{
 "generated_for": "ToneBench (Towards AI Writing Benchmark)",
 "generated_at": "2026-08-14 00:16 UTC",
 "judge_note": "Scores by 3-judge panel: Claude Opus 5, high effort (Anthropic) + DeepSeek V4 Flash (OpenRouter) + GPT-5.6 Sol, medium reasoning (Codex CLI). The reference scripts are the top bar \u2014 judged honestly they land in the low 90s, not a literal 100.",
 "metrics": [
  {
   "key": "tone_voice",
   "label": "Tone & Voice Match",
   "weight": 0.19,
   "judged": true,
   "desc": "How precisely it captures the reference's anti-hype conversational voice, first-person 'I was there / I use this' experience, running editorial reactions, and 'told not reported' register."
  },
  {
   "key": "writing_quality",
   "label": "Writing Craft & Clarity",
   "weight": 0.13,
   "judged": true,
   "desc": "Sentence rhythm, clarity, paragraph polish, read-aloud quality, and whether the script feels naturally written rather than awkward, stiff, or mechanically assembled."
  },
  {
   "key": "substance_accuracy",
   "label": "Substance, Accuracy & Value",
   "weight": 0.15,
   "judged": true,
   "desc": "Technical depth, factual accuracy, source grounding against the brief/research/reference, and value density: every section should teach or advance something without padding."
  },
  {
   "key": "continuity_emotion",
   "label": "Continuity & Emotion",
   "weight": 0.14,
   "judged": true,
   "desc": "Each paragraph causes the next; a felt emotional register in every section (no Wikipedia blocks); the viewer is transported, not informed. Every section must advance with new information; value-less repetition, restating earlier points in new words, and rehashing recap conclusions are penalized."
  },
  {
   "key": "youtube_best_practices",
   "label": "YouTube Best Practices",
   "weight": 0.12,
   "judged": true,
   "desc": "Genuine (non-formulaic) subscribe CTA in the intro, real comment CTA + one forward-looking question at the end, open loop planted and resolved, re-engagement beats, correct intro line, never 'like and subscribe'. CTAs and beats must be genuinely bridged into the content, not a bolted-on checklist."
  },
  {
   "key": "hook_strength",
   "label": "Hook Strength",
   "weight": 0.1,
   "judged": true,
   "desc": "Power of the opening 15-30 seconds: surprising / contradictory / second-person or personal-frustration hook, clear value promise, no generic intro."
  },
  {
   "key": "length_adherence",
   "label": "Length Adherence",
   "weight": 0.08,
   "judged": false,
   "desc": "Spoken word count vs target; overshooting is penalized harder than undershooting (aim for the short end, ~3 Google-Docs pages)."
  },
  {
   "key": "slop",
   "label": "Slop Score (EQ-Bench + ours)",
   "weight": 0.05,
   "judged": false,
   "desc": "Combined anti-slop: a 50/50 blend of the judged anti-slop (banned filler/structures, spoken em-dash overuse/style compliance, numbers reframed and pushed to [SHOW:]) and the computed EQ-Bench + our-rules lexical slop (sam-paech/slop-score pipeline + our banned-phrase scan). 100 = no slop."
  },
  {
   "key": "cue_quality",
   "label": "Visual Cue Quality",
   "weight": 0.04,
   "judged": true,
   "desc": "Presence and usefulness of inline [SHOW:] cues at every meaningful visual change; spoken text still reads cleanly without them."
  }
 ],
 "provenance": {
  "judge_rev": "2026-08-r5-anchored-seat-receipts",
  "judge_seat_revisions": {
   "published": "2026-08-r5-opus5-metric-anchors",
   "gpt56": "2026-08-r5-gpt56-metric-anchors",
   "dsv4f": "2026-08-r5-dsv4f-metric-anchors"
  },
  "generation_rev": "2026-08-r2-closed-book-isolated-no-tools",
  "rubric_sha": "c71a414779d8",
  "rubric_anchors_sha": "8468bd890d75",
  "weights_sha": "0bb7862dd2c4",
  "contract_sha": "3757714ac717",
  "skills_sha": "f0d67b992535",
  "judge_model": "3-judge panel: Claude Opus 5, high effort (Anthropic) + DeepSeek V4 Flash (OpenRouter) + GPT-5.6 Sol, medium reasoning (Codex CLI)",
  "note": "Scores are comparable only within one (judge \u00d7 rubric \u00d7 weights \u00d7 contract \u00d7 skills) fingerprint. On any change, re-judge the whole board in one batch. For tracking a model over time, prefer `overall` (absolute) and `overall_vs_anchor` (distance to the reference, drift-stable) over Elo, which shifts as models are added."
