{"data":{"schema_version":"model_decision_evidence.v1","snapshot_date":"2026-09-25","model":{"model":"claude-fable-5-1","direct_answer":"Claude Fable 5.1 is Anthropic's most capable widely released model: closed-weight, released on 2026-09-01, with a 1M-token context window, 128K output tokens and text and image input. Anthropic's model guide starts most workloads on Opus 5.5 and reserves Fable for the highest capability; in the reviewed tests it took 40 minutes and $17 on one front-end prompt, placed last of three models on two real robot tasks, and ranked sixth on AI Coding Daily's coding leaderboard, below Claude Opus 5.5 and GPT-6 Astra.","verified_at":"2026-09-24","model_card":{"context_window":"1M tokens","max_output":"128K tokens","modalities":"Text and image input; text output","parameter_count":"Not disclosed"},"official_sources":[{"title":"Introducing Claude Fable 5.1 and Claude Mythos 5.1","publisher":"Anthropic","url":"https://www.anthropic.com/claude-fable-and-mythos-5-1","retrieved_at":"2026-09-24"},{"title":"Claude Fable 5.1 model documentation","publisher":"Anthropic","url":"https://platform.claude.com/docs/en/models/fable-5-1/overview","retrieved_at":"2026-09-24"},{"title":"Choosing a model","publisher":"Anthropic","url":"https://platform.claude.com/docs/en/about-claude/models/choosing-a-model","retrieved_at":"2026-09-24"},{"title":"System Card: Claude Fable 5.1 \u0026 Claude Mythos 5.1","publisher":"Anthropic","url":"https://www.anthropic.com/claude-fable-5-1-mythos-5-1-system-card","retrieved_at":"2026-09-24"}],"open_weights":{"status":"not_available","summary":"Anthropic publishes no Claude Fable 5.1 weights. The model is available on the Claude API, Amazon Bedrock, Google Cloud, Microsoft Foundry and Claude Platform on AWS; Claude Mythos 5.1, the same model with different safeguards, is limited to Project Glasswing participants.","repository":"","licence":"Proprietary hosted model","hardware":"Not applicable","recommended_runtime":"ChinaAPI or the Claude API","source_urls":["https://platform.claude.com/docs/en/models/fable-5-1/overview","https://platform.claude.com/docs/en/models/fable-5-1/whats-new-fable-5-1"],"verified_at":"2026-09-24","agent_recipe":""},"curated_comparisons":["gpt-6-astra","gemini-3.8-flash","step-5-preview"],"open_weight_alternatives":["deepseek-flash","glm-5.3-flash"],"reviews":[{"id":"x-notjazii-frontend-2026-09","platform":"X","evidence_type":"task_test","title":"Step 5 Preview and Claude Fable 5.1 on one front-end prompt","url":"https://x.com/notjazii/status/2101653125623152714","published_at":"2026-09-20","retrieved_at":"2026-09-24","verified_at":"2026-09-24","author":{"name":"J A Z I I","handle":"@notjazii","profile_url":"https://x.com/notjazii","description":"X account that tests AI models and covers AI news"},"task":"Build a front-end page from a single prompt","method":"Same prompt at the highest available reasoning setting for both models; the author reported time and cost and repeated the Step 5 run without skills.","finding":"Claude Fable 5.1 took 40 minutes and $17 on the front-end prompt that Step 5 Preview finished in 10 minutes for about $0.50; the author left the quality comparison to readers.","limitation":"One prompt; the post leaves the quality comparison to readers rather than scoring it, and the cost gap follows each vendor's list prices.","metrics":[{"label":"Claude Fable 5.1","value":"40 min · $17"},{"label":"Step 5 Preview","value":"10 min · $0.50"}]},{"id":"cortexai-robot-tasks-2026-09","platform":"Independent benchmark","evidence_type":"independent_suite","title":"Cortex AI: GPT-6 Astra, Gemini 3.8 Flash and Fable 5.1 on two real robot tasks","url":"https://cortexrobot.ai/research/llm-real-world-robotics-evals/","published_at":"2026-09-17","retrieved_at":"2026-09-24","verified_at":"2026-09-24","author":{"name":"Cortex AI Research","handle":"@cortexairobot","profile_url":"https://cortexrobot.ai/","description":"Robotics team that builds real-world robot datasets and runs the MolmoAct2 real-world evaluations"},"task":"Load a sandwich into a microwave and close