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 three first-party or project-controlled source groups on October 15, 2026: the newly published Swiss AI release notes, live Hugging Face API and repository records for both final models, and version one of the Project Apertus technical report submitted October 11. We pinned the reviewed Hub records to 8B commit ef4bbc54d6fe658bc782280ff9f3b53a10a9758f and 70B commit 94888dd0db53f674b4c5304c1b726a7783d21393, then checked dates, identifiers, access state, license labels, model sizes, input and output modalities, context, training stages, artifact availability, provider-access language and evaluation qualifications. Repository and paper metadata are directly observable. Capability and benchmark statements are project-reported. AccessAllGPT did not accept the repository gate, download weights, receive API access, run either model or reproduce an evaluation.
Reviewed: Publication metadata; key enhancements; tokenizer, pretraining and post-training design; evaluation qualifications; reproducibility table; model availability; citation
Supports: The Swiss AI Initiative published the release notes on October 15, 2026. It describes 8B and 70B text-output models with image and experimental audio input, optional thinking, a 262,144-token context window, tool-use training, staged artifacts, Apache 2.0 weights and provider integrations. It also says the weights were first released on July 24.
Supports: The public Hub APIs expose swiss-ai/Apertus-v1.5-8B at commit ef4bbc54d6fe658bc782280ff9f3b53a10a9758f and swiss-ai/Apertus-v1.5-70B at commit 94888dd0db53f674b4c5304c1b726a7783d21393. Both are auto-gated, tagged Apache-2.0 and image-text-to-text, and contain model, image-tokenizer and audio-tokenizer weights.
Supports: The October 11 technical-report abstract says Apertus 1.5 continues the earlier models with four trillion additional tokens for 8B and two trillion for 70B, uses tokenizer-level image and audio inputs while keeping text-only output, extends context to 262,144 tokens and reports provider-run evaluation gains and remaining perception gaps.
AccessAllGPT reviewed three first-party or provider-controlled surfaces on October 15, 2026: Google’s October 13 robotics post and its published prompts, the current Gemini 4 Argon developer guide, and the linked ERQA repository pinned to commit 5bba488252c5b345a43335bfb9239b6bf12bd91c. We checked announcement and access dates, model ID, pricing, context language, demonstrated robot and simulation workflows, disclosed outcome numbers, benchmark artifact scope and missing deployment evidence. Videos, prompts, access terms and repository contents are directly visible artifacts. ERQA performance and robot outcomes are Google-reported. AccessAllGPT did not receive API access, robot hardware, data, checkpoints or logs; call gemini-4-argon; run ERQA; execute a robot or simulation; or reproduce any result.
Reviewed: October 13, 2026 publication date; ERQA claim; robot-control, Real2Sim, hindsight-labeling, value-function, simulation-data, self-improvement and paper-reproduction demonstrations; published prompts; access routes and model identifier
Supports: Google published a robotics-focused demonstration package for Gemini 4 Argon covering physical reasoning, code generation, simulation construction and closed-loop robot control. The post exposes representative prompts and videos, but it does not publish a common evaluation protocol, trial counts, failure counts, safety case or downloadable reproduction bundle for the demonstrations.
Reviewed: October 13 publication and rollout; model ID; paid API and subscriber access; introductory and December pricing; unified context budget; thinking levels; built-in tools; model positioning
Supports: Google identifies the callable model as gemini-4-argon on the paid Gemini API, documents a unified one-million-token input-and-output context budget and lists $2 input and $10 output per million tokens through November 30, followed by $4 input and $20 output from December 1.
Google DeepMind / embodiedreasoning on GitHub · Retrieved
Reviewed: Pinned repository identity and history; README scope; 400-example count; multimodal multiple-choice format; TFRecord artifact; example loader and evaluation harness; CC-BY-4.0 license; absence of a Gemini 4 Argon result record
Supports: The linked public ERQA artifact is a 400-example, multimodal multiple-choice dataset with a lightweight API evaluation harness. Its pinned README and repository history predate Argon and do not contain the 87% Argon score, prompts, run logs or a current comparison table; the score therefore remains a Google-reported result rather than a result reproduced from the linked repository.
AccessAllGPT reviewed four first-party or provider-controlled surfaces on October 15, 2026: OpenAI’s October 14 Third Moment customer story, Third Moment’s current product page, OpenAI’s live GPT-6.1 Sol model reference and OpenAI’s Daybreak API guide. We recorded announcement timing, named models and tools, concurrency and duration claims, the reported backtest change, token scale, orchestration and security controls, current public product availability, list pricing and missing evaluation evidence. The canonical customer story was retrievable as rendered first-party HTML after ordinary requests encountered a Cloudflare challenge; its publication record was also present in OpenAI’s official News RSS feed. Company outcomes are vendor or customer claims. AccessAllGPT did not receive credentials, traces, invoices, datasets, code, contracts, incident records, evaluation results or a briefing, and did not run an agent, model, backtest or security test.