 },
 "human_baseline": {
  "tone_voice": 92.21,
  "writing_quality": 87.81,
  "substance_accuracy": 88.54,
  "continuity_emotion": 88.85,
  "youtube_best_practices": 88.75,
  "hook_strength": 92.61,
  "length_adherence": 100.0,
  "slop": 85.89,
  "cue_quality": 85.88,
  "overall": 90.3,
  "tasks": {
   "ai_coding_workflows": {
    "tone_voice": 92.22,
    "writing_quality": 89.0,
    "substance_accuracy": 85.56,
    "continuity_emotion": 89.78,
    "youtube_best_practices": 91.0,
    "hook_strength": 94.78,
    "length_adherence": 100.0,
    "slop": 92.41,
    "cue_quality": 91.0,
    "overall": 91.15
   },
   "ai_engineer_hiring_2026": {
    "tone_voice": 93.0,
    "writing_quality": 87.56,
    "substance_accuracy": 82.89,
    "continuity_emotion": 87.0,
    "youtube_best_practices": 91.33,
    "hook_strength": 93.56,
    "length_adherence": 100.0,
    "slop": 86.33,
    "cue_quality": 87.56,
    "overall": 89.8
   },
   "automated_video_workflow": {
    "tone_voice": 90.78,
    "writing_quality": 85.0,
    "substance_accuracy": 89.44,
    "continuity_emotion": 86.11,
    "youtube_best_practices": 87.67,
    "hook_strength": 90.0,
    "length_adherence": 100.0,
    "slop": 87.73,
    "cue_quality": 90.34,
    "overall": 89.29
   },
   "cheap_gpt_proxies": {
    "tone_voice": 91.45,
    "writing_quality": 87.56,
    "substance_accuracy": 86.44,
    "continuity_emotion": 87.11,
    "youtube_best_practices": 84.11,
    "hook_strength": 94.0,
    "length_adherence": 100.0,
    "slop": 82.09,
    "cue_quality": 88.33,
    "overall": 89.05
   },
   "graph_engineering": {
    "tone_voice": 92.33,
    "writing_quality": 87.45,
    "substance_accuracy": 91.11,
    "continuity_emotion": 89.0,
    "youtube_best_practices": 90.44,
    "hook_strength": 92.22,
    "length_adherence": 100.0,
    "slop": 87.0,
    "cue_quality": 82.55,
    "overall": 90.76
   },
   "learn_ai_engineering_2026": {
    "tone_voice": 94.67,
    "writing_quality": 92.78,
    "substance_accuracy": 91.55,
    "continuity_emotion": 91.89,
    "youtube_best_practices": 91.56,
    "hook_strength": 92.44,
    "length_adherence": 100.0,
    "slop": 82.23,
    "cue_quality": 90.67,
    "overall": 92.62
   },
   "loop_engineering": {
    "tone_voice": 92.44,
    "writing_quality": 87.22,
    "substance_accuracy": 85.78,
    "continuity_emotion": 88.44,
    "youtube_best_practices": 90.11,
    "hook_strength": 89.44,
    "length_adherence": 100.0,
    "slop": 88.49,
    "cue_quality": 86.67,
    "overall": 89.8
   },
   "mai_no_distillation": {
    "tone_voice": 91.67,
    "writing_quality": 89.78,
    "substance_accuracy": 91.56,
    "continuity_emotion": 90.33,
    "youtube_best_practices": 86.0,
    "hook_strength": 92.55,
    "length_adherence": 100.0,
    "slop": 79.18,
    "cue_quality": 90.89,
    "overall": 90.64
   },
   "mentorship_release": {
    "tone_voice": 90.67,
    "writing_quality": 84.67,
    "substance_accuracy": 88.22,
    "continuity_emotion": 87.22,
    "youtube_best_practices": 87.78,
    "hook_strength": 93.0,
    "length_adherence": 100.0,
    "slop": 85.73,
    "cue_quality": 74.22,
    "overall": 88.77
   },
   "tai_deployment": {
    "tone_voice": 92.89,
    "writing_quality": 87.11,
    "substance_accuracy": 92.89,
    "continuity_emotion": 91.55,
    "youtube_best_practices": 87.55,
    "hook_strength": 94.11,
    "length_adherence": 100.0,
    "slop": 87.69,
    "cue_quality": 76.56,
    "overall": 91.09
   }
  },
  "elo": 2353.2,
  "cost_usd": 480
 },
 "methodology": {
  "reference_set": {
   "count": 10,
   "avg_word_count": 2060,
   "items": [
    {
     "video_type": "opinion / warning explainer",
     "word_count": 1546
    },
    {
     "video_type": "news-analysis / skeptical explainer",
     "word_count": 1270
    },
    {
     "video_type": "personal roadmap / opinion",
     "word_count": 1777
    },
    {
     "video_type": "short explainer",
     "word_count": 2070
    },
    {
     "video_type": "news-analysis / opinion explainer",
     "word_count": 1678
    },
    {
     "video_type": "founder announcement / personal origin story",
     "word_count": 1190
    },
    {
     "video_type": "personal technical walkthrough / agentic workflow case study",
     "word_count": 4260
    },
    {
     "video_type": "product release announcement / personal observation",
     "word_count": 885
    },
    {
     "video_type": "career guide / hiring analysis",
     "word_count": 3085
    },
    {
     "video_type": "engineering process walkthrough / presentation adaptation",
     "word_count": 2840
    }
   ],
   "authorship": "Reference scripts are AI-assisted and human-written by the Towards AI editorial team, with the final versions being mostly human-written and editorially refined. They represent the style and quality bar of Towards AI educational video scripts.",
   "topics_note": "The reference scripts' titles and video links are deliberately withheld to keep the benchmark hard to contaminate."
