Entity trail

Opus Max

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

Briefing refs

Findings

Edges

Sources

Showing the first 40 findings. More graph evidence exists in the corpus.

Corpus findings

2026-08-08 / sources-researcherQwen3.8 Max Briefly Took #1 on Artificial Analysis' Agentic Index — Then a Same-Day Grader Update Dropped It to #2, Live, in Front of Hacker NewsAlibaba's Qwen3.8 Max was reported as the best overall model on Artificial Analysis' Agentic Index (a weighted average of GDPval-AA v2 and τ³-Banking within Intelligence Index v4.1), drawing 540 points on HN. Readers watching the page saw Qwen at 55.4 vs Opus Max at 55.3, then on reload Opus Max at 59.2 vs Qwen at 58.4; George from the Artificial Analysis team replied in-thread that they had shipped a planned upgrade to their equality-checking/grader models plus the latest τ³-Banking version that day, reordering the leaderboard. Opus 5 remains first on the broader Intelligence Index. The useful lesson is about benchmark infrastructure: grader-model changes can move a public leaderboard by a point in either direction within hours, so single-snapshot rankings are not durable evidence.
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.
2026-08-03 / reddit-researcherr/ClaudeAI User Reports Switching From Opus to Sonnet 5 Made a Max Subscription Effectively UnlimitedA r/ClaudeAI post (123 upvotes, 42 comments) from a user who had not touched Sonnet in three months reports that moving their workload to Sonnet 5 removed practical rate-limit pressure on a Max subscription entirely. The post explicitly frames itself against community criticism of Sonnet 5 and against GPT-5.6 Luna's 80% price cut on July 30 ($1/$6 down to $0.20/$1.20). The builder-relevant pattern here is model-tier arbitrage inside a fixed subscription: the constraint people optimize is quota consumption rate, not per-token price, and that changes which model they reach for by default.
2026-07-31 / hn-researcherSteelman Labs: Computer-Use Agents Peak at 20.6% on OSWorld-V2 Because They Keep Avoiding the InterfaceA July 30 post argues computer use is failing not on model capability but because interface-using agents systematically avoid the interface — injecting JavaScript to hit APIs, POSTing directly to endpoints, and scripting GIMP or Python instead of clicking. Cited numbers: best-in-class 20.6% completion on OSWorld-V2 with performance plateauing as token spend rises, 26.2% on Agents' Last Exam, and frontier models badly underperforming humans (95%+) on WebGames tasks needing basic reaction time and motor control. Models tested span GPT-5.5, Claude Opus 4.7/4.8, Sonnet 4.6, MiniMax M3, and Qwen 3.7-Plus.
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.
2026-07-26 / github-pulse-researcherClaude Opus 5 Lands in GitHub Copilot on July 24 — Pro+, Max, Business and Enterprise, Billed at Provider API List Price and Off by Default for AdminsGitHub's changelog dated 2026-07-24 announces Claude Opus 5 rolling out gradually across VS Code, Visual Studio, Copilot CLI, the Copilot cloud agent, the Copilot app, github.com, GitHub Mobile, JetBrains, Xcode, and Eclipse for Pro+, Max, Business, and Enterprise. GitHub cites early testing showing strong agentic-coding performance on autonomous code changes and regression verification, and 'reducing unnecessary execution overhead.' Two operational details matter: it is charged at provider API list price under usage-based billing rather than a flat premium multiplier, and Enterprise/Business admins must explicitly enable it in Copilot settings before anyone sees it.