the door; attach a 5 mm tip to a pipette","method":"Each model controlled a bimanual YAM robot through an overhead and two wrist cameras with depth images, at medium reasoning effort with up to 10 minutes per task; an evaluator scored each attempt with partial credit for completed steps.","finding":"Fable 5.1 scored 6.7% on the microwave task and 20% on the pipette task, behind GPT-6 Astra (80% and 70%) and Gemini 3.8 Flash (13.3% and 30%).","limitation":"The post does not say how many attempts each model had; scores are partial-credit judgements by the lab's evaluator, and the authors attribute the gap mainly to weaker depth perception in the lower-scoring models.","metrics":[{"label":"Microwave","value":"6.7%"},{"label":"Pipette","value":"20%"}]},{"id":"aicodingdaily-llm-coding-leaderboard-2026-09","platform":"Independent benchmark","evidence_type":"independent_suite","title":"AI Coding Daily LLM Coding Leaderboard","url":"https://aicodingdaily.com/leaderboard","published_at":"2026-09-24","retrieved_at":"2026-09-24","verified_at":"2026-09-24","author":{"name":"AI Coding Daily","handle":"@AICodingDaily","profile_url":"https://www.youtube.com/@AICodingDaily","description":"Coding-model testing channel and website that runs its own leaderboard and sells premium tutorials and channel advertising"},"task":"Implement and harden production-shaped projects in several stacks, including a Laravel app, a Go service and a React/TypeScript app, from the same prompts","method":"Each model and reasoning setting is a separate row run in a coding harness, with the average cost and time per run. Code quality is graded by a judge model against a published 100-point rubric and scaled to 20 points per project; behavioural reliability runs each of four projects five times and scores the checks every attempt fails.","finding":"Claude Fable 5.1 at medium reasoning scored 53.63 ($1.53 and 3 min 6 s per run in Claude Code), sixth of 47 rows, behind Claude Opus 5.5 and GPT-6 Astra.","limitation":"One author's benchmark whose code-quality half is graded by a judge model; rows run in different harnesses, the page is updated as models are added (read on 2026-09-24, 47 rows), and the author sells premium tutorials and advertising.","metrics":[{"label":"Points (medium)","value":"53.63"},{"label":"Rank","value":"6 of 47"}]}]},"ranking":{"name":"claude-fable-5-1","display_name":"Claude Fable 5.1","vendor":"Anthropic","origin":"intl","category":"text","release_date":"2026-09-01","on_chinaapi":true,"sources":[{"leaderboard":"lmarena_text","metric":"Arena score (Elo)","value":"1501","entry":"claude-fable-5.1-max","url":"https://arena.ai/leaderboard/text","retrieved_at":"2026-10-05","interval":"±6","samples":11800},{"leaderboard":"livebench","metric":"Overall","value":"83.4","entry":"Claude Fable 5.1 Max Effort","url":"https://livebench.ai/","retrieved_at":"2026-10-04"},{"leaderboard":"aa_index","metric":"Intelligence Index","value":"53","entry":"Claude Fable 5.1 (max with fallback)","url":"https://artificialanalysis.ai/leaderboards/models","retrieved_at":"2026-09-21"},{"leaderboard":"vals_index","metric":"Accuracy (GDP-weighted finance, coding, legal and tax)","value":"65.83","entry":"Claude Fable 5.1","url":"https://www.vals.ai/benchmarks/vals_index","retrieved_at":"2026-10-04","interval":"±1.11"},{"leaderboard":"epoch_eci","metric":"General ECI","value":"165","entry":"Claude Fable 5.1","url":"https://epoch.ai/eci","retrieved_at":"2026-10-04","interval":"(162 - 169)"},{"leaderboard":"lmarena_text_chinese","metric":"Arena score (Elo)","value":"1570","entry":"claude-fable-5.1-max","url":"https://arena.ai/leaderboard/text/chinese","retrieved_at":"2026-10-05","interval":"±21","samples":874},{"leaderboard":"lmarena_text_coding","metric":"Arena score (Elo)","value":"1531","entry":"claude-fable-5.1-max","url":"https://arena.ai/leaderboard/text/coding","retrieved_at":"2026-10-05","interval":"±13","samples":2358},{"leaderboard":"livebench_coding","metric":"Coding","value":"86.4","entry":"Claude Fable 5.1 Max