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 AWS-controlled public surfaces on October 20, 2026: the October 9 availability notice, AWS’s October 19 technical walkthrough, the live Pegasus v1.5 Bedrock model card and the current Bedrock pricing page. We reconciled the model, availability and explainer dates; model IDs; inference paths; input and output limits; supported modes; regional routing; billing dimensions and documented caveats. These are vendor documents, not independent performance evidence. AccessAllGPT did not obtain AWS or TwelveLabs credentials, submit video, compare Pegasus 1.2, reproduce timestamps or JSON, test reference images, measure latency or accuracy, inspect data handling, or receive a bill.
Reviewed: October 19, 2026 publication date and October 9 availability chronology; new capabilities; runtime model ID and APIs; analysis modes; input methods and limits; request and response examples; stop reasons; segmentation definitions; multimodal prompting; pricing and cleanup notes
Supports: AWS documents Pegasus 1.5 on Bedrock as an on-demand video-language model for general analysis and time-based metadata. The post identifies two-hour video input, JSON segment output, up to 30 reference images, S3 or base64 input, InvokeModel and streaming invocation, and model-specific request fields.
Reviewed: October 9, 2026 availability announcement; video-to-text and time-coded metadata description; whole-video analysis; user-defined event types; audiovisual and on-screen-text processing; no-pre-indexing statement; US and Global cross-Region access
Supports: AWS announced Bedrock availability on October 9, 2026 and described Pegasus 1.5 as reading an entire video to return timestamped, structured metadata through US and Global cross-Region inference profiles.
Reviewed: Model details and October 8 launch date; active lifecycle; context and output limits; endpoint guidance; unsupported Bedrock features; Marketplace billing; base, US and Global model identifiers; cross-Region-only restriction; source and destination Regions; S3 co-location requirement; quota caveat
Supports: The live model card lists a 261,120-token context, 98,304 maximum output tokens, bedrock-runtime access, US and Global inference IDs, no in-Region on-demand support, Marketplace billing, and an active lifecycle. It says the request S3 bucket must match the source Region.
Reviewed: Model Pricing introduction; TwelveLabs on-demand section; Global and Geo cross-Region tabs; Pegasus 1.5 billing dimensions; video-input seconds; output-token units; applicability to US and Global CRIS
Supports: The current pricing surface bills Pegasus 1.5 using video-input seconds and output tokens and says Pegasus pricing applies to US and Global cross-Region inference. Numeric values are client-rendered pricing tokens and did not materialize in the reviewed public response, so this article does not quote an unverified rate.
AccessAllGPT reviewed three AWS-controlled public surfaces on October 20, 2026: AWS’s October 19 announcement, the live Amazon Bedrock structured-outputs user guide and the current Bedrock pricing page. We compared availability language, API surfaces, schema rules, tool strictness, compilation and cache behavior, exception paths, compatibility caveats and price disclosure. Availability and behavior described by AWS remain vendor documentation. AccessAllGPT did not use AWS credentials, invoke a Claude model, compile a schema, measure latency or token savings, reproduce schema compliance, test tool calls, validate every model or Region, or inspect an AWS bill.
Reviewed: October 19, 2026 announcement date; runtime and Mantle availability; JSON Schema output and strict tool use; constrained decoding; grammar compilation and caching; API examples; stop reasons; supported and unenforced schema constraints; conclusion and general-availability statement
Supports: AWS announced general availability of structured outputs for Anthropic Claude models where available on bedrock-runtime and bedrock-mantle. It documents JSON Schema responses, strict tool inputs, a Draft 2020-12 subset, a 24-hour grammar cache, first-use compilation latency, and refusal or max-token exceptions.
Reviewed: Benefits; JSON Schema and strict-tool mechanisms; request workflow; compilation and cache behavior; supported and unsupported JSON Schema features; supported APIs; citations incompatibility; geographic-profile caveat; model-support lookup
Supports: The live Bedrock guide confirms support across Converse, InvokeModel, streaming, cross-Region inference, batch inference and the Anthropic Messages API on Mantle. It warns that citations are incompatible for Anthropic models and that some geographic profiles do not support every model.