Supports: OpenAI published a customer story reporting that Third Moment runs 10,000 agents concurrently for 36–48-hour investigations, used Codex to reduce one backtest from about three hours to about three to five minutes, consumed one trillion OpenAI tokens in one day, and uses Daybreak to test research environments before simulations launch.
Reviewed: Current product positioning; four-stage detect, confirm, improve and ship process; managed serving, evaluation, post-training, harness optimization and versioning scope; OpenAI-compatible endpoint example; contact route
Supports: Third Moment currently markets a managed OpenAI-compatible inference endpoint that detects recurring failures on approved customer traffic, builds evaluations, optimizes the model or harness, and ships a revised endpoint; the reviewed page gives no public price, service level, model catalog or self-serve access path.
Reviewed: Current model ID; model positioning; 1,050,000-token context window; 128,000-token maximum output; reasoning controls; Responses API tools; Standard, long-context, Fast, Ultrafast, Batch, Flex and regional pricing qualifications; data-residency statement
Supports: OpenAI currently documents gpt-6.1-sol with a 1.05-million-token context window and Standard short-context list prices of $2 input, $0.10 cached input, $2.50 cache writes and $10 output per million tokens; those rates do not reveal Third Moment’s actual mix, discounts, tool charges or total bill.
Reviewed: Organization and project approval prerequisites; access_programs.cyber request field; standard, daybreak_blue and daybreak_red options; compatible model table; explicit and omitted defaults; response inspection; validation and authorization errors
Supports: OpenAI documents Daybreak as an approval-gated Responses API access program: request selection does not grant access, project and model permission still apply, and a selected program does not guarantee every safety check or prompt will succeed.
AccessAllGPT reviewed four first-party or provider-controlled surfaces on October 15, 2026: AWS’s October 14 architecture post, the linked AWS Samples repository pinned to commit 9c51d2c881e9edbe81adbc84fedfa89cfa1b392b, the current Amazon Bedrock pricing page and the current AgentCore Runtime service-contract documentation. We recorded the named models and services, data flow, public artifact and license, deployment prerequisites, security controls, internal test setup, timing, cost claim, current image and knowledge-base pricing, and discrepancies between the prose and code artifact. Performance and cost results are AWS-reported internal measurements. Repository and pricing observations are directly visible artifact facts. AccessAllGPT did not receive AWS credentials, deploy the stacks, upload a document, invoke a model, inspect generated slides, measure grounding or reproduce the timing or cost.
Reviewed: October 14, 2026 publication date; solution overview; AgentCore supervisor and tool routing; RAG flow; model identifiers; security controls; deployment prerequisites; internal test setup and results; cost statement; asynchronous API pattern; cleanup and security considerations
Supports: AWS published a deployable reference architecture that uses AgentCore, Claude Sonnet 4.6, Titan Text Embeddings v2 and Stable Image Inpaint to generate PowerPoint files from enterprise documents. AWS reports a 70–90-second internal run and about $0.04 in model invocation cost, while identifying substantial supporting infrastructure and prerequisites.
Reviewed: Public repository identity; MIT-0 license; pinned commit; README prerequisites; configuration example; CDK bootstrap, build and deployment steps; architecture note; default-branch metadata and latest commit returned by the GitHub API
Supports: The code artifact is public under MIT-0 and can be pinned to commit 9c51d2c881e9edbe81adbc84fedfa89cfa1b392b. Its README requires .NET, Python, AWS CLI, Node.js, CDK, an S3 bucket and an API Gateway timeout quota increase; its example model configuration differs from the October 14 blog post.
Reviewed: Current Stability AI Image Services on-demand table; Stable Image Inpaint per-generation price; managed Knowledge Base storage and retrieval pricing; exclusions and separate model-provider charges
Supports: AWS currently lists Stable Image Inpaint at $0.07 per generation in Oregon, N. Virginia and Ohio, and separately meters knowledge-base storage and retrieval. Those current list prices do not reconcile with the blog’s approximately $0.04 total model-invocation estimate for a presentation containing two generated images.