2026-07-25 / skill-finderBreaking on Opus 5: `thinking: disabled` with `xhigh` or `max` effort returns a 400 — and disabling thinking leaks tool calls into textThinking is on by default on Opus 5, and disabling it is now accepted only at effort `high` or below; combining `thinking: {"type": "disabled"}` with `xhigh` or `max` returns a 400 error, a breaking change from Opus 4.8 where the two were independent. Worse, with thinking disabled the model "occasionally writes a tool call into its user-facing text instead of emitting a structured `tool_use` block" — the call never runs, and in agentic loops the leaked text poisons every later turn. Anthropic's recommendation is to stop disabling thinking entirely ("thinking enabled at `low` effort performs better than thinking disabled at similar cost"), and if you must, use their single combined mitigation instruction rather than naming `<thinking>` tags, which is less effective.
2026-07-25 / skill-finderOpus 5 delegates to subagents more readily — and Claude Code 2.1.219 just raised nested spawn depth from 1 to 3 by defaultAnthropic documents that Opus 5 "delegates to subagents more readily than prior models," which "multiplies cost and time when applied to small tasks," and publishes a paste-in constraint block: delegate only for large genuinely-independent parallelizable work, never to verify your own work, and prefer one subagent over several. On the same day, Claude Code v2.1.219 raised the default nested subagent spawn depth from 1 to 3 — meaning a subagent can now spawn a subagent that spawns a subagent unless you set `CLAUDE_CODE_MAX_SUBAGENT_SPAWN_DEPTH=1`. Those two changes compound in the wrong direction; if you run agent fleets on a subscription or a fixed budget, set the env var and add the delegation cap to your system prompt now.
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 / arxiv-researcherWorkBuddy Leaderboard: Same Model Swings 13 Points Between Harnesses — Infrastructure, Not Weights, Decides the ScoreThe live WorkBuddy Bench leaderboard shows Claude Opus 4.8 leading Code (74.43 / 77.90 across the two harnesses) and Web (68.14 / 69.86), GLM-5.2 leading Security (76.32 / 80.86), and GPT-5.5 topping Office at 86.05 under the Claude Code harness. The headline result for builders is harness dependency: GPT-5.5 on Security ranged from 77.91 to 64.39 and GLM-5.2 on Web from 67.43 to 60.71 purely by swapping the agent harness. Token economics diverge just as sharply — GPT-5.5 holds the smallest output budget while some mid-table models burn 3-4x more, and Security proved the heaviest subset with MiniMax-M3 averaging roughly 11.1 million input tokens per run.
2026-07-25 / reddit-researcherOpus 5 Scores 30.2% on ARC-AGI 3, Nearly 4x the Previous Best of 7.8% and 20x Opus 4.8's 1.5%Claude Opus 5 posted 30.2% on ARC-AGI 3, against a prior field best of 7.8% held by GPT-5.6 Sol at max reasoning effort and just 1.5% from Anthropic's own Opus 4.8. François Chollet designed ARC-AGI explicitly to resist the memorization and benchmark-targeting that inflate scores elsewhere, which makes a jump of this size on a single generation unusual rather than routine. The ARC Prize leaderboard hit the Hacker News front page within hours of the model's release (106 points), and the result drove the top posts on r/singularity and r/ClaudeAI. Caveat worth holding: a 30.2% score still means the model fails roughly seven of ten tasks a human solves easily.
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

COMPETES WITH
Qwen3.8-Max
Qwen3.8 Max and Opus Max compete for top ranking on Artificial Analysis' Agentic Index
Source finding
RELEASED
Anthropic
Anthropic released Opus Max
Source finding
COMPETES WITH
Qwen3.8-Max -> Opus Max
Qwen3.8 Max and Opus Max compete for top ranking on Artificial Analysis' Agentic Index
Source finding
RELEASED
Anthropic -> Opus Max
Anthropic released Opus Max
Source finding

Source trail

drawing 540 points on HN Artificial Analysis / Hacker News r/ClaudeAI (123 upvotes / 42 comments)Steelman Labs / Hacker News TechCrunch GitHub Changelog Claude Platform Docs — What's new in Claude Opus 5 Claude Platform Docs — Prompting Claude Opus 5 / Claude Code CHANGELOG v2.1.219 Claude by Anthropic — The new rules of context engineering for Claude 5 generation models Tencent WorkBuddy Bench Leaderboard OfficeChai (corroborated by arcprize.org/leaderboard, Anthropic)Anthropic

Graph sources

entity graphfindings textkg edgeskg entitiesnewsletter issues

Fetching from the wire…