Reviewed: Model Pricing overview; provider and model dependency; Standard, Flex, Priority and Reserved tiers; batch-inference discount statement; Anthropic provider selector; separate capability tabs
Supports: AWS says Bedrock model pricing depends on modality, provider and model, and identifies multiple service tiers. The reviewed pricing page does not state a separate structured-output feature fee; account, model, Region, tier, token use and any surrounding Bedrock services still determine cost.
AccessAllGPT reviewed four NVIDIA-controlled surfaces on October 19, 2026: the October 15 launch post, the public Super VL NVFP4 and Bolt 74B-A9B 24GB Hugging Face repositories, and NVIDIA’s technical article. We checked exact model identifiers, announced and scheduled availability, public repository access, parameter counts, active parameters, precision, context limits, supported modalities, license labels, deployment recipes, runtime requirements, displayed benchmarks and throughput methodology notes. We also queried the public Hugging Face model API to confirm that both reviewed repositories were ungated and to record repository revisions 59b8721646c38111a242da18218859fb2b454322 for Super VL and c6cc2e8ffa353403213d6b9b18aaf8eb684d705a for Bolt at review time. AccessAllGPT did not download either checkpoint, accept separate service terms, run a model, inspect every weight shard, reproduce a benchmark, validate a hardware recipe, measure memory or power, or test NVIDIA NIM, vLLM, SGLang, TensorRT-LLM, llama.cpp or an agent harness.
Reviewed: October 15 announcement; model names and availability; parameter and memory positioning; Super VL and Bolt descriptions; benchmark and throughput claims; customization examples; deployment links; Bolt E4B timing
Supports: NVIDIA announced Nemotron 3.5 Super VL and Bolt 74B-A9B as available on October 15, 2026, while Bolt E4B was scheduled for October 23. The page positions the family for multimodal and local agents and makes the 5x throughput, benchmark-rank and deployment claims cited here; those are vendor claims.
Reviewed: Public repository status; model summary; OpenMDW 1.1 license; inputs and outputs; architecture; context; hardware and runtime matrix; benchmark table and methodology notes; release date; training stages; quick-start limitations
Supports: The public, ungated NVFP4 repository identifies a 120B-total, 12B-active vision-language MoE with text, image and video input, text output, up to 1M context and OpenMDW 1.1 terms. NVIDIA lists deployment recipes for DGX Spark, H200, two H100s and GB200, and reports benchmark results that AccessAllGPT did not reproduce.
Reviewed: Public repository status; description; 74B-total and 9B-active architecture; IQ2 precision; 24GB packed size; 256K context; multimodal inputs; supported NVIDIA architectures; reasoning and speculative-decoding controls; license
Supports: The public, ungated model card describes Bolt 74B-A9B 24GB as a 74B-total, 9B-active multimodal model with native IQ2 quantization, up to 256K context and OpenMDW 1.1 terms. Its 24GB packed-model claim does not establish total application memory, speed or compatibility on every 24GB system.
Reviewed: October 15 publication date; architecture and use cases; Artificial Analysis throughput figure; multimodal agent positioning; post-training workflow; deployment guidance; limitations of stated comparisons
Supports: NVIDIA describes Super VL as a customizable multimodal agent model and reports more than 600 output tokens per second on a Baseten endpoint at a 100K-token input in its displayed Artificial Analysis comparison. This is a vendor-selected serving result, not a universal local or production throughput guarantee.
AccessAllGPT reviewed four NVIDIA-controlled surfaces on October 19, 2026: the October 16 newsroom release, October 17 launch-event follow-up, current RTX Spark product page and NVIDIA Build resource page. We checked the exact launch dates, participating PC makers, named laptop lineup, availability language, competing starting-price statements, processor and memory ceilings, AI-performance precision, model-size and context claim, Windows and CUDA positioning, local-agent software, developer resources, announced application support and forward-looking caveat. Availability and page contents are directly observable; specifications, battery life, performance, model fit, compatibility and privacy benefits remain vendor claims. AccessAllGPT did not buy an RTX Spark PC, inspect retail inventory in every region, obtain a configuration matrix, run a model or agent, measure memory use, tokens per second, battery life, thermals, noise, application compatibility or total cost, or receive private access.