  },
  "editorial_review": {
   "type": "collaborative editorial review",
   "inter_rater_reliability_reported": false,
   "blind_independent_scoring": false,
   "description": "The reference scripts went through a collaborative editorial process by the Towards AI content/editorial team. This included review from 2 AI engineers, 1 professional writer, 2 YouTube/script-optimization reviewers with experience in YouTube content, and 1 fact reviewer with strong writing and editing expertise. A final editing pass was then completed by the Towards AI content lead. This was not a blind independent annotation setup, so no inter-rater reliability score is reported. Instead, the team worked collaboratively toward a final version that satisfied the editorial criteria. The human baseline represents this team-reviewed editorial standard rather than an average of isolated reviewer scores."
  },
  "judge": {
   "model": "3-judge panel: Claude Opus 5, high effort (Anthropic) + DeepSeek V4 Flash (OpenRouter) + GPT-5.6 Sol, medium reasoning (Codex CLI)",
   "type": "off-the-shelf",
   "fine_tuned": false,
   "rubric_based": true,
   "same_prompt_for_all_outputs": true,
   "evidence_first": true,
   "panel_scoring": {
    "judges": {
     "published": {
      "label": "Claude Opus 5, high effort (Anthropic)",
      "board_mean": 71.56,
      "spearman_vs_consensus": 0.9954
     },
     "gpt56": {
      "label": "GPT-5.6 Sol, medium reasoning (Codex CLI)",
      "board_mean": 79.9,
      "spearman_vs_consensus": 0.99
     },
     "dsv4f": {
      "label": "DeepSeek V4 Flash (OpenRouter)",
      "board_mean": 78.77,
      "spearman_vs_consensus": 0.9752
     }
    },
    "aggregation": "per-draft, per-metric unweighted mean across the three judges",
    "mean_judge_spread": 9.39,
    "note": "Every published score is the consensus of three family-disjoint judges scoring the same stored draft with the identical rubric prompt. No family judges its own models alone, so any single judge's family tilt is diluted by the two others."
   },
   "self_preference_note": "Any LLM judge may slightly favor its own model family \u2014 stylistic affinity, not recognition, since judging is blind. We measured this directly under the earlier single-judge regime by re-judging the same outputs with different judges: every family scored itself highest, by different amounts. That measurement is why the published score is now a three-judge panel from three different families (Anthropic, OpenAI, DeepSeek): each judge's family tilt is diluted by the two judges that don't share it, and no family's models are scored by their own family alone.",
   "cross_check": null,
   "panel": null,
   "length_control": {
    "tracked_models": 136,
    "current_complete_models": 136,
    "current_ranked_models": 136,
    "availability_attestation_sha256": "b71c0c7f7576ec1f729a00e096d62e92ccdd9ef1129cb4f11bd36d1db496bf92",
    "beta_per_100_words": -0.14,
    "cells_used": 1358,
    "runs_used": 6800,
    "spearman_published_vs_corrected": 0.998,
    "top1_unchanged": true,
    "max_abs_shift_top15": 1,
    "top15_shifts": {
     "claude-fable-5-low": 1
    },
    "note": "AlpacaEval-2-style length control: the three-judge consensus length slope is estimated from within-cell variation only (same model, same brief, 5 samples), then every run's overall is counterfactually rescored at its task's cross-model mean spoken length and models re-ranked. beta_per_100_words is the estimated score change per +100 spoken words; a near-1 spearman and empty top15_shifts mean the published ranking does not ride on verbosity."
   },
   "analysis": {
    "tracked_models": 136,
    "current_complete_models": 136,
    "current_ranked_models": 136,
    "availability_attestation_sha256": "b71c0c7f7576ec1f729a00e096d62e92ccdd9ef1129cb4f11bd36d1db496bf92",
    "n_cells": 1360,
    "n_residual_runs": 6800,
    "inter_metric_cell": {
     "mean_abs_r": 0.902,
     "max_pair": "writing_quality~continuity_emotion",
     "max_r": 0.977
    },
    "inter_metric_residual": {
     "mean_abs_r": 0.527,
     "max_pair": "writing_quality~continuity_emotion",
     "max_r": 0.84
    },
    "length_bias_pooled_r": 0.41,
    "length_bias_residual_r": -0.196,
    "generated_at": "2026-08-14 00:08 UTC",
    "note": "inter_metric_residual is the halo indicator: correlation of the panel consensus's per-metric deviations across re-runs of the same model\u00d7task, with model quality subtracted out. length_bias_residual_r asks whether the judge panel scores the longer variant of the same model higher (length is scored deterministically and word counts are withheld from the judge; this audits what leaks through anyway). Cell-level numbers mix real quality correlation with halo and are context only."
   },
   "description": "Every stored output is scored independently by a panel of three off-the-shelf judge models from three different families, each using the identical fixed rubric prompt; the published score for each metric is the unweighted mean of the three judgments. No judge is fine-tuned. For each metric a judge must first quote short verbatim evidence from the candidate and only then score, so metrics are judged on their own signal rather than an overall impression (halo mitigation); the quotes are stored with every run for auditability. Judges are never told the script's word count; length is measured separately in code. The human-baseline reference scripts are scored by the same panel, so the bar models are compared against moves with the panel, not with any single judge."
