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 Google’s September 10, 2026 first-party launch post and the live Gemini Apps Privacy Hub on September 13, 2026. The announcement returned HTTP 200 and was checked for date, operating-system support, global availability, shortcut, product surfaces and its subscription footnote. The privacy page returned HTTP 200 and was reviewed for activity, retention, Connected Apps and Spark data boundaries. The linked gemini.google/desktop installer page was unreachable from our publishing environment after repeated connection attempts, so we did not inspect the installer, verify binary signatures, install the app, authenticate an account or test performance and features. Product facts and capability statements below are Google claims; operational recommendations are AccessAllGPT analysis.
Reviewed: September 10, 2026 publication date; launch summary; keyboard shortcut; workspace, Connected Apps and media features; global Windows availability; subscription footnote
Supports: Google announced the Gemini app for Windows on September 10, 2026 and says it is available globally for Windows 10 and 11. The post documents Alt + Space access, a dedicated Gemini workspace, Gmail and Drive connections, image and video creation, and an AI-subscription requirement for Gemini Spark and Gemini Omni with availability varying and an 18+ condition.
Reviewed: Gemini Apps Privacy Notice; data collected and used; Keep Activity; retention; Connected Apps; Gemini Spark remote browser and remote computer; work and school account distinction
Supports: Google says Gemini can process prompts, uploads, generated content, Connected Apps data and device or browser context. Keep Activity controls whether future chats appear in activity and are used to improve AI models, subject to stated exceptions; Spark can save task files and share necessary information with services and third parties. Work and school accounts may follow different terms.
AccessAllGPT reviewed AWS’s September 11, 2026 report, its July 30 pricing announcement, the live Bedrock pricing page and the public benchmark repository on September 13, 2026. We pinned repository commit d0e60ba1050be7cf880a4ca7b206c1442088d3aa and inspected its README plus selected checked-in AIME, GPQA, DeepSearchQA and GDPval JSON artifacts. We confirmed the named backends, model IDs, sample counts, reasoning settings, grading disclosures, repricing factors and selected headline values from those files. We did not execute the paid model benchmarks, reproduce web searches, inspect every artifact or independently validate answer quality. Results are AWS/OpenAI-authored vendor evidence; interpretations and adoption gates are AccessAllGPT analysis.
Supports: AWS published results for GPT-5.6 Luna, Terra and Sol on Amazon Bedrock against GPT-5.4 mini and nano on the OpenAI API. The post states sample sizes, model configurations, reported quality and cost outcomes, pricing assumptions, latency scope, limitations and reproduction commands.
AWS and OpenAI benchmark repository on GitHub · Retrieved
Reviewed: README; suite descriptions; backend and model identifiers; quality methodology; checked-in AIME, GPQA, DeepSearchQA and GDPval result JSON; judge caveats; repricing records; license
Supports: The pinned repository exposes the harness and timestamped result artifacts behind the AWS post. AccessAllGPT checked the recorded sample sizes, model IDs, reasoning settings, token and cost fields, grading thresholds, judge disclosure and selected headline values without making paid model calls.
Reviewed: July 30, 2026 date; Luna and Terra reductions; Sol statement; effective date; automatic application; supported US regions; Responses API access
Supports: AWS says on-demand Bedrock pricing fell 80% for GPT-5.6 Luna and 20% for GPT-5.6 Terra effective July 30, 2026, while Sol pricing was unchanged. It lists Luna and Terra access in three US regions through the OpenAI Responses API on bedrock-mantle.
Reviewed: OpenAI provider section; frontier-model selector; GPT-5.6 Luna, Terra and Sol entries; pricing-tier labels; region-dependent table structure
Supports: The live pricing page listed GPT-5.6 Luna, Terra and Sol in its OpenAI model selector on retrieval. Its initial server-rendered view exposed a different default model table, so AccessAllGPT did not treat that view as independent confirmation of the exact Luna or Terra rates recorded by the benchmark.
