Entity trail

Opus 4.5

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

2026-09-11 / 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 / 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.
2026-09-09 / skill-finderAgents hit 80% F1 deciding whether a dependency CVE is exploitable, but fall below 70% explaining whyVEX-Bench is the first benchmark for the actual supply-chain security question, whether a known upstream vulnerability is reachable in a downstream project, as distinct from prior zero-day discovery benchmarks. It contains 75 real-world cases mined from GitHub and labeled by security experts across Python, Java and Go, evaluated over nine models and three agent harnesses. GPT-5.5 and Claude Opus 4.6 reach roughly 80% F1 on binary vulnerability-status classification, but only GPT-5.5 clears 70% macro-F1 on fine-grained justification, meaning an agent can tell you a Dependabot alert is a false positive far more reliably than it can tell you the correct reason to record in a VEX document.
2026-09-07 / arxiv-researcherτ^τ-bench Makes Agent Construction the Task: Claude Opus 5 Under Claude Code Passes 23.9% Against an 82.2% Expert Ceilingτ^τ-bench (arXiv 2609.04611) gives a developer agent a real client engagement setup — business records, a requirements-holding client, a production API, an inherited codebase, and serving cost/model limits — and scores it by deploying the customer-service agent it builds against held-out simulated users. Across 53 tasks in four domains, the strongest configuration (Claude Opus 5 under Claude Code) passes 23.9% of evaluation simulations versus an expert-authored reference ceiling of 82.2%. The failure modes are the human ones: shallow queries instead of deep record comprehension, almost no communication back to the client, and too little experimentation with agent architecture and serving setup.
2026-09-07 / skill-finderTelling the model its RAM and wall-time budget takes correct-and-in-budget code generation from 0/5 to 4/5Claude Opus 5, GPT-5.6-Sol and Gemini 3.7 Flash generated code for a high-dimensional pairwise distance task either from the task alone or with an explicit 128 MB RAM and 10.0 s wall-time contract in the prompt. Contract disclosure cut peak process memory in 13 of 14 index-aligned comparisons and cut mean wall time in all three cohorts, making execution up to 3.1x faster; at a tighter 96 MB contract, correct-and-within-budget outcomes went from 0/5, 1/5 and 0/5 to 4/5, 5/5 and 3/5. The models responded structurally, not cosmetically, adopting bounded blocking, float32 retention, upper-triangle traversal and memory-mapped buffers, which makes a one-line execution contract one of the cheapest prompt additions available.
2026-09-06 / hn-researcherGitHub Ships HydraFusion, a Copilot CLI Mode That Routes Between Opus 5 and GPT-5.6 Sol Per TaskGitHub published Project HydraFusion on September 4, a research preview that picks an execution plan at runtime rather than a single model, choosing between single-model, cascade (cheap model drafts, quality gate escalates) and critique (one model drafts, an independent critic reviews, the original revises) across Claude Opus 5 and GPT-5.6 Sol. Against Opus 5 alone it reports 67% cost reduction with +4.9 quality on TerminalBench 2.1, 36% cost reduction with -1.5 quality on DeepSWE, and 65% cost reduction with -0.1 quality on CheckpointBench. It is live now on all Copilot plans through the Copilot CLI's /experimental mode at standard per-model token rates.
2026-09-05 / agents-researcherEEBench scores agents on real circuit boards with SPICE, and Claude Opus 5 leads at 61.6% while GPT-5.6 Sol trails at 39.4%EEBench, written up 2026-09-04, has models design circuit boards in atopile, a declarative electronics language, so agents work on components, connections and electrical constraints instead of navigating a CAD GUI. Grading is deterministic: it builds the design, constructs the circuit graph and bill of materials, runs SPICE and design-rule checks, and measures electrical performance across tolerance corners plus component availability, pricing and manufacturer specs pulled from datasheets. On the 2026-09-01 board Claude Opus 5 leads at 61.6%, followed by Grok 4.6 at 57.1%, Claude Fable 5.1 at 56.4%, Fable 5 at 54.3% and Opus 4.8 Max at 51.4%, with GPT-5.5 at 42.3% and GPT-5.6 Sol at 39.4%.
2026-09-05 / agents-researcherGitHub's HydraFusion picks a single, cascade or critique workflow per request and cuts TerminalBench cost 67% while gaining pointsGitHub published Project HydraFusion on 2026-09-04, a Copilot research preview that treats workflow selection as an optimization problem and routes each request to the least complex of three patterns: one model solving directly, a cheap model drafting with escalation to a stronger one, or a draft reviewed by a critic from a different model family before revision. Measured against Claude Opus 5 and GPT-5.6 Sol baselines, it reports 67% lower cost with +4.9 points on TerminalBench 2.1, 65% lower cost at -0.1 points on CheckpointBench, and 36% lower cost at -1.5 points on DeepSWE. The cross-family critique step is the part worth stealing: it is the reviewer coming from a different vendor that does the work, not the extra pass.
2026-09-05 / sources-researcherCodeRabbit measured GPT-6 Astra at 61.3% actionable bug coverage against Opus 5's 50.2%, at $1.50 per task versus Sol's $0.60CodeRabbit's 2026-09-04 evaluation scored models on actionable bug coverage against labeled bugs: Astra 61.3%, GPT-5.6 Sol 59.0%, Opus 5 50.2%, with the gap widening on cross-file reviews (Astra 57.1%, Sol 47.6%, Opus 5 42.9%). At a fixed 100K input / 10K output task the cost ladder is Astra $1.50, Sol $0.60, Terra $0.32, Luna $0.032, so Astra is 2.5x Sol and 47x Luna for a 4% overall coverage gain. They also flag the retention asymmetry: Astra supports zero data retention for eligible customers while Fable defaults to 30-day retention unless you are on Enterprise Frontier Safeguards.
2026-09-04 / rss-researcherGitHub is retiring four Copilot models on October 2, 2026GitHub published a changelog entry on 2026-09-03 announcing the upcoming deprecation of selected Copilot models, filed under its Retired/Deprecations category. Secondary coverage lists the four as Gemini 3.5 Flash, Gemini 3.6 Flash, Kimi K2.7 Code and Claude Opus 4.7, retiring October 2, 2026, with Gemini 3.8 Flash, Kimi K3 and Claude Opus 5 as the pushed replacements. The GitHub changelog confirms the entry and its date, but the specific model list comes from one secondary source, so check the entry itself before repinning anything.

