This index exposes the material behind every decision guide: what we reviewed, when we retrieved it, the claim it supports and where the evidence stops.
158decision guides
707source records
699scoped claim notes
How to read this index
A citation is not a transferable result.
Reviewed identifies the part of the source used. Supports states the bounded proposition we draw from it. Retrieved records when time-sensitive material was checked.
Vendor documentation establishes vendor claims and product behavior as documented, not independent performance. Papers establish results only for their stated methods and tested scope. AccessAllGPT recommendations remain labeled analysis.
AccessAllGPT reviewed four public first-party or project-controlled surfaces on October 23, 2026: Anthropic’s October 22 announcement, the live Agent Skills specification, the Agentic AI Foundation project directory and the public Agent Skills repository pinned at commit 69ef37e9424c0a7ea9dd2293b559e43ec8176379. We checked the announcement date, donation scope, current project home, file-format contract, optional execution surface, repository availability, license and stated governance roadmap. Adoption totals and integration counts are Anthropic claims; AccessAllGPT did not obtain the underlying platform list or installation ledger, validate every integration, install a skill, execute bundled code, audit the specification, participate in AAIF governance or test interoperability between clients.
Reviewed: October 22, 2026 publication date; donation scope; claimed adoption and installation counts; named maintainers; proposed roadmap; participation terms
Supports: Anthropic says it donated the Agent Skills standard, name, logo, GitHub organization and agentskills.io domain to the Linux Foundation-hosted AAIF, while proposing versioned releases, a formal proposal process and security guidance as future work.
Reviewed: Required directory structure; SKILL.md YAML frontmatter and Markdown body; required and optional fields; optional scripts, references and assets; progressive disclosure; validation
Supports: The live specification defines a skill as a directory with a required SKILL.md file, mandatory name and description metadata, optional compatibility and allowed-tools metadata, and optional executable or reference resources.
Agentic AI Foundation / Linux Foundation · Retrieved
Reviewed: AAIF project directory; Agent Skills listing; project description; GitHub and project-site links; foundation positioning
Supports: The AAIF public project directory now lists Agent Skills as an open format for reusable agent expertise and links to its public repository and project website.
Reviewed: Public repository tree; specification and documentation scope; reference implementation; commit identity; Apache-2.0 repository license
Supports: The public repository contains the specification, documentation and a reference library, and GitHub identifies its license as Apache-2.0. The reviewed commit pins the technical artifact independently of mutable page counters.
AccessAllGPT reviewed six AWS-controlled public surfaces on October 23, 2026: the October 22 general-availability announcement, the companion technical post, the live Training session APIs documentation, the prerequisites documentation, the public AWS cookbook repository and the SageMaker AI pricing page. We checked the announcement date, release status, API boundary, supported model identifiers, Region availability, default quota, operation semantics, checkpoint behavior, IAM and data controls, billing dimensions, published example results, repository availability and license. We attempted to inspect the dedicated public pricing tab, but its embedded model-specific rate card did not render in our unauthenticated browser; we therefore report the billing units without inventing numeric rates. AccessAllGPT did not obtain an AWS account, open a training session, install the SDK, submit data, run a recipe, reproduce an AWS example, inspect managed GPUs, deploy a checkpoint or receive a bill.
Reviewed: October 22, 2026 announcement date; general-availability status; supported techniques and models; responsibility split; token billing statement; data controls; IDE and Studio access paths
Supports: AWS announced general availability of SageMaker AI training session APIs for user-authored post-training loops, named four supported base models, and said GPU-bound processing is billed by token while local reward-scoring time stops the service meter.
Reviewed: Programming model; ServiceClient, TrainingClient and SamplingClient hierarchy; operation semantics; responsibility matrix; token-billing boundary; LoRA scope; GSM8K and code-RL examples; checkpoint behavior; stop and expiry guidance
Supports: AWS documents a stateful operation-level API built around sample, forward_backward and optim_step, with local loop and reward logic, managed GPU execution, Model Registry checkpoints, an OpenAI-compatible sampling endpoint and vendor-run example results.