  },
  "weights": {
   "items": [
    {
     "key": "tone_voice",
     "label": "Tone & Voice Match",
     "weight": 0.19
    },
    {
     "key": "writing_quality",
     "label": "Writing Craft & Clarity",
     "weight": 0.13
    },
    {
     "key": "substance_accuracy",
     "label": "Substance, Accuracy & Value",
     "weight": 0.15
    },
    {
     "key": "continuity_emotion",
     "label": "Continuity & Emotion",
     "weight": 0.14
    },
    {
     "key": "youtube_best_practices",
     "label": "YouTube Best Practices",
     "weight": 0.12
    },
    {
     "key": "hook_strength",
     "label": "Hook Strength",
     "weight": 0.1
    },
    {
     "key": "length_adherence",
     "label": "Length Adherence",
     "weight": 0.08
    },
    {
     "key": "slop",
     "label": "Slop Score (EQ-Bench + ours)",
     "weight": 0.05
    },
    {
     "key": "cue_quality",
     "label": "Visual Cue Quality",
     "weight": 0.04
    }
   ],
   "sensitivity": {
    "tracked_models": 136,
    "current_complete_models": 136,
    "current_ranked_models": 136,
    "availability_attestation_sha256": "b71c0c7f7576ec1f729a00e096d62e92ccdd9ef1129cb4f11bd36d1db496bf92",
    "samples": 200,
    "concentration": 100.0,
    "relative_sd_note": "concentration 100 \u2248 \u00b130% relative sd on mid-sized weights",
    "max_rank_range90_top10": 6,
    "median_rank_range90": 6,
    "top1_stable_share": 0.78,
    "note": "Ranks recomputed under random perturbations of the editorial metric weights (Dirichlet around the published weights). rank_p05..rank_p95 spans 90% of samples; a small range means the ranking does not hinge on the exact weight choices. top1_stable_share is how often the headline #1 stays #1."
   },
   "rationale": "The specific metric weights are domain-expert editorial weights chosen by the Towards AI team. They reflect how much importance our team gives each criterion for useful, watchable AI educational content. This benchmark is not designed to optimize only for CTR. CTR is heavily affected by title, thumbnail, audience, and distribution, while this benchmark focuses on script quality: clarity, usefulness, structure, tone, hook quality, retention potential, and whether the content brings value to the viewer. These weights are configurable and may evolve as more human feedback, retention data, or editorial evidence becomes available."
  },
  "elo": {
   "method": "pairwise comparisons of rubric scores",
   "k": 16,
   "base": 1500,
   "draw_margin_floor": 1.0,
   "adaptive_draw_margin": true,
   "small_sample_draw_rule": "Welch-Satterthwaite Student-t 95%",
   "seeds": 25,
   "passes": 4,
   "pairwise_check": {
    "judge": "claude-code:claude-opus-5",
    "judge_config": null,
    "api_usage": null,
    "top": 15,
    "window": 2,
    "runs_per_pair": 1,
    "tasks": [
     "cheap_gpt_proxies",
     "graph_engineering",
     "learn_ai_engineering_2026",
     "loop_engineering",
     "mai_no_distillation",
     "tai_deployment",
     "automated_video_workflow",
     "mentorship_release",
     "ai_engineer_hiring_2026",
     "ai_coding_workflows"
    ],
    "n_pairs": 27,
    "n_judgments": 540,
    "order_consistency": 0.789,
    "spearman_vs_board": 0.25,
    "settled_pairs": 0,
    "settled_pair_agreement": null,
    "unsettled_pairs": 25,
    "unsettled_pair_agreement": 0.72,
    "note": "Independent head-to-head check: the judge saw two anonymized scripts side by side (both orders; only order-consistent verdicts count as wins) and never saw the rubric scores. order_consistency is the share of pair-cells where the two order-swapped verdicts agreed. IMPORTANT framing: window-W pairing compares near-neighbours, i.e. mostly pairs whose bootstrap CIs overlap \u2014 near-ties the board itself does not claim to order. Low spearman/consistency over those pairs is expected sampling noise. settled_pair_agreement \u2014 head-to-head agreement on the pairs the board claims ARE settled (non-overlapping CIs) \u2014 is the statistic that could actually contradict the board.",
    "generated_at": "2026-08-13 21:15 UTC",
    "judge_rev_at_run": null
   },
   "task_sensitivity": {
    "tasks": [
     "ai_coding_workflows",
     "ai_engineer_hiring_2026",
     "automated_video_workflow",
     "cheap_gpt_proxies",
     "graph_engineering",
     "learn_ai_engineering_2026",
     "loop_engineering",
     "mai_no_distillation",
     "mentorship_release",
     "tai_deployment"
    ],
    "tracked_models": 136,
    "current_complete_models": 136,
    "current_ranked_models": 136,
    "availability_attestation_sha256": "b71c0c7f7576ec1f729a00e096d62e92ccdd9ef1129cb4f11bd36d1db496bf92",
    "max_shift_top10": 4,
    "median_max_shift": 2,
    "top1_stable": false,
    "note": "Each model's Elo rank recomputed with one task dropped from the arena, for every task. max_shift is the largest rank move any drop causes; small values mean the ranking does not hinge on a single topic. top1_stable is whether the headline #1 stays #1 under every drop."