Reviewed: October 16 release date; availability and named systems; starting price; Blackwell RTX GPU and Grace CPU; memory and FP4 claims; Windows-on-Arm software support; local-agent stack; Nemotron 3.5 Bolt support; creative and gaming claims; forward-looking disclaimer
Supports: NVIDIA announced RTX Spark laptop availability on October 16, 2026 from six PC makers, starting at $2,599 with regional variation. It describes up to 128GB unified memory, up to one petaflop of FP4 AI performance, native CUDA, Windows 11, local-agent support and a named launch lineup. Performance, battery, compatibility and workload statements are NVIDIA claims.
Reviewed: Platform overview; developer and AI-agent sections; model-size and context claim; laptop and desktop distinctions; N1X specification table; named systems; regional selector; shop links
Supports: NVIDIA’s current product page lists up to a 6,144-core Blackwell RTX GPU, 20-core Grace CPU, one petaflop of FP4 performance and 128GB unified memory. It claims models up to 120B parameters with 1M context can run on qualifying configurations, but does not publish a model-specific speed, quality result or complete configuration matrix on the reviewed page.
Reviewed: October 17 publication date; stated retail availability; participating manufacturers; starting-price statement; superchip description; retail launch events; developer resources link
Supports: NVIDIA’s October 17 follow-up says RTX Spark laptops are available and repeats the Blackwell RTX, Grace CPU, 128GB and one-petaflop positioning. It says systems start at $2,399, which conflicts with the $2,599 starting price in NVIDIA’s October 16 newsroom release; buyers must verify an exact regional SKU and checkout price.
Reviewed: Agent setup entry point; essential playbooks; local-LLM playbook; AI development workspace; PAIR with llama.cpp; developer resources; visible publication dates
Supports: NVIDIA exposes public setup and playbook pages for agent, local-LLM and development workflows. Their presence establishes documentation availability, not that every model, agent or dependency works on every RTX Spark configuration without additional setup or limitations.
AccessAllGPT reviewed three Anthropic-controlled sources on October 19, 2026: the October 18 Public Benefit Fund announcement, Anthropic’s current company and governance page, and its September 2023 Long-Term Benefit Trust explanation as subsequently updated. We checked the exact equity terms, approval condition, company-valuation trigger, legal form, stated spending restriction, examples of possible uses, prior pledge context, current disclosed governance structure and the distinction between the new Fund and the existing Trust. We treated every numerical, legal, participation and mission statement as an Anthropic claim. AccessAllGPT did not receive corporate records, capitalization data, stockholder materials, the Fund operating agreement, valuation baseline, board resolutions, tax analysis, beneficiary criteria, distribution records, a public-benefit report or a private briefing; did not value Anthropic or calculate the present or future value of any pledged shares; and did not verify that stockholder approval has occurred.
Reviewed: October 18 publication and modification metadata; initial and growth-linked equity terms; stockholder-approval condition; stated uses; LLC form; political-spending restriction; earlier founder and employee pledges; promised governance disclosure
Supports: Anthropic announced a new wholly owned Anthropic Public Benefit Fund. Subject to stockholder approval, it says 5% of company shares will be committed initially and the board may add 2% for every 50% increase in company value above an undisclosed baseline. Anthropic has not yet published the Fund’s detailed governance or valuation milestones.
Reviewed: Company mission; Public Benefit Corporation statement; stated public-benefit purpose; current board and Long-Term Benefit Trust overview
Supports: Anthropic describes itself as an AI safety and research company and a Public Benefit Corporation whose stated purpose is the responsible development and maintenance of advanced AI for the long-term benefit of humanity. It says directors are elected by stockholders and the Long-Term Benefit Trust.
Reviewed: Original September 2023 publication; Public Benefit Corporation explanation; Class T stock and board-selection authority; Trust purpose and phase-in; experimental-status caveat; later trustee-composition update
Supports: Anthropic says the Long-Term Benefit Trust is a separate governance mechanism holding Class T stock with authority to elect and remove a growing portion of the board. This distinguishes the existing board-governance Trust from the newly announced equity-holding Public Benefit Fund.
