-level ideas to editing configs and launching experiments, not fully owning Luna’s end-to-end post-training

@scaling01 argued that what’s probably happening is a model implementing LLM-as-a-judge graders, reward-shaping logic, or small training configs on top of existing OpenAI RL infrastructure—not autonomous end-to-end research or training systems

@scaling01 explicitly said we should distance these statements from literal autonomous end-to-end post-training or research, which models still cannot do

Counterbalancing that skepticism, @aidan_mclau said it is routine for him to have 5.6 e2e do an entire RL run, suggesting meaningful internal workflow automation even if not self-sufficient research

The consensus across technical observers was not that Sol independently invented and trained Luna, but that GPT‑5.6 may now be capable of executing meaningful chunks of model-improvement workflows inside mature internal infrastructure

Internal productivity and recursive improvement signals

OpenAI also used internal-usage data to argue that GPT‑5.6 materially changes researcher throughput.

@scaling01 highlighted an OpenAI claim that it doubled experiment throughput per researcher since the start of the year

@eliebakouch quoted OpenAI saying average daily output tokens per active researcher were more than twice the highest level observed for GPT‑5.5 during internal testing

Another OpenAI stat, relayed by @eliebakouch, said over six months the share of research compute devoted to internal coding inference grew 100-fold, while internal agentic token usage increased ~22-fold

@FakePsyho linked these developments to OpenAI’s performance in top programming contests, describing systems close to GPT‑5.6 plus custom harnesses as decisively beating elite human competitors

This fed broader RSI/autoresearch discussion, especially from people who see long-horizon coding and heuristic optimization as proxies for model-improvement capability

Product implications: ChatGPT

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