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.
181decision guides
790source records
782scoped 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 first-party surfaces on October 17, 2026: Mistral’s October 15 launch article; the public Timestral-209M model card and repository API pinned to commit 9874d252ab0aae16f7f7c999f98f98d6ad7d8365; Mistral’s public Timestral inference repository; and the model-weight license. We also checked the API records for the public 5M, 28M and 123M repositories at commits bdd20cfc15a3fea0e4cb5ccef37f8d3ca7aa335f, 180698bbf5a50dda3516aa6419798437fb03a430 and 5a147287addda85f6acd8da3fb8858fe9af7e8f7. We verified announcement and repository dates, access state, model IDs, sizes, context, horizon, output contract, covariates, installation, inference routes, published benchmark protocol summaries, licensing, pricing disclosures and report status. Repository facts are directly observable; accuracy, Pareto-front, CPU-latency and comparison statements remain vendor-reported. AccessAllGPT did not download the weights, install the package, run Timestral, submit leaderboard jobs, use Mistral Forge or reproduce quality, calibration, latency, memory or cost results.
Reviewed: October 15 announcement date; four model sizes; zero-shot forecasting scope; covariates; benchmark claims; CPU latency claims; architecture and training summary; Mistral Forge; deployment; next steps
Supports: Mistral announced Timestral on October 15, 2026 as its first family of time-series foundation models. The announcement describes four sizes from 5M to 209M parameters, public benchmark and CPU-latency results, synthetic-first training, covariate-aware forecasting, Forge adaptation and on-premises deployment. Performance statements are vendor-reported.
Reviewed: Repository owner; public and gated state; pinned revision; creation timestamp; architecture; context and horizon; inputs and outputs; benchmark tables; installation; usage; report status; weight license
Supports: The public, ungated 209M repository was reviewed at commit 9874d252ab0aae16f7f7c999f98f98d6ad7d8365. Its card documents a 32,768-step context, a 1,024-step single-pass horizon, 21 forecast quantiles, CPU or GPU inference, vendor benchmark tables and a Modified MIT weight license. The technical report is listed as coming soon.
Reviewed: Repository identity and creation date; installation; four checkpoint identifiers and FP32 download sizes; forecast API; context and horizon behavior; covariates; architecture; benchmark protocol summary; maintenance; code and weight license boundary
Supports: Mistral publishes standalone Timestral inference code for Python 3.10 or newer and PyTorch 2.4 or newer. The repository lists public FP32 checkpoints of 21 MB, 111 MB, 490 MB and 837 MB, supports commit pinning and offline cache use, and separates Apache 2.0 code from Modified MIT model weights.
Supports: The weight license grants broad use and modification rights with attribution but withholds authorization when the user or employer had more than $20 million in global consolidated revenue in the preceding month. Mistral directs affected organizations to request a commercial license or use hosted services.
AccessAllGPT reviewed four IBM-authored or IBM-controlled surfaces on October 17, 2026: the October 15 launch article; the public 8B and 30B repositories and API records pinned to commits 0d1625d16df2d54cafb6bc5cb0650aec043f77d6 and 70b75c6fd254db33418e490714769ef028d16166; and the embedded Granite Speech repository pinned to 947f59af40db9791170a0628cf0f3f4812d720f1. We checked announcement and repository dates, access state, model identifiers, parameter metadata, context, adapters, audio architecture, invocation, licenses, runtime requirements, published evaluations, limitations, pricing disclosures and future-work language. Repository facts are directly observable; capability, memory efficiency, speed, benchmark and comparison statements remain vendor-reported. AccessAllGPT did not download the checkpoints, run Granite Switch, reproduce evaluations, invoke the demo, provision a hosted endpoint or measure memory, cache reuse, transcription, latency, throughput, quality or cost.
Reviewed: October 15 publication date; 8B and 30B model identifiers; adapter composition; single-endpoint claim; speech path; cache reuse; decision and guardian evaluation descriptions; serving guidance
Supports: IBM authors published the Granite Switch 4.1 launch article on October 15, 2026. It describes two composed checkpoints that combine a Granite 4.1 base, general decision and Guardian adapters, and an embedded Granite Speech encoder so chat, classification and English transcription can share one hosted endpoint. Performance and comparison statements in the article are vendor claims.
Reviewed: Repository owner; public and gated state; pinned revision; creation and modification timestamps; parameter metadata; model composition; release date; context; license; backends; adapter invocation; decision evaluation; requirements; limitations
Supports: The public, ungated 8B repository was reviewed at commit 0d1625d16df2d54cafb6bc5cb0650aec043f77d6. Its API record reports 8,776,400,896 BF16 safetensors parameters. The card lists an October 14 release date, a 131,072-token base context, Apache 2.0 licensing, Transformers and vLLM backends, two embedded aLoRA adapters, and an English 470M-parameter speech encoder.