Reviewed: GA documentation overview; supported use cases; stateful and serverless properties; exact model identifiers and Region matrix; prefill, sample and train billing dimensions; default concurrent-session quota
Supports: The live documentation lists openai/gpt-oss-20b, google/gemma-4-31B-it, Qwen/Qwen3.6-27B and Qwen/Qwen3.8-27B, limits availability to three AWS Regions, breaks billing into prefill, sample and train tokens, and sets the adjustable default concurrent-session quota to two.
Reviewed: CreateTrainingSession setup; caller and execution-role permissions; managed policies; PassRole condition; service principal; checkpoint model package group; optional VPC and KMS requirements
Supports: AWS requires separate caller and execution-role permissions plus a SageMaker model package group; every saved checkpoint becomes a model package, and VPC or customer-managed KMS configurations add setup requirements.
Reviewed: Public repository metadata and Apache-2.0 license; sagemaker-train requirement; operation examples; four runnable recipe families; reference-result table; run artifacts; checkpoint resume; inference deployment; Region and billing summary
Supports: The public Apache-2.0 repository contains chat SFT, GSM8K RL, code RL and preference-KTO recipes, requires sagemaker-train 1.25.0 or newer, records configuration and metrics, and publishes AWS-reported reference results rather than independently reproduced benchmarks.
Supports: AWS exposes a dedicated Training sessions pricing tab and describes SageMaker as usage-based, but the reviewed public rate table did not render model-specific training-session token prices in the unauthenticated browser used for this review.
AccessAllGPT reviewed five NVIDIA-controlled public surfaces on October 23, 2026: the October 21 launch post, the live Cosmos product page, the consolidated NVIDIA/Cosmos repository, the Cosmos Data Factory repository and the Cosmos Framework repository. We checked the announcement date, named artifacts, availability paths, model tiers, model and code licenses, prerequisites, hardware guidance, supported training and serving paths, stated limitations, pricing disclosures and the provenance of performance and customer claims. We also queried the public GitHub repository records to confirm that the Cosmos, Data Factory and Framework repositories were live and recently updated. AccessAllGPT did not download model weights, request the gated guardrail artifact, provision Kubernetes or NVIDIA GPUs, run a blueprint, call a NIM service, train or evaluate a model, inspect customer data, reproduce any benchmark or obtain commercial terms.
Reviewed: October 21, 2026 publication date; Cosmos Data Factory blueprints and availability; framework integrations; simulation checkpoints; NIM and VSS performance claims; named customer examples
Supports: NVIDIA announced five Cosmos Data Factory blueprints, broader model support in Cosmos Framework, new Cosmos 3 Nano simulation variants and updated Cosmos 3 NIM microservices at GTC Berlin.
Reviewed: Cosmos 3 model tiers and parameter counts; modalities; platform components; use cases; hardware positioning; OpenMDW-1.1 license; post-training and deployment FAQ
Supports: NVIDIA lists Cosmos 3 Super at 64B parameters, Nano at 16B and Edge at 4B; describes text, image, video, ambient sound and action support; and says Cosmos world foundation models use OpenMDW-1.1.
Reviewed: October 2026 simulation release; base-model and example-checkpoint tables; quickstart access requirement; supported backends; framework links; limitations; license
Supports: The public repository exposes Cosmos 3 documentation and recipes, names the simulation checkpoints, requires accepted access to a gated guardrail repository for its first-video quickstart, and warns about temporal, object and physics artifacts.
Reviewed: Five reference blueprints; core skills; evaluator role; prerequisites; Kubernetes and OSMO support; per-blueprint GPU table; contribution status; code and documentation licenses
Supports: The repository documents five runnable blueprints, Kubernetes, storage and credential prerequisites, and GPU requirements ranging from zero to eight depending on the blueprint and whether model endpoints are external or local.