Reviewed: Runtime service-contract purpose; supported HTTP, MCP, A2A and AG-UI protocols; ports and mount paths; message formats; discovery; authentication options; documented use cases
Supports: AgentCore Runtime currently documents HTTP, MCP, A2A and AG-UI service contracts with distinct ports, paths and message formats. This establishes the managed runtime interface, not that the presentation sample is production-ready or portable without change.
AccessAllGPT reviewed four first-party or provider-controlled sources on October 15, 2026: NVIDIA’s October 13 European-city roundup, Akila’s October 14 Côte-d’Or announcement, NVIDIA’s current Video Search and Summarization blueprint page, and NVIDIA’s Nemotron family page. We recorded named locations, partners, model families, software components, project verbs, stated availability, operational scope, reported outcomes, data-control claims and missing commercial terms. Product-page and project facts are directly observable; performance, savings, readiness and impact statements remain vendor or partner claims. AccessAllGPT did not receive municipal contracts, procurement records, model configurations, system access, telemetry, evaluation data, incident records, pricing, a briefing or responses from the named public authorities, and did not run the models or visit a deployment.
Reviewed: October 13, 2026 publication date and author; named software and infrastructure partners; NVIDIA model and blueprint references; project descriptions for the United Kingdom, France, Italy, Belgium, Sweden and Dubai; Côte-d’Or outcome claims; local AI-factory infrastructure section
Supports: NVIDIA describes municipal and regional projects using Cosmos, Nemotron, the Metropolis video-search blueprint, Omniverse and accelerated infrastructure, while using a mix of deployment, testing, planning and exploration language across the projects.
Reviewed: October 14, 2026 announcement; programme scope and architecture; named Nemotron, Cosmos, OpenUSD and Omniverse components; represented public sites; climate, accessibility, energy and safety use cases; hosting, governance and traceability statements
Supports: Akila says the next phase covers more than 200 public sites across 8,700 square kilometres, combines a governed spatial model with ThinkDeep and Akila agents, streams to departmental teams in Dijon, and keeps platform hosting in France.
Reviewed: Blueprint identity and availability labels; stated video-ingestion, summarization and interactive question-answering purpose; listed cosmos-reason2-8b and NVIDIA-NEMOTRON-NANO-9B-V2 components; GitHub and cloud-deployment routes; trial-service and model-license notices
Supports: NVIDIA presents the VSS blueprint as a launchable enterprise blueprint for live or archived video, currently listing cosmos-reason2-8b and NVIDIA-NEMOTRON-NANO-9B-V2 and linking deployment, source and governing terms.
Reviewed: Nemotron family overview; open-model and multimodal positioning; edge-to-cloud availability statement; Hugging Face and model discovery routes; agentic-AI benefits; deployment positioning and vendor capability claims
Supports: NVIDIA describes Nemotron as a family of openly available multimodal models for long-running agents and provides model-discovery routes, but the family page does not identify which exact checkpoint, configuration or service contract each city project uses.
AccessAllGPT reviewed Google’s October 14, 2026 announcement and the European Commission’s current RAISE program page on October 14, 2026. We checked the announcement date, organizations, number of researchers, compute-credit amount, named tools, stated access route and scientific use cases, then compared those claims with the Commission’s description of RAISE, its funding model and intended resources. Google’s capability and impact statements are vendor claims. AccessAllGPT did not receive an application, eligibility rules, selection rubric, award agreement, credit schedule, model credentials, quotas, pricing terms, support commitment, research output or private briefing, and did not use the program or named tools for this report.
Reviewed: October 14, 2026 publication date and author; researcher count; compute-credit amount; named scientific tools; RAISE connection; stated scientific use cases and program rationale
Supports: Google says Google DeepMind and Google Cloud will give up to 200 European scientific researchers direct access to five named scientific AI tools plus $3 million in compute credits through a commitment supporting the EU RAISE initiative.
European Commission Directorate-General for Research and Innovation · Retrieved
Reviewed: Science for AI and AI in science; funding opportunities; upcoming RAISE actions; background; RAISE operating concept; Horizon Europe pilot; Networks of Excellence; pooled compute, data, talent and research funding
Supports: The European Commission describes RAISE as a virtual institute intended to pool compute, data, talent and research funding for European researchers, with an initial Horizon Europe-funded pilot and additional actions; the page does not publish terms for Google’s separate pledge.
