Entity trail

Claude 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

2026-09-10 / 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.
2026-09-04 / 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.
2026-09-04 / reddit-researcherA model-neutral agent harness matched Claude Managed Agents' solve rate on 63% fewer tokensThe TrueForge team ran 14 DevRev Enterprise-Bench tasks three times each with a blind judge across harness/model combinations. Claude Managed Agents with Opus 4.8 solved 11/14 at $11.8 and 10.0M tokens per run; TrueForge with the same model solved 11/14 at $8.6 and 3.7M tokens, averaging 19 tool calls per task against 32. Swapping in GLM-5.2 gave 11.7/14 at $3.0 per run, roughly 75% cheaper than the managed baseline, which is the strongest published argument yet that the agent loop, not the model, is where the token bill lives.
2026-09-03 / hn-researcherAnthropic Ships Claude Fable 5.1 and a Gated Mythos 5.1 Twin, With Cache Reads Cut 75% to $0.25/MtokFable 5.1 and Mythos 5.1 are the same underlying model at two safeguard levels: Fable is generally available, Mythos goes only to vetted cybersecurity and life-sciences organizations, currently US-only. Fable 5.1 scores 52.6% on Terminal-Bench-Science 0.1 (versus 24.7% for Fable 5 and 29.0% for Opus 5), 55.8% on Terminal-Bench 4.0, 60.9% on Humanity's Last Exam without tools, and 73.4% on CursorBench 3.2.0. Token pricing holds at $10/$50 per million in/out, but cache reads drop 75% to $0.25/Mtok, which Anthropic puts at ~25% cheaper for typical workloads and up to 45% for highly agentic runs.
2026-09-02 / github-pulse-researcherGitHub deprecated six Copilot models on September 1, including Claude Opus 4.5 and 4.6 and Gemini 3.1 ProEffective 2026-09-01, GitHub retired Gemini 3.1 Pro (use Gemini 3.7 Flash), Claude Opus 4.5 and 4.6 (use Opus 4.7, 4.8 or 5), Claude Sonnet 4.5 and 4.6 (use Sonnet 5), and Raptor Mini (use MAI-Code-1.1-Flash). Sonnet 4.6 remains available to individual Copilot subscribers on annual plans. Claude Fable 5.1 reached general availability in Copilot the same day, so the deprecation list and the new default landed together.

Graph relationships

BENCHMARKED AGAINST
GraphWalker
Claude Opus 4.5 was evaluated against GraphWalker for model-based test path generation.
Source finding
BENCHMARKED AGAINST
ALFWorld
Claude Opus 4.5 achieved 98.5% success rate on ALFWorld with EvoHarness-RL.
Source finding
BENCHMARKED AGAINST
CTI-REALM
Claude Opus 4.5 scores 0.624 on CTI-REALM
Source finding
BENCHMARKED AGAINST
CUDA Agent
CUDA Agent beats Claude Opus 4.5 by 40% on GPU kernels
Source finding
BENCHMARKED AGAINST
CUDA Agent
CUDA Agent outperforms Claude Opus 4.5 by 40% on hardest kernel generation tasks.
Source finding
BENCHMARKED AGAINST
SWE-bench Verified
Claude Opus 4.5 achieves 80.9% on SWE-Bench Verified coding tasks.
Source finding
BENCHMARKED AGAINST
APEX-Agents
Claude Opus 4.5 fails 75% of complex workplace tasks on APEX-Agents benchmark.
Source finding
BENCHMARKED AGAINST
EnterpriseOps-Gym
Claude Opus 4.5 achieves 37.4% task success on EnterpriseOps-Gym.
Source finding
BENCHMARKED AGAINST
SWE-bench Verified
Claude Opus 4.5 scored 80.9% on SWE-Bench Verified.
Source finding
BENCHMARKED AGAINST
SWE-bench Pro
Claude Opus 4.5 scores 45.9% on SWE-bench Pro under SEAL standardized scaffolding.
Source finding
BENCHMARKED AGAINST
Claude Opus 4.7
Opus 4.7 scores lower than Opus 4.5 on SimpleBench
Source finding
BUILT BY
Anthropic
Claude Opus 4.5 agents closed more deals in Anthropic's Project Deal marketplace experiment.
Source finding

Source trail

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 GitHub Changelog (model list via ntcompatible.com)r/LocalLLaMA Anthropic / Hacker News GitHub Changelog CellCog (corroborating Qwen's announcement)

Graph sources

entity graphfindings textkg edgeskg entities

Fetching from the wire…