Reviewed: Training and inference scope; setup; tested eight-H100 recipes; supported post-training methods; model integrations; agent skills; serving backends
Supports: NVIDIA documents distributed training, inference, policy serving, reinforcement learning and distillation, with shipped training recipes configured and tested on eight H100 80 GB GPUs while allowing users to tune parallelism.
AccessAllGPT reviewed four first-party OpenAI surfaces on October 23, 2026: the official News RSS record for the October 22 announcement, the live Decisions API guide, the gpt-6-luna model reference and the API data-controls guide. We checked launch timing, current availability, endpoint and model identifiers, supported inputs and answer types, beta-to-GA additions, request limits, pricing, prompt caching, usage reporting, context specifications, data-control qualifications and implementation caveats. The canonical editorial page returned a Cloudflare challenge, so its title, URL, category, description and timestamp were verified through OpenAI’s official feed while technical claims were checked against directly accessible developer documentation. AccessAllGPT did not obtain API credentials, call /v1/decisions, measure latency or accuracy, compare it with the Responses API, validate probability calibration, inspect invoices, or verify account eligibility for ZDR, HIPAA, residency, Private Link or regional processing.
Reviewed: Official RSS title, canonical URL, Product category, description and October 22, 2026 publication timestamp
Supports: OpenAI’s official feed records the Decisions API announcement at 19:00 UTC on October 22, 2026 and describes it as a product for fast, focused and low-cost application decisions.
Reviewed: General availability; endpoint and model; typed question formats; supported inputs; beta-to-GA additions; question limits; interpretation guidance; prompt caching; usage reporting; pricing; availability; data controls; voice integration
Supports: OpenAI documents POST /v1/decisions with gpt-6-luna as generally available for predicate, choice and score answers over text, image, JSON and conversation evidence, with up to 400 questions, input-only billing and qualified data-control options.
Supports: OpenAI identifies gpt-6-luna as its efficient high-volume model, with text and image input, a 1.05-million-token context window, and standard model prices that differ from the Decisions endpoint’s output-free contract.
Reviewed: API data storage categories; default abuse-monitoring retention; Modified Abuse Monitoring; Zero Data Retention eligibility and behavior; residency and regional processing qualifications
Supports: OpenAI says default API abuse-monitoring logs can contain customer content and are retained for up to 30 days, while eligible approved customers can configure Modified Abuse Monitoring or Zero Data Retention subject to requirements and endpoint limitations.
AccessAllGPT reviewed five public first-party or developer-controlled surfaces on October 22, 2026: Recursive’s launch page, the live BF16 model card, Recursive’s documentation, NVIDIA’s dated GTC Berlin announcement and Recursive’s INT4 GGUF repository. We checked launch timing, artifact availability, identifiers, architecture, context limits, modalities, license, serving paths, hardware guidance, evaluation settings, reported results, speed claims, limitations and hosted access. Registry and page state are directly observable. Benchmark, speed, quality and automated-research statements are Recursive or NVIDIA claims. AccessAllGPT did not download the roughly 80 GB BF16 checkpoint or 26 GB INT4 artifact, run Flint, inspect training data, reproduce evaluations, test tool use, audit the Eureka Machine, measure memory or speed, or obtain hosted API access or pricing.
Reviewed: Launch description; architecture and active parameters; coding use case; context guidance; benchmark methodology; local speed claims; Eureka Machine contributions; quantization; supported runtimes; weights and license
Supports: Recursive describes Flint as a 40-billion-parameter mixture-of-experts coding model with 2.5 billion active parameters per token, publishes vendor-run evaluation and local-speed claims, and says weights are available under Apache 2.0 for local use.
Reviewed: BF16 artifact availability; model architecture; parameter counts; modalities; context configuration; knowledge cutoff; benchmark scores and methodology; hardware and download guidance; generation settings; training summary; limitations; license; provider status
Supports: The live model card identifies a 39.8B-parameter text model with about 2.5B active parameters, a 262,144-token prompt-plus-output configuration, BF16 weights of about 80 GB, one-B300-or-H200 SGLang guidance, Apache 2.0 licensing and no listed Hugging Face Inference Provider deployment.