Effort","url":"https://livebench.ai/","retrieved_at":"2026-10-04"},{"leaderboard":"livebench_agentic_coding","metric":"Agentic Coding","value":"66.1","entry":"Claude Fable 5.1 Max Effort","url":"https://livebench.ai/","retrieved_at":"2026-10-04"},{"leaderboard":"aa_speed","metric":"Median output tokens/s","value":"65","entry":"Claude Fable 5.1 (max with fallback)","url":"https://artificialanalysis.ai/leaderboards/models","retrieved_at":"2026-09-22"},{"leaderboard":"aa_gdpval","metric":"Agentic Real-World Work Tasks, (Elo-500)/2000 (%)","value":"62","entry":"Claude Fable 5.1 (max with fallback)","url":"https://artificialanalysis.ai/leaderboards/models","retrieved_at":"2026-09-21"},{"leaderboard":"aa_tbench_v40","metric":"Agentic Coding \u0026 Terminal Use (%)","value":"52","entry":"Claude Fable 5.1 (max with fallback)","url":"https://artificialanalysis.ai/leaderboards/models","retrieved_at":"2026-09-21"},{"leaderboard":"aa_mlcr","metric":"Medical Long Context Reasoning (%)","value":"71.1","entry":"Claude Fable 5.1 (max with fallback)","url":"https://artificialanalysis.ai/evaluations/mlcr-aa","retrieved_at":"2026-09-07"}],"notes":"Released 2026-09-01 per Anthropic's announcement (Introducing Claude Fable 5.1 and Claude Mythos 5.1); on sale here since 2026-09-02. LMArena's row is the max-effort build, standing on 11,800 votes in its 2026-10-02 dataset publish; the same row on the Chinese category stands on 874 votes. Epoch's fit as read on 2026-10-04 scores it 164.82 and Claude Fable 5 162.22."},"sources":{"aa_gdpval":{"id":"aa_gdpval","name":"Artificial Analysis · GDPval-AA v2.1","operator":"Artificial Analysis","metric":"Agentic Real-World Work Tasks, (Elo-500)/2000 (%)","url":"https://artificialanalysis.ai/leaderboards/models","edition":"GDPval-AA v2.1, live board, read 2026-09-22. 220 agentic task-completion tasks with file outputs, ranked pairwise by a panel of three frontier LLM judges; since v2.1 the Elo scale is anchored to DeepSeek V4.1 Flash (max) at 1600 and fitted with a Crowd-BT model, so its figures are not comparable with the v2 figures this page quoted from its 2026-09-12 read. The column prints clamp((Elo-500)/2000). It carries 10% of the Agents category inside Intelligence Index v4.3.2, so this column is a component of the AA Intelligence Index shown as an individual source, not an independent operator. Rows Artificial Analysis marks deprecated are read with the leaderboard's Status filter set to All (CA-948). The column is hidden until the leaderboard's Intelligence group is expanded","retrieved_at":"2026-09-22","scene":"agent_work"},"aa_image_edit":{"id":"aa_image_edit","name":"Artificial Analysis Editing","operator":"Artificial Analysis","metric":"Elo","url":"https://artificialanalysis.ai/image/leaderboard/editing","edition":"live board; exact row settings retained","retrieved_at":"2026-09-13","task":"image_editing"},"aa_image_t2i":{"id":"aa_image_t2i","name":"Artificial Analysis Text To Image","operator":"Artificial Analysis","metric":"Elo","url":"https://artificialanalysis.ai/image/leaderboard/text-to-image","edition":"live board; exact row settings retained","retrieved_at":"2026-09-13","task":"text_to_image"},"aa_index":{"id":"aa_index","name":"Artificial Analysis Intelligence Index","operator":"Artificial Analysis","metric":"Intelligence Index","url":"https://artificialanalysis.ai/leaderboards/models","edition":"Intelligence Index v4.3.2, read 2026-09-22; its figures are not comparable with the v4.3 figures this page quoted from its 2026-09-12 read. v4.3.1 refreshed the pairwise judge panels of AA-Briefcase and GDPval-AA, and v4.3.2 moved GDPval-AA to v2.1 (Elo scale anchored to DeepSeek V4.1 Flash (max) at 1600, fitted with a Crowd-BT model) and refitted AA-Briefcase v1.1 with Crowd-BT, so rows moved without any model changing. v4.3 had already swapped τ³-Banking out of the index for AutomationBench-AA and Terminal-Bench v2.1 for v4.0. The leaderboard's default Status: Current filter hides rows Artificial Analysis marks deprecated; they still render with Status set to All and are re-read from that view like any other row (CA-948). A trailing * is reproduced as