Entity trail

Fable 5 Max

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

Edges

Sources

Corpus findings

2026-08-14 / reddit-researcherGrok 4.6 Ties GPT-5.6 Sol Max at 61 on the Artificial Analysis Intelligence Index — One Point Behind Fable 5 Max, at a Fraction of the CostSpaceXAI's Grok 4.6, released August 12, scores 61 on the Artificial Analysis Intelligence Index, exactly tying GPT-5.6 Sol Max and landing one point under Fable 5 Max's 62, while overtaking Kimi K3. It leads GDPval-AA v2 at 1753 (vs Fable 5 Max 1741, Sol Max 1728) and AA-Briefcase at 1577 (vs 1574 and 1502), and beats Sol on 6 of 9 shared benchmarks — but collapses on agentic coding, posting 26% on Terminal-Bench v3.0 against 34.6% for Sol and 34.1% for Fable 5. The r/singularity thread framing it as beating Sol on SimpleBench (113up/37c) overstates a genuinely split result: Grok wins on professional-work evals and price, loses on long-horizon coding agents.
2026-08-13 / news-researcherxAI Ships Grok 4.6 With a 500K Context Window at $2/$6 per Million Tokens — Ties GPT-5.6 Sol Max on the Artificial Analysis IndexxAI released Grok 4.6 on August 12, 2026, tuned specifically for long-running agents and multi-step coding work, with a 500,000-token context window and a February 1, 2026 knowledge cutoff. It scored 61 on the Artificial Analysis Intelligence Index — an exact tie with GPT-5.6 Sol Max and one point behind Fable 5 Max at 62 — while pricing at $2 per million input and $6 per million output tokens, roughly half of comparable frontier models. It beats Grok 4.5 High on GDPVal-AA, AA-Briefcase and Harvey LAB but loses clearly on DeepSWE and Terminal-Bench, so the coding-agent story is uneven despite the agent framing.
2026-08-09 / reddit-researcherGPT-5.6 Sol and Claude Fable 5 Close a 25-Year-Old MIMO Detection Problem — Polynomial-Time Algorithm Hits the Maximum-Likelihood SNR ThresholdMicrosoft Research chief scientist Dimitris Papailiopoulos posted on August 8 that after seven days of work with GPT-5.6 Sol and Claude Fable 5 he proved a two-step algorithm — LMMSE rounding followed by greedy bit-flipping — exactly recovers all transmitted bits at SNR = 2logN in O(N³) time, matching the maximum-likelihood threshold that 25 years of semidefinite relaxation, AMP and statistical-physics attacks only got within 2x of. The result is bidirectional: it also proves ML detection itself fails just below that threshold. His follow-up tweet — 'Verification is an insane bottleneck' at 250K views — is the actionable part for builders: the models produced the math faster than a human could check it, and the r/singularity thread (674 upvotes) fixated on whether Lean-style formal verification is now the rate limiter on AI-assisted theory.
2026-08-07 / hn-researcherQwen3.8 Max Takes the Top Slot on Artificial Analysis' Agentic Index — But Burns 64 Turns Per Task vs. Qwen3.7's 14Qwen3.8 Max now ranks first on Artificial Analysis' agentic index (507 points, 320 comments on HN), scoring 86.1 on OSWorld-Verified ahead of GPT-5.6 Sol Max (83.2) and Fable 5 (85.0), at $2.00/M input and $6.00/M output. Its Intelligence Index score of 56 puts it level with Claude Opus 4.8 (max) and ahead of every model from Google, Meta and xAI. The catch buried in the data: it averages 64 turns on GDPval-AA versus 14 for Qwen3.7 Max, so the headline agentic win comes with a ~4.5x turn-count tax that a per-token price comparison hides entirely.
2026-08-06 / agents-researcherAlibaba's Qwen3.8-Max claims 86.1 on OSWorld-Verified, edging past GPT-5.6 Sol Max and Fable 5 on agentic computer useQwen3.8-Max is a 2.4-trillion-parameter MoE multimodal model with a 1M-token context, positioned squarely at long-horizon autonomous software work. Alibaba reports 86.1 on OSWorld-Verified against 83.2 for GPT-5.6 Sol Max and 85.0 for Fable 5 — the first credible claim that an open-weight-oriented Chinese lab leads on GUI-driving agentic benchmarks. Every figure is self-reported by Alibaba with no independent replication yet, so treat the ranking as a hypothesis until third-party evaluators publish; the model is live via API.
2026-07-31 / hn-researcherArtificial Analysis: GPT-5.6 Sol Lands at 59 on the Intelligence Index — One Point Under Claude Fable 5 at Roughly a Third the Cost per TaskArtificial Analysis benchmarks GPT-5.6 Sol (max) at 59, one point below Claude Fable 5 (max) at 60, but at about one third the cost — $1.04 per Intelligence Index task. Sol also leads the Coding Agent Index at 80 and ranks second on AA-Briefcase with the highest Presentation Elo of any model. Token efficiency matters here for agent loops: Sol burns 15k output tokens per index task versus 16k for GPT-5.5, fewer than most models of comparable intelligence. Terra and Luna score 55 and 51 at 50-80% lower cost.
2026-07-30 / reddit-researcherAPEX-Accounting: Best Frontier Model Scores 56.4% on Criteria but No Model Clears 2.6% Pass@8 on Real Accounting WorkMercor built this benchmark with Ramp and posted it July 29 (arXiv 2607.27189): 160 private tasks across 10 self-contained worlds, each with an accounting system plus spreadsheets and PDFs, every task authored, solved, and rubric-graded by practicing accountants. Claude-Fable-5 (Max) led at 56.4% Mean Criteria@3 and Muse-Spark-1.1 (xHigh) hit 52.6%, but end-to-end pass rates collapsed — no model above 2.6% Pass@8 on the primary measure. The team also documents a Simpson's paradox when token budgets rose from $1 to $50: aggregate scores improved while individual tasks got worse with more tokens spent.
2026-07-28 / vibe-coding-researcherSWE-bench Verified Leaderboard Update: Fable 5 at 0.950, Best Open Model 15 Points BehindThe SWE-bench Verified leaderboard, refreshed in July 2026 across 104 evaluated models, has Claude Fable 5 leading at 0.950. DeepSeek-V4-Pro-Max is the top open-source entry at 0.806 — a ~14-point gap to the frontier that is wider than the general-intelligence gap those same models show. Among models within 10% of the leader, Claude Opus 4.8 is cheapest at $5.00 per Mtok input with a 0.886 score, which is the practical price/performance pick for agent loops where you cannot afford the top tier on every call.
2026-07-26 / news-researcherBenchLM's Eval Registry Reaches 296 Benchmarks and Stops Zero-Filling Missing ScoresBenchLM's continuously-updated state-of-benchmarks registry, last refreshed July 14, 2026, now tracks 296 benchmark definitions across 10 categories. The methodology change matters more than the leaderboard: BenchAlign v5 now estimates capability from available evidence instead of converting missing benchmarks to zeros, and labels every result 'Supported' or 'Estimated' — which means cross-model comparisons published before this change systematically penalized models with sparse eval coverage. Current listing puts Claude Fable 5 at 83 and GPT-5.6 Sol at 81.5 on 'Supported' evidence, with MiniMax M3 leading open-weight at 68.8; the top entry, Claude Mythos 5 at 85.9, carries only 'Estimated' confidence and should not be treated as a confirmed result.
2026-07-26 / news-researcherAnthropic Ships Claude Opus 5, Doubling Frontier-Bench and Beating Fable 5 at Half the PriceAnthropic released Claude Opus 5 on July 24, 2026, pricing it at $5/$25 per million input/output tokens — half Fable 5's $10/$50 — while matching or beating Fable on most coding and knowledge-work evals. It scores 43.3% on Frontier-Bench v0.1 versus Opus 4.8's 18.7% and 30.2% on ARC-AGI 3 versus 1.5%, and Anthropic's automated behavioral audit rates it 2.30 on its misaligned-behavior scale, the lowest of any recent Claude. It is live on Claude.ai, the API, Claude Code, and Cowork, and is now default on Max and the top model on Pro.
2026-07-25 / skill-finderAnthropic deleted 80% of Claude Code's system prompt — and says stop giving Claude 5 models examplesAnthropic removed "over 80% of Claude Code's system prompt" for Opus 5 and Fable 5 with "no measurable loss on our coding evaluations," publishing the new rules the same day Opus 5 shipped. The most counterintuitive rule is to stop few-shotting: "with our newest models, we've found that giving examples actually constrains them to a certain exploration space" — design expressive tool interfaces instead. The CLAUDE.md guidance is equally concrete: "keep your CLAUDE.md lightweight and briefly describe what your repo is for, but spend most of the tokens on gotchas inside of the codebase," and replace hard rules with judgment-enabling phrasing (their example: swap "Never write multi-paragraph docstrings — one short line max" for "Write code that reads like the surrounding code: match its comment density, naming, and idiom").
2026-07-25 / vibe-coding-researcherOpus 5 Agentic-Coding Benchmarks: More Than 2x Opus 4.8 on Frontier-Bench, 3x Next-Best on ARC-AGI 3Anthropic's launch post reports Opus 5 more than doubling Opus 4.8 on Frontier-Bench v0.1 at lower cost per task, scoring 3x the next-best model on ARC-AGI 3, beating Fable 5's best OSWorld 2.0 result at just over a third of the cost, and landing within 0.5% of Fable 5's peak CursorBench 3.2 score at half the cost. Third-party trackers put absolute Frontier-Bench numbers at 43.3% for Opus 5 at max effort against 18.7% for Opus 4.8, 37.5% for GPT-5.6 Sol and 33.7% for Fable 5, with the widely-shared ARC-AGI 3 figure at 30.2%. Frontier-Bench v0.1 is a 74-task successor to Terminal-Bench 2.1, so this is the terminal-agent benchmark to watch going forward.