Reviewed: Local and GPU runtime options; supported coding harnesses; setup chooser; first-task recommendation; release and troubleshooting paths
Supports: Recursive documents local use through rec, Ollama, Unsloth, LM Studio and llama.cpp; GPU serving through Transformers, SGLang and vLLM; and harness connections for OpenCode, Pi, Cline and OpenHands.
Reviewed: October 21 publication date; Flint launch summary; architecture; RTX and DGX availability; open-weight and license statement; NeMo post-training paths; 55% RTX Spark kernel claim; adjacent local-AI announcements
Supports: NVIDIA independently confirms the October 21, 2026 launch event and availability of Flint weights for NVIDIA local hardware, while repeating vendor-supplied performance and benchmark claims that remain unverified by AccessAllGPT.
Reviewed: INT4 GGUF artifact; Apache 2.0 license; model size; Q4_1 file size; Apple Silicon instructions; llama.cpp, Pi and OpenCode integration notes; training-data pointer
Supports: The public INT4 GGUF repository exposes a 26 GB Q4_1 artifact under Apache 2.0 and documents local use with llama.cpp and coding harnesses; it points back to the base card for training information.
AccessAllGPT reviewed five NVIDIA-controlled public surfaces on October 22, 2026: the dated launch announcement, the Confidential Bare Metal Dev Beta documentation, the live confidential-computing product page, NVIDIA’s July Blackwell inference benchmark and the released Confidential Containers v1.1.0 documentation. We checked announcement timing, deployment modes, platform scope, trust boundaries, attestation and key-release responsibilities, explicit exclusions, partner and availability language, benchmark design and commercial disclosures. Public page and documentation state are directly observable; security, performance, integration and partner deployment statements remain NVIDIA or named-partner claims. AccessAllGPT did not obtain a supported host, deploy a workload, perform attestation, inspect firmware, release keys, reproduce the benchmark, validate a provider implementation or receive pricing.
Reviewed: October 21 announcement date; Confidential Bare Metal launch; three deployment modes; Cohere and Google Cloud plans; supported NVIDIA platforms; provider and systems-partner lists; DSX OS integration statement
Supports: NVIDIA announced Confidential Bare Metal on October 21, 2026 as a third confidential-computing deployment mode beside containers and virtual machines. The announcement names platform and partner plans but does not publish general-availability dates, regions, prices or service levels.
Reviewed: Dev Beta status; deployment model; measured launch; attestation and key release; personas and responsibilities; threat model; exclusions; Grace Blackwell and Vera Rubin platform scope; engineering caveats
Supports: The Dev Beta documentation defines a dedicated-host model in which the tenant controls and trusts the software stack, attestation policy and keys while the infrastructure owner remains outside the runtime trust boundary. It explicitly excludes tenant administrators, denial of service, all physical attacks and all side channels from its protection claims.
Reviewed: Product overview; bare-metal, Kubernetes-container and virtual-machine modes; hardware-rooted attestation; data and model protection claims; platform generations; deployment descriptions; customer and partner examples
Supports: NVIDIA presents bare metal, Kubernetes confidential containers and confidential virtual machines as distinct operating models. The product page says attestation verifies assets before workloads release secrets and states that protection extends across supported GPU memory and interconnect paths.
Reviewed: July 2 publication date; hardware root of trust; remote-attestation workflow; stated protection scope; benchmark setup; Qwen 3.5 model and HGX B300 configuration; throughput and latency results; tested sequence lengths and concurrency; optimization notes
Supports: NVIDIA reports a virtual-machine benchmark—not a Confidential Bare Metal benchmark—using Qwen3.5-397B-A17B-FP8 on HGX B300. Across the displayed configurations, confidential mode changed throughput and time per output token by about 1% to 8%; the result remains vendor-run and workload-specific.
Reviewed: Version 1.1.0 status; reference-architecture scope; Kubernetes, Kata Containers and GPU Operator components; TEE and attestation model; workload configuration; supported deployment contexts; day-two operations boundary
Supports: The released v1.1.0 container documentation describes Kubernetes pods running in hardware-isolated virtual machines with Kata Containers and NVIDIA GPU Operator, making it a materially different trust and operations model from the new bare-metal Dev Beta path.