AccessAllGPT reviewed the October 15 Hugging Face launch guide and the live Meta-owned BF16 and NVFP4 repositories on October 16, 2026, then materially updated this briefing on October 19 after reviewing AWS’s October 15 SageMaker JumpStart launch guide. We pinned the repository API records to commits 0fb02478b02565b2ce7fc57e6051c91172774ac9 and 4239e4db19376904f2e82c167e78f70abb5455fe, and checked model and JumpStart IDs, dates, access state, parameter metadata, modalities, context, precision, serving recipes, instance mappings, launch Regions, endpoint request shape, security controls, deployment timing, billing caveats and displayed license terms. Repository state and the documented AWS serving contract are directly observable public facts. Architecture, hardware fit, quantization quality, deployment timing and capability statements remain vendor or launch-partner claims. AccessAllGPT did not submit gate information, accept the EULA, download either checkpoint, open an AWS account, request service quota, provision the documented hardware, invoke a SageMaker or other hosted endpoint, run the model, obtain a price quote or reproduce any result.
Reviewed: Publication date; model identity; architecture; modalities; context; Transformers, llama.cpp, SGLang and vLLM support; deployment recipes; fine-tuning example; demos
Supports: Hugging Face published its launch and implementation guide on October 15, 2026. It identifies the model as a Meta release with about 816B total and 42B active parameters, text/image/audio/video input, text output, a 1,048,576-token maximum context, BF16 and NVFP4 checkpoints, and concrete multi-GPU serving recipes. Performance and accuracy statements in the post are Hugging Face or Meta-reported, not independent AccessAllGPT results.
Reviewed: Repository owner; model identifier; current commit; public and gated state; release date; license and usage-policy text; file inventory; parameter metadata; modality and library tags
Supports: Meta’s public repository is manually gated and identifies the October 15 release as meta-models/Muse-Spark-Open-816B-A42B. The API record at commit 0fb02478b02565b2ce7fc57e6051c91172774ac9 reports 822,036,529,664 safetensors parameters and image-text-to-text Transformers support. The displayed Muse license requires attribution, a “Built with Muse Spark Open” notice, usage-policy compliance, and a separate Meta agreement for certain model-as-a-service businesses above $20 million in twelve-month revenue.
Reviewed: Repository owner; quantized model identifier; current commit; public and gated state; license; base-model relationship; file inventory; parameter metadata; deployment tags
Supports: Meta’s manually gated NVFP4 repository is live at commit 4239e4db19376904f2e82c167e78f70abb5455fe. Its API record identifies the BF16 repository as the base model, exposes 483,807,947,264 safetensors parameters in the quantized artifact, and applies the same custom Muse model license and usage policy.
Reviewed: October 15 publication date; launch Regions; JumpStart model IDs; supported variants and instances; gated-model EULA; Studio and SDK deployment; endpoint request contract; security options; deployment timing; billing and cleanup caveats
Supports: AWS says both Muse Spark Open variants are available through SageMaker JumpStart in US East (N. Virginia) and US East (Ohio). It maps NVFP4 to ml.p5e.48xlarge and BF16 to ml.p6-b300.48xlarge, documents OpenAI-compatible chat requests and estimates 15–20 minutes to deploy. AWS does not publish a Muse-specific per-token price in the launch post, and endpoints accrue instance charges while running.
AccessAllGPT reviewed three first-party AWS surfaces on October 18, 2026: the October 16 case study, the live AgentCore Evaluations developer guide and the live AgentCore pricing page. We checked the publication date, internal application scope, failure examples, nine lifecycle stages, evaluator types, gate sizes, sampling and alarm cadences, reported outcomes, figure labels, service interfaces, quotas and current public rates. We treated all operational outcomes as AWS self-reported evidence and did not generalize them beyond the described sales system. We did not obtain an AWS account, production traces, datasets, prompts, evaluator configurations, model identifiers or source code; run AgentCore; recreate the 15-case sentinel or 200-question gate; calibrate an LLM judge; or reproduce quality, latency, cost or incident-detection results.
Reviewed: October 16 publication metadata; internal sales-agent scope; failure taxonomy; nine lifecycle stages; evaluator stack; CI and release gates; canary and online sampling; reported outcomes; figure caveats; conclusion
Supports: AWS published a nine-stage evaluation framework used for its internal multi-agent field-sales system. The post reports a 15-case CI sentinel, a 200-question release gate, 30-minute canary windows, 10% online sampling, six domain failure classes and vendor-reported improvements in pass rate, hallucination frequency and detection time. Several figure values are explicitly illustrative.
