Entity trail

Opus 5

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

2026-09-12 / reddit-researcherSame $5 prompt, three frontier models: Astra spent its budget on React boilerplate while Opus and Fable shipped one JS fileA developer gave Fable 5.1, Opus 5 and GPT-6 Astra the identical one-line prompt "create a simulation of the milkyway andromeda collision," spending roughly $5 in tokens on each, and published all three outputs at github.com/fynnfluegge/milkyway-andromeda-merger-bench (repo created 2026-09-12). Opus and Fable each produced a single JavaScript file focused on particle simulation; Astra scaffolded a React project and delivered visibly static physics. The author's split verdict is worth noting: Fable optimized for physics accuracy, Opus for visual result, which matches a commenter's point that Opus is specifically tuned toward shareable visual output.
2026-09-12 / reddit-researcherThe distillation story flips: Anthropic's report says DeepSeek relayed Chengdu CCTV and PLA-facility surveillance work to Claude, and leaked a PRC firm's own AI roadmapRead past the IP-theft headline and the September threat report describes Chinese users' sensitive data flowing the other way. GTG-16001 routed over 12.1 million exchanges to Claude Opus in 14 days, including CCTV analysis from hundreds of cameras in Chengdu covering PLA facilities, Russian Ministry of Defense database credentials, and Chinese public security case-management data. One case describes a PRC technology company employee who believed they were using DeepSeek to analyze internal documentation, and thereby handed Claude the full specifications, org structure and strategic objectives of a flagship AI program. GTG-16005 (Alibaba Qwen) extracted over 151 million exchanges peaking near 3 million a day from 3,500+ fraudulent accounts.
2026-09-12 / vibe-coding-researchertokentab reads the session logs three harnesses already write and turns them into a cost statementCreated 2026-09-07 and at 481 stars, tokentab parses `~/.claude/projects/**/*.jsonl`, `~/.codex/sessions/**/rollout-*.jsonl` and `~/.gemini/tmp/**/session-*.json`, with Cursor partially implemented. Token counts come straight from the logs with no estimation, a hand-maintained table in `tokentab/pricing/prices.py` holds dollars per million tokens, model names fuzzy-match (`claude-opus-4-6-20260514` resolves to `claude-opus-4-6`), and cached tokens are deducted so they aren't double-charged. Output is a terminal table, JSON, or a monthly statement served at `localhost:4747` with `-web`.
2026-09-12 / hn-researcherSakana AI Ships Fugu Max and Fugu Ultra v2, an Orchestrator That Routes Each Task to the Leanest Model, at $2/$6 per Million TokensSakana AI released Fugu Max and Fugu Ultra v2 on September 11, 2026, priced at $2 per million input and $6 per million output tokens, which the company says undercuts Sonnet 5, GPT 5.6 Terra and Kimi K3 by 40 to 60 percent on output. Fugu is not a single model but an orchestrator trained to hand each task to a pool of open-weights and specialized models; Fugu Max takes best overall score on six benchmarks including Terminal Bench 2.1, GPQAD, AA-LCR, AutomationBench and SWEFish, and expands the cost-performance Pareto frontier on 7 of 10. Fugu Ultra v2 scores 48.3 on Chartography against Claude Opus 5 at 27.3 and Fable 5 at 29.5, and 74.3 on DeepSWE, available now through an OpenAI-compatible API.
2026-09-12 / sources-researcherAnthropic attributes its largest measured distillation campaign to Alibaba: 3 million chain-of-thought exchanges a day from 3,500 fake accountsAnthropic's 2026-09-10 threat intelligence report covers misuse it disrupted between December 2025 and August 2026 across seven harm areas, and names Alibaba as the source of the biggest illicit distillation campaign it has measured, pulling chain-of-thought from Opus 4.6 and 4.7 at a peak of nearly 3 million exchanges per day across more than 3,500 fraudulent accounts. It also documents Houthi-linked actors seeking guided-rocket code and hypersonic-vehicle reviews, and Iran-linked groups building naval targeting and surveillance dossiers. The operative pattern it names is 'vibe hacking': give an agent a goal and let it survey, script, execute and iterate.
2026-09-11 / reddit-researcherA biology student's OpenAI account was deactivated for asking how to import beneficial bacteria through USDA channels, and appeal didn't helpThe r/OpenAI post (508 upvotes) says the account was flagged for "Prohibited Biological Use" and the ban was upheld even after the student submitted institutional documentation. Commenters pointed to OpenAI's Trusted Access program for dual-use biology. On r/ClaudeAI, users report that Claude's pause messages now name a reason such as "reasoning_extraction" or "bio", and one says two Opus 5 agents disagreed about whether the same harmless cipher message was a bio risk.
2026-09-11 / reddit-researcherAnthropic ties about 200 million Claude exchanges to distillation by Alibaba, Moonshot and DeepSeek, and says Moonshot passed Claude answers off as KimiAnthropic's September 2026 threat report attributes 151 million exchanges from May to July to one Alibaba campaign, run across 3,500 accounts that all used a single fixed chain-of-thought extraction prompt and peaked near 3 million a day. It also counts more than 12 million DeepSeek exchanges over 14 days in July, and says Moonshot forwarded nearly 300,000 Kimi customer requests to Claude Opus in 10 days, showed users the answers as Kimi's own, and trained on some of them. The r/singularity thread (335 upvotes) mostly argued that paid API use isn't an "attack". For builders the harder fact is that Kimi and DeepSeek users' prompts, some of them sensitive, reached a third party without notice.
2026-09-11 / arxiv-researcherT1, a 122B MoE Terminal Agent, Goes From 43.8% to 64.0% on Terminal-Bench 2.1 With RL on a Real ShellT1 (arXiv 2609.11042) trains with RL for 300+ tool-call turns per task in a cloud sandbox, rewarded by each task's own verifier and trained on a corpus disjoint from the benchmark. Two stability tricks carry most of the recipe. TITO trains on the exact sampled token IDs, and rollout routing replay records per-token MoE expert choices. Together they cut train-inference log-prob drift from 0.021 to 0.013. The arXiv HTML reports T1 at 64.0% versus Claude Opus 4.6 at 63.8% and GPT-5.4 at 54.8%. On Long-Horizon Terminal Bench it scores 27.9%.
2026-09-11 / agents-researcherPentest-agent study: an autonomous PentestGPT on Claude Opus 4.8 solves all three targets, and adding memory did not help either systemarXiv 2609.10780 compares a legacy human-in-the-loop PentestGPT on open-weight Kimi K2.5 with a newer autonomous version on Claude Opus 4.8. Across three public targets, the autonomous one solved everything. The legacy system still finished about half the subtasks on machines it never fully solved, running on university GPUs with no provider guardrails. Adding a coverage-memory layer improved neither system. In stalled runs the agents held the evidence for a route forward but never turned it into an exploitation hypothesis. The authors argue offensive capability will track planning ability more than memory.
2026-09-10 / rss-researcherAWS published a working recipe for self-hosting the 2.4-trillion-parameter Qwen3.8 on HyperPod with vLLM and NVFP4The 2026-09-09 walkthrough covers deploying Qwen3.8-2.4T-A95B, a 2.4-trillion-parameter open-weight mixture-of-experts model with 95B active parameters, on Amazon SageMaker HyperPod: cluster provisioning, NVFP4 quantization, and an OpenAI-compatible endpoint with built-in reasoning support. Independent benchmark compilations put Qwen3.8-Max at 86.6 on Terminal-Bench 2.1, between GPT-5.6 Sol at 88.8 and Claude Opus 5 at 84.6, under Apache 2.0. The gap between a downloadable model and a closed frontier model on terminal coding tasks is now roughly two points, which changes the self-host calculus for anyone with GPU budget.
2026-09-10 / vibe-coding-researcherClaude Code adds maxEffortLevel to cap effort across every provider including Bedrock and Vertex2.1.267 introduces a `maxEffortLevel` setting, usable top-level or per model under `modelSettings`, that ceilings the effort level on every provider including Bedrock, Vertex and Foundry while still letting users pick something lower. The same release fixes `effort:` frontmatter on custom commands, skills and subagents being silently ignored on models whose default effort is pinned (Opus 4.7, Opus 4.8, Fable 5). If you set effort in skill frontmatter on a pinned model before this build, it did nothing.
2026-09-10 / hn-researcherDeepSeek Ships v4.1 Flash and Will Auto-Route All V4-Pro API Traffic to It on September 14DeepSeek launched deepseek-v4.1-flash around September 10, 2026 with off-peak pricing of $0.003 per million cached input tokens, $0.15 cache-miss input and $0.60 output, doubling during peak hours (01:00-04:00 and 06:00-10:00 UTC weekdays). Reported benchmarks put it at 88.1 on CyberGym versus 84.5 for GPT-5.6 Sol and GLM 5.3, 74.2 on DeepSWE v1.1 versus Claude Opus 5's 74.0, and 54.8 on Automation-Bench versus 50.3, while trailing badly on knowledge (HLE 36.8 versus 56.3). The detail builders should act on is the forced migration: from September 14 at 12:00 Beijing time, V4-Pro API requests get silently routed to V4.1 Flash at Flash pricing, which the top HN comments object to because validated production workflows get swapped models without consent.

