This index exposes the material behind every decision guide: what we reviewed, when we retrieved it, the claim it supports and where the evidence stops.
158decision guides
707source records
699scoped claim notes
How to read this index
A citation is not a transferable result.
Reviewed identifies the part of the source used. Supports states the bounded proposition we draw from it. Retrieved records when time-sensitive material was checked.
Vendor documentation establishes vendor claims and product behavior as documented, not independent performance. Papers establish results only for their stated methods and tested scope. AccessAllGPT recommendations remain labeled analysis.
AccessAllGPT reviewed four 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.
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.