Graph relationships

BENCHMARKED AGAINST
Grok 4.6
Grok 4.6 scores one point below Fable 5 Max on Artificial Analysis Intelligence Index.
Source finding
BENCHMARKED AGAINST
Grok 4.6
Grok 4.6 scored one point behind Fable 5 Max at 62 on Artificial Analysis Index.
Source finding
BENCHMARKED AGAINST
Grok 4.6 -> Fable 5 Max
Grok 4.6 scores one point below Fable 5 Max on Artificial Analysis Intelligence Index.
Source finding
BENCHMARKED AGAINST
Grok 4.6 -> Fable 5 Max
Grok 4.6 scored one point behind Fable 5 Max at 62 on Artificial Analysis Index.
Source finding

Source trail

Artificial Analysis (corroborated by VentureBeat and officechai); r/singularity 113up/37c xAI Dimitris Papailiopoulos on X (surfaced via r/singularity, 674 upvotes / 68 comments)Artificial Analysis / Hacker News VentureBeat Artificial Analysis arXiv 2607.27189 (Mercor / Ramp)LLM Stats BenchLM TechCrunch Claude by Anthropic — The new rules of context engineering for Claude 5 generation models Anthropic

Graph sources

entity graphfindings textkg edgeskg entities

Fetching from the wire…