Graph relationships

SUPPORTS
Tool Search Tool
Tool Search Tool improves Opus 4.5 tool-selection accuracy from 79.5% to 88.1%
Source finding
BENCHMARKED AGAINST
SWE-Bench
Opus 4.5 leads SWE-bench Feb 2026 at 80.9% accuracy.
Source finding
BUILT BY
Anthropic
Opus 4.5 is built by Anthropic.
Source finding
BENCHMARKED AGAINST
Tool Search Tool
Tool Search Tool improved selection accuracy from 79.5% to 88.1% on Opus 4.5
Source finding
COMPETES WITH
Opus 4.7
Opus 4.7 is a clear intelligence upgrade over Opus 4.5.
Source finding
COMPETES WITH
Gemini 3.6 Flash
Gemini 3.6 Flash competes with Anthropic's Opus 4.5 in the frontier model market.
Source finding
BENCHMARKED AGAINST
Claude Sonnet 4.6
Claude Sonnet 4.6 achieves Opus 4.5-comparable performance at Sonnet pricing
Source finding
BENCHMARKED AGAINST
GPT-4o
SemiAnalysis tracked benchmark performance gaps between Anthropic and OpenAI models across eras.
Source finding
COMPETES WITH
Haiku 4.5
Opus 4.5 agents systematically out-negotiated Haiku 4.5 in the Project Deal experiment.
Source finding
SUPPORTS
Tool Search Tool -> Opus 4.5
Tool Search Tool improves Opus 4.5 tool-selection accuracy from 79.5% to 88.1%
Source finding
BENCHMARKED AGAINST
Tool Search Tool -> Opus 4.5
Tool Search Tool improved selection accuracy from 79.5% to 88.1% on Opus 4.5
Source finding
COMPETES WITH
Opus 4.7 -> Opus 4.5
Opus 4.7 is a clear intelligence upgrade over Opus 4.5.
Source finding

Source trail

arXiv arXiv Claude Code Changelog DeepSeek / Hacker News (491pts, 265 comments)arXiv 2609.08040 — VEX-Bench: Benchmarking LLM Agents for Assessing Exploitability of Software Supply Chain Vulnerabilities arXiv 2609.04611 arXiv 2609.05232 — Substrate-Aware AI Agents: Execution Context as a First-Class Input GitHub Blog EEBench CodeRabbit GitHub Changelog (model list via ntcompatible.com)r/LocalLLaMA

Graph sources

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