AccessAllGPT reviewed five IBM-authored or IBM-controlled public surfaces on October 22, 2026: the dated launch article, the public ensemble repository pinned to commit 7ec4923ae4ad36ff2202065ec549a6282251b961, its pinned default config, the granite-tsfm v0.3.11 release and the live package configuration. We checked release timing, artifact access, exact identifiers, aggregation behavior, quantile outputs, package requirements, license statements, evaluation design, result snapshots, caveats and pricing disclosures. Repository access and configuration are directly observable; all accuracy and rank results are IBM-reported from GIFT-Eval. AccessAllGPT did not install granite-tsfm, download the four checkpoints, execute the notebook, reproduce the benchmark, inspect the 97 dataset configurations or measure forecast quality, calibration, latency, memory, energy or cost.
Reviewed: October 22 publication date; four-member recipe; installation and usage; aggregation methods; October 20 GIFT-Eval comparison; extension guidance; application evaluation steps; limitations and links
Supports: IBM announced Granite Timeseries Ensemble R1 on October 22, 2026 and documents a four-checkpoint zero-shot forecasting recipe, point and quantile outputs, two unfitted aggregation methods, vendor-run GIFT-Eval results and deployment limitations.
Reviewed: Public and gated state; pinned revision; repository files; Apache 2.0 tag; default and expanded recipes; member checkpoints; methods; installation; GIFT-Eval methodology and results; intended use; limitations
Supports: The public, ungated repository was reviewed at commit 7ec4923ae4ad36ff2202065ec549a6282251b961. It contains configuration recipes rather than merged weights and links four default member repositories; its benchmark snapshot and ranks differ from the later launch article snapshot.
Reviewed: Model type; linear-pool aggregation; nine quantile levels; four exact member identifiers; FlowState revision; null fitted weights
Supports: The pinned machine-readable recipe selects linear_pool, quantiles 0.1 through 0.9, PatchTST-FM-r1, PatchTST-FM-r2, FlowState-r1 at revision r1.1 and TTM-r3, with no learned weight vector.
Supports: IBM published granite-tsfm v0.3.11 on October 21, 2026. The launch article requires granite-tsfm 0.3.11 or newer, while the pinned model card still states 0.3.10 or newer and shows older result attribute names.
Supports: The live project configuration marks granite-tsfm as beta, requires Python 3.11 through 3.13, declares Apache 2.0 and specifies dependencies including PyTorch 2.10 through 2.11 and Transformers 4.57.6 through 5.x.
AccessAllGPT reviewed six NVIDIA-controlled public surfaces on October 22, 2026: the dated Agent Toolkit announcement, the live Agent Knowledge Foundation and Kumo catalogs, the Kumo Relational NIM page, the dated OpenShell Reference Architecture release and the NemoClaw optimization blueprint. We checked announcement timing, named components, current links and prototype paths, model and API identifiers, deployment routes, pricing disclosures, partner and performance claims, and operational boundaries. Availability visible on those surfaces is directly observable; adoption, accuracy, speed, capacity and security-performance figures remain vendor-reported. AccessAllGPT did not create an NVIDIA account, generate an API key, download weights, clone repositories, run a NIM, deploy OpenShell, inspect partner systems or reproduce any claim.
Reviewed: October 21 announcement date; toolkit components; Agent Knowledge Foundation description; named Kumo models; Auto-Ontology; OpenShell and NemoClaw roles; partner adoption statements; retrieval performance claims
Supports: NVIDIA announced the expanded Agent Toolkit on October 21, 2026, naming Agent Knowledge Foundation, Nemotron Retriever, Kumo, Auto-Ontology, OpenShell, NemoClaw, NeMo Relay and NeMo Compass. Adoption and performance statements are NVIDIA or partner claims.