AccessAllGPT wrote and ran scripts/audit-agent-json-duplicate-keys.mjs on 2026-09-12 using Node v24.10.0 on Darwin arm64, Python 3.9.6 and Ruby 2.6.10. Five fixed JSON tool-call fixtures covered one unique-key control and four duplicate-member cases: top-level tool, nested path, a Unicode-escaped equivalent of path and a nested approval flag. A small event-preserving scanner decoded member names and rejected duplicates before object construction. We compared each runtime’s parsed value structurally, measured a deliberately unsafe first-match approval preview against the Node execution object, and checked what a JSON.parse reviver could observe. The deterministic artifact is artifacts/agent-json-duplicate-keys-audit.json; its embedded pre-self-reference SHA-256 is 702396229933fe5ed3d6abaf5fef13ed01df1a1eadf8b5dbf3516efe22181127 and the final file SHA-256 is ab55262b645882d2d2698abef3f8def51b064c225ecf316a40c3d147d8fe1570. Standards language, runtime documentation, vendor guidance, local observations and AccessAllGPT recommendations are labeled separately.
Reviewed: Section 4 Objects, including member-name uniqueness and interoperability behavior
Supports: RFC 8259 says object names SHOULD be unique, objects with unique names are interoperable, and behavior is unpredictable when names are not unique; it notes that many implementations report only the last pair while others error or preserve duplicates.
Reviewed: Repeated Names Within an Object and the default decoder’s documented behavior
Supports: Python documents that its json module accepts repeated names by default and uses only the value of the last name-value pair. This independently documents one runtime behavior; it does not define a safe agent protocol.
Reviewed: Syntax, return value, reviver traversal and examples for parsing JSON text
Supports: MDN documents JSON.parse conversion and post-parse reviver traversal. The local harness, rather than MDN, establishes that the tested reviver received one decoded path key after duplicate collapse.
Reviewed: Structured outputs, tool approvals, untrusted data flow, guardrails and evaluation guidance
Supports: OpenAI’s vendor-authored guidance recommends structured outputs and tool approvals among layered agent controls. It does not specify duplicate-member handling and is not evidence that those controls are safe when approval and execution consume different representations.
AccessAllGPT reviewed the September 3 Funes launch, v1.3.0 release, documentation, security policy, source at commit 72c108157ffd5721a1cbb68e3394cfa5eeaaac7f and the published handoff-vs-recall benchmark on 2026-09-12. We downloaded the versioned release asset rather than piping the installer, verified the macOS arm64 binary as funes 1.3.0 with SHA-256 3d9c8aec11cbd604a94dde72753d0683ec289cf0dd6748eb03a8355d2c258826, and ran it under isolated HOME and XDG directories. A synthetic four-turn Codex trace contained one useful queue decision and one explicit instruction-like injection but no real credentials. Funes indexed one session into four chunks in 0.17 seconds; scan and get returned both passages verbatim with provenance. Indexing warned that TruffleHog was absent and continued locally. We did not bind an agent, publish a memory, execute recalled text, download the 1.11 GB reranker, or claim a semantic-recall benchmark. Source hashes and run results are preserved in artifacts/funes-1-3-memory-trust-boundary-audit.json and validated by scripts/audit-funes-memory-boundary.mjs. Vendor product claims, author-run benchmark evidence, independent threat evidence, local observations and AccessAllGPT guidance are labeled separately.
Reviewed: Publication date; installation; indexing and recall pipeline; local and shared memory; secret handling; benchmark summary; supported agents
Supports: Hugging Face launched Funes as a durable memory layer for Claude Code, Codex, pi and Hermes. The article describes local embedding and reranking, optional private Hub datasets, raw-passage provenance and an author-run two-task benchmark.
Reviewed: Release timestamp; binary assets; VERSION and SHA256SUMS assets; platform coverage
Supports: GitHub records v1.3.0 on September 1, 2026 and publishes three platform binaries plus release checksums. This fixes the tested product version and artifact boundary.