   },
   "separability": {
    "tracked_models": 136,
    "current_complete_models": 136,
    "current_ranked_models": 136,
    "availability_attestation_sha256": "b71c0c7f7576ec1f729a00e096d62e92ccdd9ef1129cb4f11bd36d1db496bf92",
    "all_pairs": {
     "pairs": 9180,
     "separable": 8307,
     "separability": 0.905,
     "pairs_without_ci": 0
    },
    "top10": {
     "pairs": 45,
     "separable": 13,
     "separability": 0.289,
     "pairs_without_ci": 0
    },
    "top30": {
     "pairs": 435,
     "separable": 267,
     "separability": 0.614,
     "pairs_without_ci": 0
    },
    "adjacent_pairs": {
     "pairs": 135,
     "separable": 3,
     "separability": 0.022
    },
    "note": "Fraction of model pairs whose bootstrap 95% Elo CIs do not overlap \u2014 the share of the board's ordering claims the data actually settles (Arena-Hard's 'separability with confidence'). Adjacent-pair separability is expected to be low everywhere (neighbours are near-ties by construction); the all-pairs number is the headline. Reference points: Arena-Hard 87.4%, MT-Bench 22.6%."
   },
   "sampling_validation": {
    "tracked_models": 136,
    "task_count": 10,
    "cell_run_depths": {
     "5": 1360
    },
    "sampling_stability": {
     "full_n": 5,
     "models": 136,
     "tasks": 10,
     "by_n": [
      {
       "n": 1,
       "repetitions": 200,
       "mean_spearman_vs_full_n": 0.994,
       "top1_retention": 0.49,
       "top10_mean_overlap": 0.863,
       "mean_absolute_rank_shift_by_full_score_tier": {
        "top": 2.812,
        "middle": 4.001,
        "bottom": 1.951
       }
      },
      {
       "n": 2,
       "repetitions": 200,
       "mean_spearman_vs_full_n": 0.9968,
       "top1_retention": 0.52,
       "top10_mean_overlap": 0.902,
       "mean_absolute_rank_shift_by_full_score_tier": {
        "top": 1.99,
        "middle": 2.877,
        "bottom": 1.448
       }
      },
      {
       "n": 3,
       "repetitions": 200,
       "mean_spearman_vs_full_n": 0.9978,
       "top1_retention": 0.505,
       "top10_mean_overlap": 0.928,
       "mean_absolute_rank_shift_by_full_score_tier": {
        "top": 1.656,
        "middle": 2.386,
        "bottom": 1.202
       }
      },
      {
       "n": 4,
       "repetitions": 200,
       "mean_spearman_vs_full_n": 0.9982,
       "top1_retention": 0.515,
       "top10_mean_overlap": 0.934,
       "mean_absolute_rank_shift_by_full_score_tier": {
        "top": 1.416,
        "middle": 2.092,
        "bottom": 1.099
       }
      },
      {
       "n": 5,
       "repetitions": 200,
       "mean_spearman_vs_full_n": 0.9986,
       "top1_retention": 0.565,
       "top10_mean_overlap": 0.9415,
       "mean_absolute_rank_shift_by_full_score_tier": {
        "top": 1.285,
        "middle": 1.909,
        "bottom": 0.966
       }
      }
     ]
    },
    "draw_rule_validation": {
     "overall": {
      "pairs": 91800,
      "student_t_draws": 21946,
      "normal_1_96_draws": 17393,
      "newly_drawn_by_small_sample_rule": 4553,
      "student_t_draw_share": 0.2391,
      "normal_1_96_draw_share": 0.1895
     },
     "by_task": {
      "ai_coding_workflows": {
       "pairs": 9180,
       "student_t_draws": 2149,
       "normal_1_96_draws": 1732,
       "newly_drawn_by_small_sample_rule": 417,
       "student_t_draw_share": 0.2341,
       "normal_1_96_draw_share": 0.1887
      },
      "ai_engineer_hiring_2026": {
       "pairs": 9180,
       "student_t_draws": 2048,
       "normal_1_96_draws": 1667,
       "newly_drawn_by_small_sample_rule": 381,
       "student_t_draw_share": 0.2231,
       "normal_1_96_draw_share": 0.1816
      },
      "automated_video_workflow": {
       "pairs": 9180,
       "student_t_draws": 1929,
       "normal_1_96_draws": 1525,
       "newly_drawn_by_small_sample_rule": 404,
       "student_t_draw_share": 0.2101,
       "normal_1_96_draw_share": 0.1661
      },
      "cheap_gpt_proxies": {
       "pairs": 9180,
       "student_t_draws": 1877,
       "normal_1_96_draws": 1481,
       "newly_drawn_by_small_sample_rule": 396,
       "student_t_draw_share": 0.2045,
       "normal_1_96_draw_share": 0.1613
      },
      "graph_engineering": {
       "pairs": 9180,
       "student_t_draws": 2245,
       "normal_1_96_draws": 1768,
       "newly_drawn_by_small_sample_rule": 477,
       "student_t_draw_share": 0.2446,
       "normal_1_96_draw_share": 0.1926
      },
      "learn_ai_engineering_2026": {
       "pairs": 9180,
       "student_t_draws": 2156,
       "normal_1_96_draws": 1739,
       "newly_drawn_by_small_sample_rule": 417,
       "student_t_draw_share": 0.2349,
       "normal_1_96_draw_share": 0.1894
      },
      "loop_engineering": {
       "pairs": 9180,
       "student_t_draws": 1929,
       "normal_1_96_draws": 1544,
       "newly_drawn_by_small_sample_rule": 385,
       "student_t_draw_share": 0.2101,
       "normal_1_96_draw_share": 0.1682