Reviewed: Service overview; OpenTelemetry and OpenInference integration; LLM-as-a-Judge method; evaluator ARN and IAM model; regional configuration and token quotas; online, on-demand, batch and dataset evaluation topics
Supports: AWS documents AgentCore Evaluations as a live service for automated pre- and post-deployment assessment. It supports built-in and private custom evaluators, popular instrumented agent frameworks, up to 1,000 evaluation configurations per Region by default and up to 1 million input and output tokens per minute per account in large Regions.
Reviewed: Evaluations consumption model; built-in, custom and batch rates; CloudWatch charges; sampling controls; optimization preview and A/B test charges
Supports: AWS lists built-in evaluations at $0.0024 per 1,000 input tokens and $0.012 per 1,000 output tokens, custom evaluations at $1.50 per 1,000 evaluations plus model usage, and batch evaluation at $0.0018 per 1,000 input tokens and $0.009 per 1,000 output tokens. CloudWatch and other AgentCore resources are billed separately.
AccessAllGPT reviewed four Google-controlled surfaces on October 18, 2026: the October 16 feature announcement, Google’s September 15 rollout detail, its August 28 compute-limit notice and the live unauthenticated Gemini Notebook entry point. We compared exact launch language, feature composition, duration and language claims, rollout timing, usage-limit mechanics, plan references and facts omitted from the public material. We treated the October post’s “recently launched” wording as present availability for the three named study features, but did not extend that conclusion to separate mobile capabilities that the September post described as upcoming. AccessAllGPT did not sign in, upload coursework, generate a video or learning overview, take a quiz, exhaust a quota, inspect prompts or models, compare plans, or measure accuracy, grounding, accessibility, latency, cost or learning outcomes.
Reviewed: Publication date, launch wording, Short Video Overviews, Interactive Learning Overviews, expanded quiz formats and product call to action
Supports: Google published the update on October 16, 2026 and says three recently launched study features are available: approximately 60-second Short Video Overviews, Interactive Learning Overviews under Reports, and short-answer, multi-select and fill-in-the-blank quiz formats with follow-up feedback through notebook chat.
Reviewed: September 15 rollout language, source grounding, mobile conversation and recording status, learning-overview composition, quiz editing, Short Video languages, duration and student-plan terms
Supports: Google’s earlier rollout notice describes Interactive Learning Overviews as combining summaries with outputs such as infographics, quizzes and flashcards; says quizzes can be edited; and describes Short Video Overviews as roughly 60 seconds and available in more than 80 languages. It separately labels some mobile capabilities as coming soon or starting the following week, so those are not treated as part of the October 16 availability claim.
Reviewed: Compute-specific usage policy, five-hour refresh, usage factors, deferred generation behavior, notification option and consumer rollout date
Supports: Google says consumer usage limits refresh every five hours and can vary with prompt complexity, chat length, source count and feature choice. If an output such as a Video Overview exceeds the available limit, users can defer it for automatic generation later and request a completion notification. Google does not publish a fixed per-output compute charge in this notice.
Reviewed: Public product entry point, sign-in requirement, product positioning and unauthenticated surface
Supports: The public product route resolves to Gemini Notebook and presents a sign-in action. The unauthenticated surface establishes that the service is live, but it does not expose account-specific feature eligibility, remaining compute, generated output quality, model identifiers or data controls.
AccessAllGPT reviewed four first-party surfaces on October 18, 2026: OpenAI’s October 16 canonical customer-story URL, the corresponding item in OpenAI’s News RSS feed, the live Stack Overflow for Agents public product, and the product’s unauthenticated API documentation response. We checked the publication timestamp, exact growth wording, product availability, content taxonomy, plugin call to action, signup and login surfaces, displayed post count and API authentication boundary. The full OpenAI article and ChatGPT plugin listing returned Cloudflare challenges, while /skill.md became rate-limited after the first product retrieval. We therefore report only claims preserved in OpenAI’s first-party feed and facts directly observable on the product or API response. AccessAllGPT did not receive an account, plugin access, API key, Codex logs, analytics export, cohort table, experiment design, revenue data or private briefing; did not install the plugin, create an agent, post content or reproduce the reported signup or agent-creation changes.
Reviewed: Canonical title, publication date, category and syndication summary; Cloudflare access condition on the full article
Supports: OpenAI published the customer story on October 16, 2026. Its title says signups tripled; its summary says two engineers shipped Stack Overflow for Agents in a few weeks with Codex and that the ChatGPT plugin helped drive up to tenfold growth in agent creation. The full article returned a Cloudflare challenge during review, so AccessAllGPT does not rely on inaccessible body details.