Reviewed: Component catalog; compatibility statement; Nemotron Retriever, Kumo and Auto Ontology descriptions; CUDA-X library roles; workflow architecture; published performance claims
Supports: The live product page describes components as individually usable with existing data platforms, ontologies, vector databases and agent frameworks. It lists retrieval, structured prediction, ontology generation, data processing, graph, optimization and vector-search components.
Reviewed: Kumo family scope; Relational, Tabular, Forecast and Anomaly model descriptions; open-weight links; NIM API links; Structured Data Models and Kumo-TS runtimes; ethical considerations
Supports: NVIDIA exposes four named Kumo routes for relational, tabular, forecasting and anomaly workloads. The page links downloadable weights for all four, NIM API prototypes for Relational and Tabular, and public runtime repositories.
Reviewed: Prototype endpoint; request model identifier; task and schema contract; API-key requirement; self-hosted NIM path; NVIDIA AI Enterprise evaluation statement
Supports: The live page offers a free prototype endpoint using model identifier kumo-relational and documents a self-hosted NIM path. It does not display a production per-request price on the reviewed surface.
Reviewed: October 21 release date; software and hardware layers; supported operating environments; partner list; availability paragraph; throughput, performance, quarantine and capacity claims
Supports: NVIDIA announced a reference architecture spanning OpenShell software, Vera Rubin, Vera CPUs, BlueField-4 and Sentry, with Linux, Kubernetes, Windows and major-cloud environment statements. All performance and enforcement results are vendor claims.
Reviewed: Blueprint purpose; open status; NeMo Compass and Eval Author workflow; Hermes and LangChain examples; held-out evaluation and A/B comparison steps; Try Now and GitHub access
Supports: The live blueprint documents a trace-to-evaluation workflow for changing prompts, tool descriptions or routing logic, with two runnable examples and saved comparisons. It is an evaluation blueprint, not evidence that an agent improved on a buyer workload.
AccessAllGPT reviewed four IBM-authored or IBM-controlled public surfaces on October 22, 2026: the October 21 launch article and the live repositories and model cards for Granite Speech 5.0 2B, Granite Speech 5.0 470M TurboCTC and Granite Speech 4.1 2B Plus. Repository records were pinned to commits f4e68169fa2a5751acc4b5da9957855e5e8fc3f1, 1dd23f46f6d4e6eae2531982ba2e33220086dd97 and e69efe37f97b6283c61f5a8dadda2b3eb67765d6. We checked release timing, current artifact access, model identifiers, parameter metadata, licenses, languages, task boundaries, training summaries, runtime requirements, published evaluations, caveats and pricing disclosures. Repository availability is directly observable; accuracy, robustness, speed and comparison results remain vendor-reported. AccessAllGPT did not download the checkpoints, run inference, reproduce benchmarks, inspect every training example or measure quality, latency, memory, energy or cost.
Reviewed: October 21 publication date; three-model release table; 5.0 2B languages, translation and keyword claims; TurboCTC speed and accuracy claims; 4.1 2B Plus attribution, timestamp and long-form claims; selection guidance; license and runtime summary
Supports: IBM announced one new Granite Speech 5.0 2B model and two revised speech models on October 21, 2026. The article distinguishes the high-accuracy translation model, the high-throughput edge model and the multi-speaker model; all performance figures are IBM-reported.
Reviewed: Public and gated state; pinned revision; parameter metadata; release date; license; supported languages and translation directions; training summary; architecture; keyword limit; evaluation descriptions; runtime support; segment guidance; limitations
Supports: The public, ungated repository was reviewed at commit f4e68169fa2a5751acc4b5da9957855e5e8fc3f1. Its API record reports 2,313,207,132 BF16 safetensors parameters. The card lists an October 20 release, Apache 2.0, six ASR languages, bidirectional translation with English, 2,000-term keyword biasing, and Transformers, vLLM, llama.cpp and MLX usage paths.