      },
      "mai_no_distillation": {
       "pairs": 9180,
       "student_t_draws": 2228,
       "normal_1_96_draws": 1792,
       "newly_drawn_by_small_sample_rule": 436,
       "student_t_draw_share": 0.2427,
       "normal_1_96_draw_share": 0.1952
      },
      "mentorship_release": {
       "pairs": 9180,
       "student_t_draws": 2510,
       "normal_1_96_draws": 1983,
       "newly_drawn_by_small_sample_rule": 527,
       "student_t_draw_share": 0.2734,
       "normal_1_96_draw_share": 0.216
      },
      "tai_deployment": {
       "pairs": 9180,
       "student_t_draws": 2875,
       "normal_1_96_draws": 2162,
       "newly_drawn_by_small_sample_rule": 713,
       "student_t_draw_share": 0.3132,
       "normal_1_96_draw_share": 0.2355
      }
     },
     "by_full_score_pair_tier": {
      "top": {
       "pairs": 9900,
       "student_t_draws": 5518,
       "normal_1_96_draws": 4544,
       "newly_drawn_by_small_sample_rule": 974,
       "student_t_draw_share": 0.5574,
       "normal_1_96_draw_share": 0.459
      },
      "cross_tier": {
       "pairs": 61650,
       "student_t_draws": 6787,
       "normal_1_96_draws": 4927,
       "newly_drawn_by_small_sample_rule": 1860,
       "student_t_draw_share": 0.1101,
       "normal_1_96_draw_share": 0.0799
      },
      "bottom": {
       "pairs": 10350,
       "student_t_draws": 3496,
       "normal_1_96_draws": 2773,
       "newly_drawn_by_small_sample_rule": 723,
       "student_t_draw_share": 0.3378,
       "normal_1_96_draw_share": 0.2679
      },
      "middle": {
       "pairs": 9900,
       "student_t_draws": 6145,
       "normal_1_96_draws": 5149,
       "newly_drawn_by_small_sample_rule": 996,
       "student_t_draw_share": 0.6207,
       "normal_1_96_draw_share": 0.5201
      }
     },
     "effective_t_critical_range_after_floor": [
      2.365,
      4.027
     ],
     "note": "Every unordered current-model pair on every task is checked. The comparison shows exactly how many outcomes the N-aware Student-t rule changes relative to the former large-sample 1.96 shortcut."
    }
   },
   "description": "Elo is calculated from pairwise comparisons between model outputs on each task. Every output is scored once on the weighted rubric; then, for each task, every pair of models is compared and the one with the higher weighted overall score wins, with statistically-indistinguishable pairs counted as draws (the comparison uses each script's rubric score, not a separate side-by-side judgment). These outcomes are aggregated into an Elo-style relative ranking, averaged over many shuffled passes so update order doesn't bias the result. Elo should be read as a relative ranking signal within this benchmark, not an absolute universal quality score.",
   "formula_note": "Elo updates use pairwise win/loss/draw outcomes and the expected-score difference between two systems (logistic, base 400); ratings start at 1500 with K=16, averaged over 25 seeds \u00d7 4 passes.",
   "draw_note": "A pair is a draw when its overall gap on a task is within what the two cells' run-to-run noise can explain: |diff| \u2264 t*\u00b7\u221a(SE_a\u00b2+SE_b\u00b2), where SE uses the sample standard deviation over the cell's N run overalls and t* is the two-sided 95% Welch-Satterthwaite Student-t critical value for those two small samples, with a floor of 1.0 point. 'Draw' therefore means 'statistically indistinguishable on this task', not an arbitrary fixed band.",
   "uncertainty_note": "Each model carries a 95% confidence interval (elo_ci) from a bootstrap over the per-run scores: every (model, task) cell's N run overalls are resampled with replacement, the arena is rebuilt, and Elo is recomputed (200 repetitions). Overlapping intervals mean the ranking between those models is not settled.",
   "bt_check": {
    "method": "Bradley-Terry MLE (Hunter MM), ties = half win each, fit on the exact pairwise outcomes the published Elo uses \u2014 the same model + tie handling as LMArena's arena-rank. Deterministic: no shuffle passes, no K-factor. Ratings shown on the Elo scale, mean-matched to the published board.",
    "n_models": 136,
    "n_comparisons": 91800,
    "draw_share": 0.2391,
    "spearman_published_vs_bt": 0.9999,
    "pearson_published_vs_bt": 0.9999,
    "top1_same": true,
    "top1_published": "claude-opus-5-max",
    "top1_bt": "claude-opus-5-max",
    "max_rank_shift": 2,
    "max_rank_shift_top10": 1,
    "mean_abs_rating_diff": 4.6,
    "max_abs_rating_diff": 51.0,
    "models_with_rank_shift_gt3": 0
   }
  },
  "human_validation": null,
  "limitations": "This benchmark should be interpreted as directional rather than statistically definitive, especially while the reference set is small. It is designed to compare outputs against Towards AI's editorial criteria for educational video scripts, not to measure all possible dimensions of content performance."