Reviewed: Repository owner; public and gated state; pinned revision; creation and modification timestamps; parameter metadata; model composition; release date; context; license; backends; decision evaluation; dependencies; ethical considerations and limitations
Supports: The public, ungated 30B repository was reviewed at commit 70b75c6fd254db33418e490714769ef028d16166. Its API record reports 29,989,814,272 BF16 safetensors parameters. The card lists the same adapters, speech encoder, 131,072-token context, Apache 2.0 license and serving backends as the 8B checkpoint, while reporting a higher vendor-run Decision Index score.
Reviewed: Model identity; pinned revision; architecture; language; training-data summary; release date; license; inference paths; OpenASR and FFASR evaluation descriptions; hardware; intended use
Supports: The embedded speech component was reviewed at commit 947f59af40db9791170a0628cf0f3f4812d720f1. IBM describes it as a 470M-parameter, English-only, non-autoregressive conformer CTC model trained on about 60,000 hours of public or public-corpus-derived audio. Leaderboard speed and error-rate results are provider-reported and were not reproduced by AccessAllGPT.
AccessAllGPT reviewed five official surfaces on October 17, 2026: Black Forest Labs’ October 15 launch post, the distilled Hugging Face repository and API record pinned to revision 337337621b8526e120f2275d250242f4a6bedd75, the base repository pinned to 3f7fa5ee22ca1d5a5cc095ac7cc96e6a173b4c86, the public reference-code repository, and current BFL API pricing documentation. We checked release date, model identifiers, artifact visibility and gate state, model scope, parameter-count caveat, output and editing modes, reference limits, resolutions, inference steps, prompt-upsampling dependency, quantized variants, documented memory, runtime support, API pricing, license boundaries, safety defaults and benchmark provenance. AccessAllGPT did not accept the Hugging Face gate, download weights, invoke the API, run the model, reproduce images, latency or quality evaluations, audit training data, or obtain private access. Product specifications and all performance claims are attributed to their publishers.
Reviewed: October 15 release date; model scope; base and distilled variants; quantized builds; internal quality claims; NVIDIA-measured latency; memory claims; API identifier and price; weight license summary
Supports: Black Forest Labs announced FLUX 3 Image Klein on October 15, 2026 as a 5.6B-parameter image generator for text-to-image, editing, multi-reference generation and bounding-box control. It documents open-weight and API availability, a base and distilled checkpoint, FP8, INT8 and NVFP4 builds, and a starting API price of $0.009 for a 1K image. Quality, latency and hardware results are vendor or NVIDIA measurements, not AccessAllGPT results.
Reviewed: Repository owner; public auto-gated access; API revision; model card; model role; parameter scope; reference-image limit; prompt upsampler requirement; BF16, FP8 and NVFP4 files; steps; license label
Supports: The official repository was publicly visible but required accepting conditions before file access. Its API record identified revision 337337621b8526e120f2275d250242f4a6bedd75 and listed BF16 model components plus FP8 and NVFP4 variants. The card describes a 5.6B image transformer, a separate Qwen3-VL-4B-Instruct text encoder, up to ten reference images, 16-step distilled inference and the FLUX Kommunity License.
Reviewed: Repository owner; public auto-gated access; API revision; model card; base-checkpoint role; 50-step guidance; fine-tuning and research scope; files and license label
Supports: The official base repository was publicly visible behind the same acceptance gate at revision 3f7fa5ee22ca1d5a5cc095ac7cc96e6a173b4c86. Its model card positions the undistilled 50-step checkpoint with classifier-free guidance 3.5 for fine-tuning, LoRA training and research rather than the low-latency production path.
Reviewed: Public repository; model matrix; installation; artifact download sizes; CLI and Python examples; prompt upsampling; bounding boxes; runtime integrations; benchmark provenance; measured memory; safety defaults; code and weight licenses
Supports: The public reference repository supplies Apache 2.0 inference code, examples and integrations. It says the distilled package plus prompt upsampler is about 52 GB, all documented weights about 107 GB, prompt upsampling is required for intended quality, safety checks are off by default, and the weights use the separate FLUX Kommunity License. Those implementation and benchmark statements remain publisher-provided unless independently reproduced.
Reviewed: Current API pricing overview and FLUX 3 Image Klein pricing section
Supports: The official pricing surface was reviewed to cross-check that FLUX 3 Image Klein API charges depend on output size and that the launch post’s $0.009 figure refers to a 1K image, not one thousand images. Pricing is mutable and should be rechecked before deployment.
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.