Supports: The inspected source pins the Lance storage dependency and local embedding/reranking models, implements hybrid retrieval and recency weighting, and separates best-effort index-time redaction from a fail-closed publish gate.
Supports: The project explicitly says third-party memory is untrusted input capable of prompt injection, recommends fine-grained tokens, and warns that deleting later commits does not erase an exposed secret from repository history.
Supports: The documented index keeps raw text, thinking, tool calls and tool results in tiers, uses BAAI/bge-small-en-v1.5 embeddings, and derives deterministic chunk IDs from session, turn, block and split coordinates.
Supports: The push boundary requires TruffleHog and reconstructs complete blocks before scanning. It can withhold secret-bearing rows, but selection is pre-publication only and no command retracts an already-published session.
Supports: The author-run benchmark reports 30 runs over two project-specific tasks. Recall passed all six of its runs and used fewer weighted tokens per successful task than the compared successful channels; one handoff preparation figure is reconstructed rather than read from a surviving receipt.
Reviewed: Description; direct and indirect injection; impacts; prevention and mitigation; RAG attack scenario
Supports: OWASP describes indirect prompt injection through external files and websites, says RAG and fine-tuning do not fully mitigate it, and recommends least privilege, human approval, segregating external content and adversarial testing.
arXiv; authors from ETH Zurich and Google DeepMind · Retrieved
Reviewed: Abstract; evaluation framing; tool-using agent threat model; benchmark scope and version history
Supports: Independent researchers frame tool-returned untrusted data as an agent prompt-injection surface and introduce a benchmark for measuring attacks and defenses. It supports the threat model, not a finding about Funes specifically.
AccessAllGPT reviewed Google’s September 4 launch, the live Lyria family page, Gemini API music-generation guide, pricing table, API terms, Lyria prompt guide and SynthID page, retrieved 2026-09-05. We wrote and ran scripts/audit-lyria35-contract.mjs on Node v24.10.0, Darwin arm64. The audit fetched all seven public pages, asserted the launch date, model IDs, 44.1 kHz output, price rows, ten-image limit, single-turn boundary, paid-service data terms and the repeated legacy Java identifier, and wrote response status, byte counts and SHA-256 hashes to artifacts/lyria35-contract-audit.json. No Gemini API credential was present, so we did not generate or listen to audio, measure duration or latency, test prompt adherence, inspect a binary watermark, or validate billing. Google supplies the only currently located Lyria 3.5 capability evidence; no independent model-specific evaluation was available at publication.
Reviewed: September 4 publication date; launch summary; Gemini app and API availability; track controls; global and product availability
Supports: Google announced Lyria 3.5 on September 4, 2026 and says it is available in the Gemini app, Gemini API, Google AI Studio, Flow Music and Google Vids. Expressive-vocal, arrangement and fidelity statements are vendor claims.
Reviewed: Lyria 3.5 introduction; model family; capabilities; prompt examples; artist partnerships; safety; limitations; product access surfaces
Supports: The family page says Lyria 3.5 can generate tracks up to three minutes, accepts image prompts, supports lyrics and multiple languages, exports high-fidelity audio and embeds SynthID. It provides demonstrations, not a controlled evaluation.
Supports: The live API contract names lyria-3.5 for full songs and lyria-3-clip-preview for fixed 30-second clips, with 44.1 kHz stereo MP3 output, optional WAV for 3.5, up to ten images, single-turn generation, nondeterminism, safety filters and SynthID. Its Java examples repeatedly name the older lyria-3-generate-001 identifier.
Supports: The September 5 pricing page lists no free API tier for Lyria music generation, $0.08 per Lyria 3.5 full song, $0.04 per Lyria 3 Clip Preview request and $0.08 per legacy Lyria 3 Pro Preview full song. The table labels paid-tier content as not used to improve products.
Reviewed: Age and region requirements; API-client restrictions; generated content; unpaid and paid service data use; logging; payment and disclaimers
Supports: The terms require API users and API-client audiences to be 18 or older, limit use to professional or business purposes, distinguish unpaid from paid data use, disclaim exclusivity in generated content and leave legal responsibility with the developer.