AccessAllGPT reviewed four first-party surfaces on October 14, 2026: OpenAI’s October 13 Higgsfield customer story, Higgsfield’s current public creative-suite homepage, Higgsfield’s public API model catalog and OpenAI’s current GPT-6 Astra model reference. We checked the announcement date, named products, what is described as available now versus future ambition, the role assigned to GPT-6 Astra, native model modalities, public API exposure, displayed clip limits, list pricing and published evaluation evidence. Product capabilities and outcomes are vendor and customer claims. AccessAllGPT did not receive Higgsfield or OpenAI credentials, a workflow diagram, prompt, model trace, project file, generated source assets, 10-minute output, bill, latency result or briefing, and did not run the workflow.
Reviewed: October 13, 2026 publication date; headline and product summary; company, region, industry and product labels; longer-video workflow; GPT-6 Astra tool calling; GPT-Image-2.5 claims; Codex production workflow; prospective GPT-Live-1 voice-to-video section
Supports: OpenAI published a customer story saying Higgsfield now uses a GPT-6 Astra-based workflow to generate instructional videos over 10 minutes, while separately describing GPT-Image-2.5, Codex and a future voice-to-video ambition involving GPT-Live-1.
Reviewed: Current public product navigation; featured video, image, audio, agent and API surfaces; Supercomputer attribution to GPT-6 Astra; current Wan 3.0 Unlimited duration copy; Cinema Studio, Genjutsu and creative-suite positioning
Supports: Higgsfield’s public site currently presents Supercomputer as an agent powered by GPT-6 Astra and exposes a multi-product creative suite; its featured Wan 3.0 Unlimited card advertises individual videos up to 30 seconds, not a single 10-minute generation endpoint.
Reviewed: Current public API catalog; featured and listed video models; provider attribution; supported input types; displayed clip-duration limits; Higgsfield-exclusive labels; public model descriptions and image-product price shown in catalog
Supports: Higgsfield’s public API catalog lists several short-form video endpoints with displayed maxima of 15 or 30 seconds; it does not expose a named GPT-6 Astra long-video model or a public over-10-minute generation endpoint on the reviewed catalog page.
Reviewed: Model identifier and positioning; text and image input; text output; 1,050,000-token context window; 128,000-token maximum output; reasoning controls; Responses API tools; Standard prices; long-context threshold
Supports: OpenAI documents gpt-6-astra as a text-output reasoning model with image input, tool support, a 1.05-million-token context window and Standard list prices of $10 input and $50 output per million tokens; it is not documented as a native video-output model.
AccessAllGPT reviewed three first-party Google surfaces on October 14, 2026: Google DeepMind’s same-day project announcement, its May 20 National AI Partnership announcement, and the current Google AI for Developers Gemma family documentation. We checked the announcement date, participating organizations, intended availability, consent and data-flow claims, model naming, pipeline design, fine-tuning environment, training-data description, testing claims, published evaluation evidence, artifact access and pricing. The architecture, governance and performance descriptions are vendor and partner claims. AccessAllGPT did not receive an application, model checkpoint, model ID, device build, test set, metrics, evaluation report, rollout date or briefing, and did not test the system.
Supports: Google DeepMind and GovTech Singapore developed an on-device scam-detection system that sends suspicious messages from a lightweight first-stage classifier to a fine-tuned Gemma model for multi-turn analysis, while stating that application deployment will be phased and consent-based.
Reviewed: May 20, 2026 partnership announcement; National Partnerships for AI context; named Singapore public-sector, research, health, education, accessibility and sustainability programs; responsible-deployment framing
Supports: Google DeepMind described the broader national partnership with Singapore in May 2026 and identified public-sector transformation and locally developed applications as central goals; the October scam detector is presented as work under that partnership.
Reviewed: Current Gemma family overview; model sizes and capabilities; local and edge deployment paths; tuning documentation; memory-planning guidance; model cards; licensing and prohibited-use links
Supports: Google documents Gemma as a family of downloadable models with local and edge deployment and fine-tuning paths, but this family documentation does not identify the exact checkpoint used by the Singapore scam-detection project.
AccessAllGPT reviewed four first-party or provider-controlled surfaces on October 14, 2026: Liquid AI’s October 13 release article, the public LFM2.5-24B-A2B repository and model card at commit 1769516ac588c4c558089c94f27d96b0dc67b183, Liquid AI’s official GGUF repository, and the complete LFM Open License v1.0. We checked the announcement date, artifact access, model IDs, weight format and size, total and active parameters, architecture, context length, languages, supported runtimes, benchmark scope and provenance, throughput conditions, hosted availability and commercial-use terms. Repository and artifact facts are directly observable; benchmark and speed results are Liquid AI claims. AccessAllGPT did not download the roughly 48 GB BF16 repository, execute the model, reproduce a benchmark, measure memory or speed, inspect training data, audit weights or code, test tool-call safety, or obtain legal advice.
