OpenAI 发布 GPT-5.6 三款模型，Codex 整合为 ChatGPT 超级应用

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