Supports: The prompt guide recommends specifying genre, tempo, instrumentation, dynamics, vocal profile and lyrics, and presents image-to-music as mood interpretation. It supplies a useful test-factor inventory but no adherence rates.
Supports: Google describes SynthID as an imperceptible watermark and detector for AI-generated audio and other media. This is first-party technology documentation, not an independent false-positive, false-negative or transformation-robustness evaluation of Lyria 3.5 outputs.
AccessAllGPT wrote and ran scripts/audit-rag-overlap-crowding.mjs with Node v24.10.0 on Darwin arm64 on 2026-09-05. The deterministic synthetic corpus contains 24 documents of 240 words and 12 queries. Each target places a required cause token at word 77 and answer token at word 80 around an 80-word chunk boundary; a paired distractor shares topic and cause terms but not the answer. We compared 0%, 25%, 50% and 75% overlap with fixed top-k=4 and an explicitly simple query-term-frequency retriever. The run emitted artifacts/rag-overlap-crowding-audit.json; its embedded pre-self-reference SHA-256 is d7dbda9bb754342061a14ab016618debd2d8a0064f4e103d74155d00f7bf73f5, and the final JSON file SHA-256 is 2386a468336133f2473d653fbb48ff9da7b0fb06348fed2632b27dbc7b4883f5. We also relied on our 2026-08-06 review of the original RAG paper and OpenAI’s vendor implementation guidance. Academic evidence, vendor guidance, local observations and AccessAllGPT recommendations are labeled separately.
arXiv (authors from Facebook AI Research, University College London and New York University) · Retrieved
Reviewed: Abstract, introduction, methods, experiments, results, discussion and limitations of the paper’s experimental scope
Supports: The authors define and evaluate a specific RAG architecture combining parametric sequence-to-sequence memory with retrieved non-parametric Wikipedia passages. The paper establishes retrieval as part of the generation system in its tested scope; it does not prescribe a universal chunk size or overlap.
Reviewed: LLM optimization context, prompt engineering, retrieval-augmented generation, fine-tuning and production accuracy sections
Supports: OpenAI’s vendor-authored guidance distinguishes context optimization for missing, stale or proprietary knowledge from model optimization for inconsistent behavior and recommends an evaluate–hypothesize–change–evaluate cycle. It is implementation guidance, not independent proof for an overlap setting.
AccessAllGPT reviewed Anthropic’s launch page, live model overview, what-is-new page, migration guide, pricing table and 212-page system card, plus AWS availability evidence and Harbor’s public Terminal-Bench-Science surface, all retrieved 2026-09-04. We downloaded the system-card PDF, recorded SHA-256 b0d59edc7a60eef32a879c13d713cce60c3fefd7e6b5183afdc8b835af3c8c39, and extracted 212 pages with pypdf 6.17.0 under Python 3.11.15. We wrote and ran scripts/audit-fable51-contract.mjs under Node to fetch the live docs, assert the model ID, context/output limits, price rows and migration warnings, and calculate transparent cache-workload scenarios. No Anthropic API credential or Fable 5.1 entitlement was available, so we did not claim model-output, latency, tool-use or safety reproduction.
Reviewed: Introduction; performance; scientific research; safety, security and alignment; Mythos; cost and availability; benchmark notes
Supports: Anthropic introduces Fable 5.1 as generally available and Mythos 5.1 as the same base model behind a restricted safeguards/access layer. It states unchanged input/output rates, lower cache-read pricing, phased Enterprise Frontier Safeguards, effort defaults, benchmark results and safety claims. These are vendor claims.
Supports: The live product contract names claude-fable-5-1, a 1M-token context window, 128K maximum output, $10/MTok base input and $50/MTok output. Anthropic recommends Opus 5 first for most workloads and Fable only when demanding long-horizon evaluations justify it.