Reviewed: October 13, 2026 publication date; architecture and training summary; 16-benchmark table; benchmark runner note; edge and B300 throughput claims; supported inference engines; Transformers example; agent-harness configuration; artifact availability
Supports: Liquid AI announced LFM2.5-24B-A2B on October 13 as a downloadable reasoning and tool-use model, described its training and architecture, and published provider-run benchmark and throughput results.
Reviewed: Public repository at commit 1769516ac588c4c558089c94f27d96b0dc67b183; model metadata; 23.97B BF16 parameters; active-parameter count; architecture; context and vocabulary; languages; usage examples; benchmark claims; inference-engine support; model files and license metadata
Supports: The public, ungated repository exposes ten Safetensors weight shards and identifies a 24.0B-parameter text model with 2.45B active parameters, 131,072-token context, 64 experts with top-4 routing and LFM Open License v1.0.
Reviewed: Public quantized repository; llama.cpp usage; QAD Q4_0 artifact; Q4_0, Q4_K_M, Q5_K_M, Q6_K, Q8_0, BF16 and F16 variants; displayed download sizes; inference-provider availability; license metadata
Supports: Liquid AI provides official GGUF variants from 13.6 GB at Q4_0 through 48 GB at BF16/F16 for local llama.cpp use; Hugging Face showed no hosted inference provider for the GGUF repository when reviewed.
Reviewed: Definitions; copyright and patent grants; redistribution conditions; commercial-use restriction; $10 million annual-revenue threshold; nonprofit exception; trademarks; warranty; liability and termination
Supports: The custom license permits use and redistribution subject to its terms, but does not license commercial use by a legal entity with annual revenue of $10 million or more.
AccessAllGPT materially updated its October 1 early-access briefing after reviewing five first-party Google surfaces on October 14, 2026: the October 13 launch post, the Google AI Studio developer guide, the developer availability notice, the Gemini app availability notice and Google’s safety article. We checked the announcement and rollout dates, account routes, API model identifier, introductory and later list prices, context language, thinking controls, subscriber eligibility, product availability, reported evaluations and disclosed safeguards. Availability, identifiers, interface specifications and list prices are treated as current vendor service evidence; performance and safety results remain labeled Google-reported. We did not receive Google credentials, call the API, inspect account-specific quotas, reproduce evaluations or test the product and safety behavior.
Reviewed: October 13 launch and global availability; developer, enterprise and subscriber routes; introductory pricing; engineering and professional-work examples; vendor-reported evaluations; safety summary; footnotes
Supports: Google says Gemini 4 Argon became globally available on October 13, 2026 through the Gemini API, AI Studio, Gemini platform, Antigravity, Gemini app and AI Mode in Search, with access varying by product and subscription. It states introductory API pricing of $2 per million input tokens and $10 per million output tokens, with cached input 95% below the input rate.
Reviewed: Publication date; model overview table; gemini-4-argon model ID; introductory and December pricing; unified context window; thinking levels; API changes; tool support; workflow guidance
Supports: Google’s developer guide identifies the API model as gemini-4-argon, sets medium as the default thinking level, documents low, medium and high reasoning controls, and describes a unified 1-million-token input-and-output context budget. It lists $2 input and $10 output per million tokens through November 30, then $4 and $20 from December 1.
Reviewed: October 13 availability notice; paid Gemini API access; AI Studio and Android Studio routes; AI Ultra access; Google Antigravity availability; linked developer resources
Supports: Google says developers can build with Argon through the paid Gemini API tier in Google AI Studio and Android Studio, while Google AI Ultra subscribers can also use it in AI Studio and Google Antigravity.
Reviewed: October 13 app launch; Pro and Ultra eligibility; tasks mode; Antigravity in the Gemini macOS app; connected-app claims; response and progress-update claims
Supports: Google says AI Pro and Ultra subscribers can access Argon in the Gemini app and use tasks mode. It also says the Gemini macOS app includes an Antigravity coding interface and that Gemini connects to more than 40 apps.
Reviewed: Broad rollout; Fairwind expansion; cyber and biology capability findings; input screening; activation probes; continuous monitoring; jailbreak and honeypot claims; prompt-injection and misalignment controls; safety-report link
Supports: Google says Argon solved all 12 of its end-to-end simulated cyberattack scenarios and that elevated cyber and biology capabilities triggered additional safeguards. It describes input screening, activation probes and continuous monitoring, and reports no covert-action behavior in its honeypot tests; these are vendor-reported findings.