the board prints it: the board's data marks that score as estimated (intelligenceIndexIsEstimated), i.e. not every evaluation in the index had been run for that model","retrieved_at":"2026-09-22"},"aa_mlcr":{"id":"aa_mlcr","name":"Artificial Analysis · MLCR-AA","operator":"Artificial Analysis","metric":"Medical Long Context Reasoning (%)","url":"https://artificialanalysis.ai/evaluations/mlcr-aa","edition":"MLCR-AA, live board, read 2026-09-22. Artificial Analysis marks it a standalone evaluation that is NOT part of Intelligence Index v4.3 — unlike the GDPval-AA v2 and Terminal-Bench v4.0 columns, this one is not a component of the AA Intelligence Index shown as an individual source, only the same operator. The underlying benchmark is Wisedocs' open MLCR (Wisedocs-AI/medical-long-context-reasoning): synthetic medical records of roughly 25,000-64,000 tokens, graded across six tiers from locating a single fact to expert-level clinical synthesis. It measures multi-document reasoning over long records, not medical capability in general. The main leaderboard carries no column for it; the figures were first read off the evaluation page's chart (2026-09-07) and since 2026-09-22 from the per-model pages' payload, which carries every model the chart can show (CA-956)","retrieved_at":"2026-09-22","scene":"medical"},"aa_speed":{"id":"aa_speed","name":"Artificial Analysis · Output Speed","operator":"Artificial Analysis","metric":"Median output tokens/s","url":"https://artificialanalysis.ai/leaderboards/models","edition":"live board; measured by Artificial Analysis against the vendor's own API, not through ChinaAPI. Rows Artificial Analysis marks deprecated are read with the leaderboard's Status filter set to All (CA-948)","retrieved_at":"2026-09-22","scene":"speed"},"aa_tbench_v40":{"id":"aa_tbench_v40","name":"Artificial Analysis · Terminal-Bench v4.0","operator":"Artificial Analysis","metric":"Agentic Coding \u0026 Terminal Use (%)","url":"https://artificialanalysis.ai/leaderboards/models","edition":"Terminal-Bench 4.0 (66 tasks), run by Artificial Analysis with the mini-swe-agent harness, pass@1 averaged over 3 repeats per task; read 2026-09-22. When this column replaced the benchmark's own board on 2026-09-07 the methodology page named the harness as mini-SWE-agent v2.4.6; on 2026-09-21 it and the evaluation page name mini-swe-agent without a version, and every figure quoted before then read back unchanged. One harness for every model, so the column compares models rather than model-plus-best-scaffold — which is why it replaced the benchmark's own board, whose rows are agent × model and covered one Chinese model out of 34 (CA-788, overturning CA-734). It carries 10% of the Coding category inside Intelligence Index v4.3.2, so this column is a component of the AA Intelligence Index shown as an individual source, not an independent operator. Rows Artificial Analysis marks deprecated are read with the leaderboard's Status filter set to All (CA-948). The column is hidden until the leaderboard's Intelligence group is expanded","retrieved_at":"2026-09-22","scene":"agentic"},"aa_video_i2v_audio":{"id":"aa_video_i2v_audio","name":"Artificial Analysis Image To Video · With Audio","operator":"Artificial Analysis","metric":"Elo","url":"https://artificialanalysis.ai/video/leaderboard/image-to-video","edition":"live board; with audio pool; exact row settings retained","retrieved_at":"2026-09-13","task":"image_to_video_audio"},"aa_video_t2v_audio":{"id":"aa_video_t2v_audio","name":"Artificial Analysis Text To Video · With Audio","operator":"Artificial Analysis","metric":"Elo","url":"https://artificialanalysis.ai/video/leaderboard/text-to-video","edition":"live board; with audio pool; exact row settings retained","retrieved_at":"2026-09-13","task":"text_to_video_audio"},"epoch_eci":{"id":"epoch_eci","name":"Epoch Capabilities Index (ECI)","operator":"Epoch AI","metric":"General ECI","url":"https://epoch.ai/eci","edition":"live board","licence":"CC BY 4.0, as labelled on the page (https://creativecommons.org/licenses/by/4.0/)","retrieved_at":"2026-10-04"},"livebench":{"id":"livebench","name":"LiveBench","operator":"LiveBench (sponsored by Abacus.AI)","metric":"Overall","url":"https://livebench.ai/","edition":"LiveBench-2026-06-25","retrieved_at":"2026-10-04"},"livebench_agentic_coding":{"id":"livebench_agentic_coding","name":"LiveBench · Agentic Coding","operator":"LiveBench (sponsored by Abacus.AI)","metric":"Agentic Coding","url":"https://livebench.ai/","edition":"LiveBench-2026-06-25","retrieved_at":"2026-10-04","scene":"agentic"},"livebench_coding":{"id":"livebench_coding","name":"LiveBench · Coding","operator":"LiveBench (sponsored by Abacus.AI)","metric":"Coding","url":"https://livebench.ai/","edition":"LiveBench-2026-06-25","retrieved_at":"2026-10-04","scene":"coding"},"lmarena_i2v":{"id":"lmarena_i2v","name":"LMArena Image-to-Video Arena","operator":"LMArena (arena.ai)","metric":"Arena score (Elo)","url":"https://arena.ai/leaderboard/image-to-video","edition":"Hugging Face dataset lmarena-ai/leaderboard-dataset (image_to_video, category overall), published 2026-09-22, revision 46919c467f7f. Each rating is rounded half-up to a whole number and its interval is rating_upper minus rating, as arena.ai prints them; the dataset carries no Preliminary badge, so each citation records its row's vote count instead, and row names follow the dataset, which can trail a rename on arena.ai","licence":"CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/), as labelled on the dataset card (https://huggingface.co/datasets/lmarena-ai/leaderboard-dataset)","retrieved_at":"2026-10-05","task":"image_to_video","updated_at":"2026-09-22"},"lmarena_image_edit":{"id":"lmarena_image_edit","name":"LMArena Image Edit Arena","operator":"LMArena (arena.ai)","metric":"Arena score (Elo)","url":"https://arena.ai/leaderboard/image-edit","edition":"Hugging Face dataset lmarena-ai/leaderboard-dataset (image_edit, category overall), published 2026-09-30, revision 46919c467f7f. Each rating is rounded half-up to a whole number and its interval is rating_upper minus rating, as arena.ai prints them; the dataset carries no Preliminary badge, so each citation records its row's vote count instead, and row names follow the dataset, which can trail a rename on arena.ai","licence":"CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/), as labelled on the dataset card (https://huggingface.co/datasets/lmarena-ai/leaderboard-dataset)","retrieved_at":"2026-10-05","task":"image_editing","updated_at":"2026-09-30"},"lmarena_t2i":{"id":"lmarena_t2i","name":"LMArena Text-to-Image Arena","operator":"LMArena (arena.ai)","metric":"Arena score (Elo)","url":"https://arena.ai/leaderboard/text-to-image","edition":"Hugging Face dataset lmarena-ai/leaderboard-dataset (text_to_image, category overall), published 2026-09-24, revision 46919c467f7f. Each rating is rounded half-up to a whole number and its interval is rating_upper minus rating, as arena.ai prints them; the dataset carries no Preliminary badge, so each citation records its row's vote count instead, and row names follow the dataset, which can trail a rename on arena.ai","licence":"CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/), as labelled on the dataset card (https://huggingface.co/datasets/lmarena-ai/leaderboard-dataset)","retrieved_at":"2026-10-05","task":"text_to_image","updated_at":"2026-09-24"},"lmarena_t2v":{"id":"lmarena_t2v","name":"LMArena Text-to-Video Arena","operator":"LMArena (arena.ai)","metric":"Arena score (Elo)","url":"https://arena.ai/leaderboard/text-to-video","edition":"Hugging Face dataset lmarena-ai/leaderboard-dataset (text_to_video, category overall), published 2026-09-22, revision 46919c467f7f. Each rating is rounded half-up to a whole number and its interval is rating_upper minus rating, as arena.ai prints them; the dataset carries no Preliminary badge, so each citation records its row's vote count instead, and row names follow the dataset, which can trail a rename on arena.ai","licence":"CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/), as labelled on the dataset card (https://huggingface.co/datasets/lmarena-ai/leaderboard-dataset)","retrieved_at":"2026-10-05","task":"text_to_video","updated_at":"2026-09-22"},"lmarena_text":{"id":"lmarena_text","name":"LMArena Text Arena","operator":"LMArena (arena.ai)","metric":"Arena score (Elo)","url":"https://arena.ai/leaderboard/text","edition":"Hugging Face dataset lmarena-ai/leaderboard-dataset (text_style_control, category overall), published 2026-10-02, revision 46919c467f7f. Each rating is rounded half-up to a whole number and its interval is rating_upper minus rating, as arena.ai prints them; the dataset carries no Preliminary badge, so each citation records its row's vote count instead, and row names follow the dataset, which can trail a rename on arena.ai","licence":"CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/), as labelled on the dataset card (https://huggingface.co/datasets/lmarena-ai/leaderboard-dataset)","retrieved_at":"2026-10-05","updated_at":"2026-10-02"},"lmarena_text_chinese":{"id":"lmarena_text_chinese","name":"LMArena Text Arena · Chinese","operator":"LMArena (arena.ai)","metric":"Arena score (Elo)","url":"https://arena.ai/leaderboard/text/chinese","edition":"Hugging Face dataset lmarena-ai/leaderboard-dataset (text_style_control, category chinese, prompts in Chinese), published 2026-10-02, revision 46919c467f7f. Each rating is rounded half-up to a whole number and its interval is rating_upper minus rating, as arena.ai prints them; the dataset carries no Preliminary badge, so each citation records its row's vote count instead, and row names follow the dataset, which can trail a rename on arena.ai","licence":"CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/), as labelled on the dataset card (https://huggingface.co/datasets/lmarena-ai/leaderboard-dataset)","retrieved_at":"2026-10-05","scene":"chinese","updated_at":"2026-10-02"},"lmarena_text_coding":{"id":"lmarena_text_coding","name":"LMArena Text Arena · Coding","operator":"LMArena (arena.ai)","metric":"Arena score (Elo)","url":"https://arena.ai/leaderboard/text/coding","edition":"Hugging Face dataset lmarena-ai/leaderboard-dataset (text_style_control, category coding), published 2026-10-02, revision 46919c467f7f. Each rating is rounded half-up to a whole number and its interval is rating_upper minus rating, as arena.ai prints them; the dataset carries no Preliminary badge, so each citation records its row's vote count instead, and row names follow the dataset, which can trail a rename on arena.ai","licence":"CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/), as labelled on the dataset card (https://huggingface.co/datasets/lmarena-ai/leaderboard-dataset)","retrieved_at":"2026-10-05","scene":"coding","updated_at":"2026-10-02"},"superclue":{"id":"superclue","name":"SuperCLUE 智能指数","operator":"SuperCLUE","metric":"总分 (overall)","url":"https://www.superclueai.com/homepage","edition":"2026-07 evaluation, published 2026-08-06. The homepage reads this edition from its data file (/data/generalboard/2026年7月.xlsx, sheet 总排行榜, last modified 2026-09-11), which also carries rows evaluated after publication, each with its own evaluation publication date (the latest 2026-09-11); the page's Latest Update line refers to a different sub-board","retrieved_at":"2026-10-04"},"vals_index":{"id":"vals_index","name":"Vals Index","operator":"Vals AI","metric":"Accuracy (GDP-weighted finance, coding, legal and tax)","url":"https://www.vals.ai/benchmarks/vals_index","edition":"V2.1, page states updated 2026-10-02; read 2026-10-04. This revision covers finance, coding, legal and tax tasks and is not comparable with the V2 figures previously quoted. The entire cited cohort was re-read, including standard errors; 11 previously cited V2 rows are absent from the V2.1 board. Figures use the default scores including provider fallbacks, as labelled by Vals.","retrieved_at":"2026-10-04"},"vendor_card":{"id":"vendor_card","name":"Vendor's own evaluation card","operator":"the model's own vendor","metric":"varies; the benchmark is named in each row","url":"","edition":"self-reported","self_reported":true,"retrieved_at":"2026-09-22"}}},"success":true}