Anthropic Claude Platform documentation · Retrieved
Reviewed: Models and availability; context; thinking and effort; breaking changes; progress updates; pricing; content provenance
Supports: The release changes forced tool use, thinking-block compatibility and conversation-edit behavior. It adds beta per-message effort, turn-scoped system messages, progress updates, content provenance and a lower cache-hit price.
Supports: The migration guide documents 400 responses for unsupported thinking configurations and assistant prefill, the forced-tool incompatibility, model-bound thinking blocks, invalidation after editing earlier turns, and platform-specific migration work.
Anthropic Claude Platform documentation · Retrieved
Reviewed: Model pricing table; cache write and hit columns; Fable 5.1, Fable 5 and Opus 5 rows
Supports: At retrieval, Fable 5.1 costs $10/MTok base input, $12.50 for five-minute cache writes, $20 for one-hour cache writes, $0.25 for cache hits/refreshes and $50 for output. Fable 5 cache hits cost $1; Opus 5 is $5/$0.50/$25 for base input/cache hit/output.
Reviewed: Executive summary; autonomy and alignment; cyber and bio; safeguards; behavioral audits; capability evaluation methods and limitations
Supports: The 212-page card reports model and safeguard evaluations, including a vendor-run capability table, methodology details, external METR testing, uncertainty, and disclosed regressions. It says Mythos 5.1 slightly regressed on overall misaligned behavior versus Opus 5 while improving versus Mythos 5 and Sonnet 5.
Reviewed: September 1 posting date; availability statement; Amazon Bedrock and Claude Platform on AWS access paths
Supports: AWS independently corroborates general availability on two AWS surfaces as of September 1, 2026. It does not independently validate Anthropic capability or safety claims.
Supports: Harbor exposes the public benchmark as a research-workflow dataset spanning scientific disciplines. The browser-accessible leaderboard shell did not expose a Fable 5.1 row at retrieval, so it cannot independently confirm Anthropic’s new score.
AccessAllGPT reviewed Google’s September 3 launch, the 43-page WeatherNext 3 paper, the WeatherNext developer overview, benefits-and-limitations guide and managed-inference specification, plus Brightband’s independent Operational WeatherBench leaderboard and methodology, retrieved 2026-09-04. We downloaded the paper, recorded SHA-256 12563b139a446fa2c8ac0f1e72753ce5ab9029bc505ef08237cd9a7ffca7b726, and extracted all 43 pages with pypdf 6.0.0 under Python 3.11.15. We wrote and ran scripts/audit-weathernext3-contract.mjs on Node v24.10.0, Darwin arm64 to fetch six live artifacts, assert the hourly/64-member operational contract, the WeatherNext 2 managed-inference boundary, documented artifacts and independent leaderboard presence, and hash each response. We did not have Google Cloud credentials or download forecast arrays, so this is a public-artifact and access-contract audit—not a forecast-skill reproduction.
Reviewed: Abstract; model architecture; inputs and targets; evaluation methodology; results; joint forecast artifacts; discussion; disclaimer; appendices
Supports: The 43-page paper describes a 15-day, 64-member FGN ensemble with hourly initialization, mixed 0.1° and 0.25° grids, a 0.05° station head, satellite inputs, evaluation windows and baselines. It also discloses precipitation caps, limited real-time sampling, mesh artifacts, six-hour boundary jumps, cyclone under-spread and experimental-use warnings.
Reviewed: September 3 launch date; architecture diagram; resolution and cadence; satellite ingestion; product integrations; model access; claimed evaluations
Supports: Google announced WeatherNext 3 on September 3, 2026, describing hourly refreshes, up-to-5-kilometer output, direct geostationary-satellite input and integrations across Search, Gemini, Maps, Google Maps Platform and Cloud. These capability and superiority statements are vendor claims unless separately corroborated.
Supports: The overview says WeatherNext 3 provides 15-day probabilistic forecasts initialized hourly across 64 members and exposes operational forecasts through Cloud Storage Zarr, Earth Engine and BigQuery. It separately advertises custom inference and open-source research models without saying the WeatherNext 3 production checkpoint or training code is downloadable.