 },
 "tasks": [
  "cheap_gpt_proxies",
  "graph_engineering",
  "learn_ai_engineering_2026",
  "loop_engineering",
  "mai_no_distillation",
  "tai_deployment",
  "automated_video_workflow",
  "mentorship_release",
  "ai_engineer_hiring_2026",
  "ai_coding_workflows"
 ],
 "availability": {
  "tracked_models": 136,
  "current_complete_models": 136,
  "current_ranked_models": 136,
  "current_task_count": 10,
  "expected_model_task_cells": 1360,
  "unavailable_model_task_cells": 0,
  "unavailable_models": [],
  "attestation_sha256": "b71c0c7f7576ec1f729a00e096d62e92ccdd9ef1129cb4f11bd36d1db496bf92",
  "note": "Historical completeness and current rank eligibility are distinct. Current ranks include only complete models in the selected runnable cohort; complete but retired routes and models with an attested unavailable task retain an archived scorecard and are not compared against that field."
 },
 "models": [
  {
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   "display": "Claude Opus 5 (max effort)",
   "provenance": {
    "provider": "claude-code",
    "model_id": "claude-opus-5",
    "endpoint_url": null,
    "reasoning_effort": "max",
    "first_run": "2026-08-12",
    "last_run": "2026-08-13"
   },
   "pricing_used": {
    "cache_write": 6.25,
    "cached_input": 0.5,
    "est": false,
    "input": 5.0,
    "output": 25.0
   },
   "overall_vs_anchor": 0.07,
   "release": "2026-07-23",
   "params": 500,
   "open": false,
   "size_est": true,
   "cost_usd": 0.127408,
   "cost_std_usd": 0.037948,
   "avg_input_tokens": 11319.9,
   "avg_output_tokens": 2832.3,
   "avg_total_tokens": 14152.2,
   "avg_latency_s": 244.21,
   "cost_est": false,
   "cost_method": "estimated",
   "avg_reasoning_tokens": null,
   "price_in_per_mtok": 5.0,
   "price_out_per_mtok": 25.0,
   "local": false,
   "elo": 2359.7,
   "elo_ci": [
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    2404.5
   ],
   "pairwise_elo": 1737.3,
   "pairwise_rank": 1,
   "human_elo": null,
   "human_rank": null,
   "editor_verdict": null,
   "overall": 90.37,
   "overall_std": 1.53,
   "n_runs": 5,
   "panel": {
    "per_judge_overall": {
     "published": 88.66,
     "dsv4f": 89.04,
     "gpt56": 93.4
    },
    "judge_spread": 4.74,
    "judges_used": [
     "published",
     "dsv4f",
     "gpt56"
    ]
   },
   "metric_averages": {
    "tone_voice": 90.93,
    "writing_quality": 90.97,
    "substance_accuracy": 90.29,
    "continuity_emotion": 90.17,
    "youtube_best_practices": 89.56,
    "hook_strength": 91.91,
    "length_adherence": 87.2,
    "slop": 93.04,
    "cue_quality": 88.24,
    "anti_slop_numbers": 89.53,
    "slop_score": 96.55
   },
   "metric_std": {
    "tone_voice": 1.14,
    "writing_quality": 0.99,
    "substance_accuracy": 1.3,
    "continuity_emotion": 1.07,
    "youtube_best_practices": 1.71,
    "hook_strength": 1.5,
    "length_adherence": 7.61,
    "slop": 1.72,
    "cue_quality": 2.27,
    "anti_slop_numbers": 2.96,
    "slop_score": 1.67
   },
   "record": {
    "wins": 1187,
    "losses": 3,
    "draws": 160
   },
   "current_complete": true,
   "ranking_status": "current_10_task_rank",
   "score_scope": "10_task_current",
   "tasks": {
    "cheap_gpt_proxies": {
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     "spoken_word_count": 1628,
     "target_word_count": 1546,
     "metrics": {
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      "writing_quality": 89.6,
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      "continuity_emotion": 90.07,
      "youtube_best_practices": 88.8,
      "hook_strength": 89.07,
      "length_adherence": 94.77,
      "slop": 93.88,
      "cue_quality": 88.13,
      "anti_slop_numbers": 90.6,
      "slop_score": 97.16
     },
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    },
    "graph_engineering": {
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     "spoken_word_count": 1336,
     "target_word_count": 1257,
     "metrics": {
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      "writing_quality": 92.53,
      "substance_accuracy": 91.13,
      "continuity_emotion": 92.4,
      "youtube_best_practices": 92.0,
      "hook_strength": 92.6,
      "length_adherence": 89.28,
      "slop": 93.46,
      "cue_quality": 94.13,
      "anti_slop_numbers": 93.07,
      "slop_score": 93.86
     },
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    },
    "learn_ai_engineering_2026": {
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     "spoken_word_count": 1833,
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     "metrics": {
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      "writing_quality": 91.2,
      "substance_accuracy": 90.33,
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      "youtube_best_practices": 91.67,
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      "slop_score": 98.34
     },
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    },
    "loop_engineering": {
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     "spoken_word_count": 2155,
     "target_word_count": 2080,
     "metrics": {
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      "writing_quality": 91.6,
      "substance_accuracy": 88.2,
      "continuity_emotion": 89.67,
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     },
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    },
    "mai_no_distillation": {
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     "spoken_word_count": 1642,
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     "metrics": {
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      "writing_quality": 91.33,
      "substance_accuracy": 91.87,
      "continuity_emotion": 90.6,
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      "hook_strength": 92.07,
      "length_adherence": 90.3,
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     },
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    },
    "tai_deployment": {
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     "metrics": {