Reviewed: Feed build timestamp; Stack Overflow for Agents item title, canonical URL, category, description and publication timestamp
Supports: The first-party feed independently exposes the October 16 item as a Startup story and supplies the same title and summary. It establishes OpenAI’s published wording and date, not the measurement design, denominator, attribution window or independent validation of the reported growth.
Reviewed: Public home feed; content types; signup and login surfaces; plugin call to action; displayed post count and current feed examples
Supports: The live product exposes a public feed for agent-oriented content, including playbooks, blueprints and today-I-learned posts, plus signup, login and a Get the plugin call to action. At review time the feed displayed 10,115 posts; that mutable count is a point-in-time interface observation, not a measure of active users or content quality.
Reviewed: Unauthenticated API response; authentication requirement; bearer header format; documentation location and missing-key remediation
Supports: An unauthenticated request returned HTTP 401 and documented bearer-key authentication through the Authorization header, with operating guidance at /skill.md. This confirms an agent-facing API boundary, but AccessAllGPT did not create an account, obtain a key, inspect authenticated documentation or exercise read or write operations.
AccessAllGPT reviewed four first-party or launch-partner surfaces on October 17, 2026: Reflection’s October 5 preview, Hugging Face’s October 16 release guide, the public BF16 repository and API record pinned to commit 78f8f654d40a40919b8e9954b40dd879ecb9a692, and AWS’s October 16 deployment guide. We checked announcement versus artifact dates, repository access, model identity, parameter metadata, architecture, modalities, context, checkpoint formats, license, documented runtimes, hardware guidance, hosted availability, Regions, pricing disclosures, benchmark provenance and missing evidence. Repository and service availability are directly observable or provider-documented. Capability, training, safety, efficiency and quantization-quality statements remain vendor-reported. AccessAllGPT did not download the approximately one-terabyte BF16 checkpoint, provision GPUs, deploy JumpStart or HyperPod, invoke a hosted provider, run Beam, reproduce benchmarks or receive private access.
Reviewed: October 16 publication date; model architecture; context; checkpoint formats; license; hardware guidance; Transformers, vLLM and SGLang support; hosted providers; post-training recipes; evaluation disclosures
Supports: Hugging Face published the implementation and release guide on October 16, 2026. It identifies Beam as a 501B-total, 23B-active text model with a one-million-token context, Apache 2.0 weights in BF16, FP8 and NVFP4, and day-one support in Transformers, vLLM and SGLang. Hardware sizes, performance statements and retained quantization quality are Reflection or Hugging Face claims, not AccessAllGPT measurements.
Reviewed: Repository owner; public access state; pinned revision; parameter metadata; model overview; modalities; context; knowledge cutoff; benchmark table; training-data summary; safety and alignment; usage; Apache 2.0 license
Supports: The public, ungated repository was reviewed at commit 78f8f654d40a40919b8e9954b40dd879ecb9a692. Its API record reports 501,267,612,352 safetensors parameters, and its model card lists 501.3B total and 23.4B active parameters, text input and output, a one-million-token context, a June 30, 2026 knowledge cutoff, predominantly English development, vendor-run evaluations, and Apache 2.0 terms. The card still labels the technical report as coming.
Reviewed: October 16 publication metadata; launch availability; checkpoint variants; managed and Kubernetes deployment paths; supported GPU families; Regions; caching and routing; measurement guidance; cleanup
Supports: AWS published its Beam deployment guide on October 16, 2026 and says Beam is available through SageMaker JumpStart in US East (N. Virginia) and US East (Ohio). The guide covers JumpStart real-time endpoints and HyperPod Inference with JumpStart, SGLang or vLLM, and maps FP8 to H100/H200 instance families and NVFP4 to B200. It does not publish a Beam-specific per-token price or a workload benchmark.
Reviewed: October 5 announcement; staged availability statement; architecture; pretraining and reinforcement-learning claims; capability comparisons; evaluation methodology notes; safety and alignment; release plan
Supports: Reflection announced Beam on October 5, 2026 while saying final red-teaming was underway and weights and developer artifacts would arrive later in the month. This establishes that October 16 is the artifact release, not the first preview. Training scale, benchmark comparisons, safety results and inference-efficiency statements remain vendor-reported.