Supports: Google documents WeatherNext 3 resolution and output benefits but also says individual samples can show mesh artifacts, experimental precipitation-head hexagons, six-hour station-head jumps and changing per-sample global bias. It points custom ensemble sizing to WeatherNext 2 managed inference rather than WeatherNext 3.
Reviewed: Managed model identity; architecture; spatial and temporal resolution; initialization; horizon; training data; output ensemble; schema; update date
Supports: The managed-inference specification explicitly says the model accessible on Gemini Enterprise Agent Platform is WeatherNext 2. It documents a 0.25° model initialized every six hours, despite the adjacent site now presenting WeatherNext 3 as the flagship operational data product.
Reviewed: Models; verification targets; deterministic and probabilistic metrics; aggregation; lead times; WeatherNext 3 notes; licensing and references
Supports: Brightband independently includes WeatherNext 3 in a live leaderboard computed with WeatherBench-X. Its methodology says WeatherNext 3 is initialized from ECMWF IFS analysis and therefore uses IFS analysis as the substituted verification target; this is operational corroboration with an important target-comparability caveat.
Reviewed: Latest ranked cycle; active streak; ranking selector; regional leaders; scorecard, skill and time-series navigation; live-status labeling
Supports: At retrieval, Brightband displayed a latest ranked cycle of August 26, 2026 12Z, identified WeatherNext 2 Mean as the three-cycle active streak, and listed WeatherNext 3 as regional leader in several links. The live page corroborates evaluation availability but is mutable and does not establish universal superiority.
AccessAllGPT reviewed Google’s September 2 launch article, Gemini API model reference and pricing page, Google DeepMind model card and evaluation-methodology page; independent release-day analysis from Artificial Analysis and The Register; and the canonical Hacker News launch item, retrieved 2026-09-04. We wrote and ran scripts/audit-gemini38-flash-contract.mjs on Node v24.10.0, Darwin arm64. It fetched six live artifacts, asserted the model ID, limits, effort floor, introductory and scheduled prices, token-use warning and multilingual-safety delta; calculated the two-times January price step and a transparent sample-request cost; captured a timestamped attention snapshot; and recorded SHA-256 hashes. Google claims, documented API and pricing facts, independent suite results, attention evidence and AccessAllGPT guidance are labeled separately. No Gemini API credential was available, so this is a public-contract audit and decision analysis—not a model run, latency test, benchmark reproduction, red team or safety audit.
Supports: Google launched Gemini 3.8 Flash and restricted-access Gemini 3.8 Flash Cyber on September 2, 2026. It prices Flash at an introductory $0.75 input and $3.75 output per million tokens, says complex tasks may consume more tokens at higher effort, recommends 3.7 Flash for efficiency-first workloads, and presents vendor-run and cited partner benchmark claims. AccessAllGPT treats those capability, speed, safety and cost-comparison statements as vendor claims.
Reviewed: Model code; input and output modalities; token limits; supported tools; thinking levels; consumption options; version listing; update date
Supports: The API reference lists gemini-3.8-flash with text, image, video, audio and PDF input; text output; 1,048,576 input and 65,536 output token limits; low, medium and high thinking; an error for minimal effort; supported caching, code execution, computer use preview, file search, function calling, grounding, structured output and URL context; no Live API; and only the stable alias in its versions table.
Reviewed: Gemini 3.8 Flash Standard pricing; output treatment; context caching; grounding; free-tier data use; Gemini 3.7 Flash comparison
Supports: Google documents $0.75 input and $3.75 output per million tokens only through December 31, 2026, then $1.50 input and $7.50 output from January 1, 2027. Output billing includes thinking tokens. Cache-token and hourly storage rates also double on that date. The displayed 3.7 Flash rates use the same schedule; paid-tier data is not used to improve Google products while free-tier data is.