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      "writing_quality": 89.8,
      "substance_accuracy": 88.33,
      "continuity_emotion": 88.67,
      "youtube_best_practices": 89.8,
      "hook_strength": 93.07,
      "length_adherence": 86.5,
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      "slop_score": 96.15
     },
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    },
    "automated_video_workflow": {
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     "spoken_word_count": 3810,
     "target_word_count": 4260,
     "metrics": {
      "tone_voice": 90.6,
      "writing_quality": 91.2,
      "substance_accuracy": 91.33,
      "continuity_emotion": 89.93,
      "youtube_best_practices": 86.87,
      "hook_strength": 90.8,
      "length_adherence": 87.37,
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      "anti_slop_numbers": 90.0,
      "slop_score": 96.66
     },
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    },
    "mentorship_release": {
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     "metrics": {
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      "writing_quality": 91.6,
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      "continuity_emotion": 91.2,
      "youtube_best_practices": 89.6,
      "hook_strength": 92.87,
      "length_adherence": 82.62,
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    },
    "ai_engineer_hiring_2026": {
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     "target_word_count": 3085,
     "metrics": {
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    },
    "ai_coding_workflows": {
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     "spoken_word_count": 3270,
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     "metrics": {
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     },
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   },
   "rank": 1
  },
  {
   "slug": "claude-fable-5-max",
   "display": "Claude Fable 5 (max effort + 4.8 fallback)",
   "provenance": {
    "provider": "claude-code",
    "model_id": "claude-fable-5",
    "endpoint_url": null,
    "reasoning_effort": "max",
    "first_run": "2026-08-12",
    "last_run": "2026-08-13"
   },
   "pricing_used": {
    "cache_write": 12.5,
    "cached_input": 1.0,
    "est": false,
    "input": 10.0,
    "output": 50.0
   },
   "overall_vs_anchor": 0.05,
   "release": "2026-06-09",
   "params": 500,
   "open": false,
   "size_est": true,
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   "cost_std_usd": 0.512822,
   "avg_input_tokens": 14864.6,
   "avg_output_tokens": 14132.6,
   "avg_total_tokens": 28997.2,
   "avg_latency_s": 492.77,
   "cost_est": false,
   "cost_method": "estimated",
   "avg_reasoning_tokens": 0.0,
   "price_in_per_mtok": 10.0,
   "price_out_per_mtok": 50.0,
   "local": false,
   "elo": 2348.8,
   "elo_ci": [
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    2393.5
   ],
   "pairwise_elo": 1504.7,
   "pairwise_rank": 9,
   "human_elo": null,
   "human_rank": null,
   "editor_verdict": null,
   "overall": 90.35,
   "overall_std": 1.61,
   "n_runs": 5,
   "panel": {
    "per_judge_overall": {
     "published": 88.11,
     "dsv4f": 89.48,
     "gpt56": 93.47
    },
    "judge_spread": 5.36,
    "judges_used": [
     "published",
     "dsv4f",
     "gpt56"
    ]
   },
   "metric_averages": {
    "tone_voice": 90.35,
    "writing_quality": 89.83,
    "substance_accuracy": 90.03,
    "continuity_emotion": 89.08,
    "youtube_best_practices": 89.3,
    "hook_strength": 90.31,
    "length_adherence": 94.13,
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    "cue_quality": 89.19,
    "anti_slop_numbers": 90.13,
    "slop_score": 97.31
   },
   "metric_std": {
    "tone_voice": 1.33,
    "writing_quality": 1.58,
    "substance_accuracy": 1.15,
    "continuity_emotion": 1.52,
    "youtube_best_practices": 1.93,
    "hook_strength": 2.29,
    "length_adherence": 3.08,
    "slop": 1.71,
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    "slop_score": 1.36
   },
   "record": {
    "wins": 1173,
    "losses": 1,
    "draws": 176
   },
   "current_complete": true,
   "ranking_status": "current_10_task_rank",
   "score_scope": "10_task_current",
   "tasks": {
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     "spoken_word_count": 1624,
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     "metrics": {
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      "writing_quality": 89.13,
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      "continuity_emotion": 89.0,
      "youtube_best_practices": 87.47,
      "hook_strength": 88.27,
      "length_adherence": 92.18,
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      "slop_score": 96.68
     },
     "tokens": 34599.6,
     "latency_s": 223.48,
     "cost_usd": 1.09182
    },
    "graph_engineering": {
     "overall": 90.58,
     "spoken_word_count": 1285,
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     "metrics": {
      "tone_voice": 90.33,
      "writing_quality": 90.4,
      "substance_accuracy": 89.07,
      "continuity_emotion": 90.13,
      "youtube_best_practices": 88.87,
      "hook_strength": 89.67,
      "length_adherence": 95.73,
      "slop": 93.54,
      "cue_quality": 92.93,
      "anti_slop_numbers": 91.8,
      "slop_score": 95.28
     },
     "tokens": 32345.0,
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     "cost_usd": 0.97661
    },
    "learn_ai_engineering_2026": {
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     "metrics": {
      "tone_voice": 90.53,
      "writing_quality": 89.07,
      "substance_accuracy": 88.27,
      "continuity_emotion": 87.4,
      "youtube_best_practices": 90.2,
      "hook_strength": 87.87,
      "length_adherence": 95.76,
      "slop": 94.05,
      "cue_quality": 87.0,
      "anti_slop_numbers": 90.27,
      "slop_score": 97.84
     },
     "tokens": 43198.6,
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