Reviewed: Public and gated state; pinned revision; October 21 update and August predecessor; parameter metadata; seven languages; training summary; punctuation; English WER comparison; speed statement; Transformers and MLX requirements; license
Supports: The public, ungated repository was reviewed at commit 1dd23f46f6d4e6eae2531982ba2e33220086dd97. The API reports 472,993,792 safetensors parameters. IBM says the October 21 revision adds six languages and native punctuation while reporting 5.51% average English WER versus 5.78% for the earlier English-only checkpoint.
Reviewed: Public and gated state; pinned revision; April release and October 19 update; parameter metadata; supported languages; speaker-attributed ASR; timestamps; long-segment tables; keyword biasing; updated Open ASR comparison; Transformers and vLLM requirements; license
Supports: The public, ungated repository was reviewed at commit e69efe37f97b6283c61f5a8dadda2b3eb67765d6. The API reports about 2.11 billion BF16 parameters. The card documents an October 19 update for five-language transcription, speaker labels, word timestamps and keyword biasing, with mixed rather than uniformly improved vendor-run benchmark results.
AccessAllGPT reviewed five first-party public surfaces on October 21, 2026: OpenAI’s dated partnership announcement, Google Cloud’s launch article, and OpenAI’s live model references for GPT-6 Astra, GPT-6.1 Sol and GPT-6 Luna. We checked announcement timing, current versus promised access, named models, direct OpenAI model IDs and specifications, API and Codex claims, billing path, published prices, governance statements and coming-soon controls. Google Cloud’s structured metadata says datePublished 2026-10-21 while its rendered article displayed October 22 during this review; we preserve that inconsistency and use OpenAI’s October 21 announcement date. AccessAllGPT did not obtain preview access, submit the interest form, call a Google Cloud endpoint, connect Codex, inspect a contract or invoice, or test the models or controls.
Reviewed: October 21 announcement date; Model Garden and Codex partnership scope; Marketplace transaction path; GCP commitment drawdown; Google Antigravity and Gemini Agent roadmap; general-availability timing; Microsoft cloud-partner statement
Supports: OpenAI says its frontier models and Codex workflows are coming to Google Cloud Model Garden, purchases will transact through Google Cloud Marketplace and can draw down GCP commitments, general availability is planned in the coming weeks, and Microsoft remains OpenAI’s primary cloud partner.
Reviewed: Publication metadata and rendered dateline; three named models; private-preview availability; API and Codex connection statements; Marketplace billing and Flexible Savings Plans; data-perimeter, training-use, infrastructure, identity and audit claims; coming-soon ZDR and Confidential Compute
Supports: Google Cloud names GPT-6 Astra, GPT-6.1 Sol and GPT-6 Luna, says they are rolling out in private preview to select enterprise customers, and says selected customers can use APIs and connect local Codex CLI and Desktop workflows. Google also describes Marketplace billing, governance controls and two security features that remain forthcoming.
Reviewed: Direct OpenAI API model ID; model positioning; input and output modalities; context and output limits; reasoning levels; Standard token prices; long-context multiplier; endpoints, features and tools
Supports: OpenAI documents gpt-6-astra for text and image input and text output with a 1,050,000-token context window, 128,000-token maximum output, and direct-API Standard prices of $10 input and $50 output per million tokens. These are not published Google Cloud preview rates.
Reviewed: Direct OpenAI API model ID; positioning; modalities; context and output limits; reasoning levels; Standard token prices; long-context and processing-mode modifiers; Responses and Chat Completions support
Supports: OpenAI documents gpt-6.1-sol for text and image input and text output with a 1,050,000-token context window, 128,000-token maximum output, and direct-API Standard prices of $2 input and $10 output per million tokens. These are not published Google Cloud preview rates.
Reviewed: Direct OpenAI API model ID; positioning; modalities; context and output limits; reasoning levels; Standard token prices; long-context and processing-mode modifiers; Responses and Chat Completions support
Supports: OpenAI documents gpt-6-luna for text and image input and text output with a 1,050,000-token context window, 128,000-token maximum output, and direct-API Standard prices of $0.10 input and $0.50 output per million tokens. These are not published Google Cloud preview rates.