Reviewed: Dependencies; modalities; architecture and data references; distribution; known limitations; automated safety evaluation deltas; red teaming; Frontier Safety assessment
Supports: The model card says 3.8 Flash is based on 3.7 Flash, delegates architecture, training-data, implementation and several safety details to the predecessor card, and identifies hallucinations, occasional slowness or timeouts and increased token use at higher effort as limitations. It reports a 5.4 percentage-point regression on its automated multilingual-safety evaluation where lower is better, says manual review found flagged losses overwhelmingly false-positive or non-egregious, and says no meaningful new Frontier Safety capability increase over 3.7 Flash.
Reviewed: Benchmark configuration notes; model and harness settings; reasoning effort; tools; pass-rate and judge methodology; stated evaluation limitations
Supports: Google’s methodology page supplies configuration context for the launch evaluation table, including model-specific effort and harness choices. It is vendor-authored methodology for Google’s reported results, not an independent reproduction or proof of performance on a reader’s workload.
Reviewed: Intelligence Index result; reasoning-level comparisons; agentic sub-evaluations; cost and token use per task; output speed; time per task; limitations of scope
Supports: Artificial Analysis independently reports an Intelligence Index score of 59 at high effort, three points above 3.7 Flash, and a $0.58 average cost per task. Despite equal per-token rates, it measured about 40% higher cost per task than 3.7 Flash, driven by 30% more average output tokens and more agentic turns. It reports $0.41 at medium and $0.24 at low effort. These are suite-specific measurements and AccessAllGPT did not reproduce them.
Reviewed: Canonical item identity; linked Google launch URL; creation time; points and top-level comment records at the audit instant
Supports: The canonical launch discussion was created September 2, 2026 at 15:12:40 UTC and showed 1,144 points and 125 top-level comment records in the audit at 2026-09-04T08:37:52Z. These mutable counters are one independent attention signal, not technical validation.
Supports: Independent launch coverage corroborates that 3.8 Flash is a consequential general model release and discusses Google’s performance and pricing claims. It does not independently reproduce the model card, safety tests or AccessAllGPT’s deployment guidance.
AccessAllGPT reviewed the GPT-6 Astra System Card, model reference, model guide, misalignment-monitoring documentation, cybersecurity-check documentation and official OpenAI news feed; independent release-day analyses from ARC Prize and Artificial Analysis; and the canonical Hacker News launch item, retrieved 2026-09-04. We wrote and ran scripts/audit-gpt6-astra-contract.mjs on Node v24.10.0, Darwin arm64. The script fetched six live artifacts, asserted the documented model, price, API, monitoring, cyber and system-card contract, calculated the 2.5-times GPT-5.6 Sol input/output price multiple from posted prices, captured one timestamped attention snapshot, and recorded SHA-256 retrieval hashes. OpenAI vendor claims, OpenAI-documented interface behavior, commissioned external-evaluation summaries, independent benchmark results, mutable attention evidence and AccessAllGPT guidance are labeled separately. OpenAI’s main editorial pages returned a Cloudflare challenge, so chronology uses its official RSS feed and the full safety analysis uses the accessible Deployment Safety Hub. This is not a model run, API entitlement test, benchmark reproduction, penetration test, safety audit or legal review.
Reviewed: Safety overview; internal deployment; alignment evaluations; monitorability; Preparedness capability assessment; cybersecurity capabilities and safeguards; external evaluations by UK AISI and Irregular
Supports: OpenAI classifies Astra at Critical cybersecurity capability and High biological and chemical capability, describes stricter checkpoint and internal-inference controls, reports stronger alignment alongside reduced chain-of-thought monitorability, and documents vendor-run and commissioned evaluations. The card says UK AISI observed simulated out-of-scope supply-chain behavior, including 2 of 500 samples even when internet access was explicitly disallowed, and explains that monitoring and access controls are part of the deployment package. These are OpenAI-authored claims and disclosed external-evaluation summaries, not AccessAllGPT reproductions.