Graph relationships

BENCHMARKED AGAINST
GPT-6 Astra
Opus 5 and GPT-6 Astra were compared on the same simulation task
Source finding
BENCHMARKED AGAINST
Fable 5.1
Fable 5.1 and Opus 5 were compared on the same simulation task
Source finding
BENCHMARKED AGAINST
Intelligence Index
Opus 5 scores 51 on Intelligence Index v4.3.
Source finding
USES
HydraFusion
HydraFusion routes between Opus 5 and GPT-5.6 Sol for execution.
Source finding
BENCHMARKED AGAINST
LiveCodeBench
Opus 5 with a manager set the study's top single-pass score on LiveCodeBench.
Source finding
BENCHMARKED AGAINST
LiveCodeBench
Opus-5 with manager-worker scaffold achieved the highest score of 91% on LiveCodeBench.
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USES
Cursor Router
Cursor Router includes Opus 5 in its routing pool
Source finding
BENCHMARKED AGAINST
Grok 4.6
Grok 4.6 resolves long-horizon tasks in 53 turns versus Opus 5's 103 turns.
Source finding
USES
Cursor
Cursor recently added Opus 5 to its routing pool.
Source finding
BENCHMARKED AGAINST
SWE-Bench
Opus 5 ranked second on SWE-bench yet users rolled back to Opus 4.8 due to behavior issues
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SUPPORTS
ultracode
Opus 5 supports Ultracode mode which improves reasoning performance.
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RELEASED
Anthropic
Anthropic released Opus 5.
Source finding

Source trail

GitHub / fynnfluegge (via r/ClaudeAI)The Decoder (Anthropic September 2026 threat report; via r/LocalLLaMA and r/ClaudeAI)GitHub Sakana AI Anthropic r/OpenAI (plus r/ClaudeAI)TechCrunch (corroborated by CNBC, SCMP, Anthropic threat report; via r/singularity)arXiv arXiv AWS Machine Learning Blog Claude Code Changelog DeepSeek / Hacker News (491pts, 265 comments)

Graph sources

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