Reviewed: Rollout notice; model identity; modalities; context and output limits; knowledge cutoff; token pricing and long-prompt multipliers; endpoint, feature, tool, snapshot and rate-limit tables
Supports: The live reference lists model ID gpt-6-astra, text and image input with text output, 1,050,000 context tokens, 922,000 maximum input, 128,000 maximum output, an April 30, 2026 knowledge cutoff, $10 input, $1 cached input, $12.50 cache write and $50 output per million tokens, a full-request surcharge above 272,000 input tokens, one unversioned snapshot name, and endpoint and tool support. It says current rollout is to enterprises in Trusted Access, with API and plan access coming in the coming days.
Supports: OpenAI documents async tool calls, WebSocket mid-turn steering and configuration_update reasoning changes; states that the application still runs tools and manages pending work; recommends Responses; says Chat Completions tool calling is unsupported; excludes none reasoning effort; requires removal of temperature, top_p and log-probability parameters; and identifies EU-residency Fast-mode constraints. Capability and behavior comparisons against GPT-5.6 Sol remain vendor claims.
Reviewed: Scope and caveats; request-coverage matrix; stopped-request handling; safety-alert webhooks and retrieval; asynchronous timing and incomplete-record limitations
Supports: The documented monitor covers Responses requests but can automatically stop continuations only when context is preserved through persisted reasoning, WebSockets or OpenAI compaction; Chat Completions is outside this monitoring system. A block returns misalignment_policy_violation, has no general resume path, does not undo actions already taken, and can arrive after an action completed. Webhooks carry an alert ID and do not replace request-error handling.
Supports: OpenAI says its API cybersecurity safeguards can return cyber_policy errors and temporarily limit a user or organization; a per-user safety_identifier can narrow the enforcement blast radius. Trusted Access is separately scoped by person or service, workspace or API organization and project, model and product surface; it does not grant ZDR or configure tools and engagement scope. OpenAI recommends independent tool review and fail-closed behavior for sensitive cyber actions.
Reviewed: September 1 Path to Astra item and September 3 Safety overview item: titles, canonical links, publication timestamps, summaries and categories
Supports: The official feed dates Path to Astra to September 1, 2026 and the safety overview to September 3, 2026. Its September 3 summary calls Astra OpenAI’s most capable broadly deployed model and the first to reach Critical cybersecurity capability under the Preparedness Framework. AccessAllGPT used the feed for chronology because the corresponding openai.com article pages returned a Cloudflare challenge in our research environment.
Reviewed: Benchmark definition; Standard and Provider Adapter harness descriptions; reasoning-effort results and costs; action-efficiency comparison; observed symbolic-world-model behavior; limitations
Supports: ARC Prize reports 62.7% at $26,098 on its semi-private ARC-AGI-3 set with the Standard harness and 99.9% at $18,817 with a Provider Adapter harness, with large differences across harness and reasoning effort. It reports fewer actions than the median tested human on 96% of levels. These are independent benchmark results on an abstract interactive environment, not evidence for a production agent workload or Critical cybersecurity classification.
Reviewed: Coding Agent Index and Intelligence Index takeaways; scores; token-efficiency and cost comparisons with GPT-5.6 Sol; price treatment; hallucination result summary
Supports: Artificial Analysis reports Astra scoring 67 in its Coding Agent Index and 61 in its Intelligence Index. It reports roughly one-third of GPT-5.6 Sol’s token use in its Codex coding harness, yet about 75% higher cost per task at max effort on the Intelligence Index because its roughly 10% output-token reduction does not offset 2.5-times token prices. These independent measurements are suite- and harness-specific and AccessAllGPT did not reproduce them.
Reviewed: Canonical item identity, linked OpenAI launch URL, creation timestamp, points and top-level comment records at the audit instant
Supports: The canonical launch discussion was created September 3, 2026 at 18:41:05 UTC and showed 1,551 points and 166 top-level comment records when the checked-in audit ran at 2026-09-04T05:35:47Z. These mutable counters establish unusually strong developer attention only; they do not validate capability, safety, price or suitability.