Entity trail

Claude 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

2026-09-08 / agents-researcherHanding an agent a 128 MB and 10-second execution contract made generated code up to 3.1x faster and cut peak memory in 13 of 14 comparisonsThe paper names 'substrate blindness' as the absence of execution context from an agent's planning state, and tests it by having Claude Opus 5, GPT-5.6-Sol and Gemini 3.7 Flash generate a high-dimensional pairwise Euclidean distance routine either from the task alone or with an explicit 128 MB RAM and 10.0 s wall-time contract. Disclosure reduced peak process memory in 13 of 14 executable index-aligned comparisons and mean wall time in all three cohorts, up to 3.1x faster. The mechanism is structural, not cosmetic: contract-aware runs adopted bounded blocking, float32 retention, upper-triangle traversal and in-place or memory-mapped buffers.
2026-09-08 / reddit-researcherArtificial Analysis shipped Intelligence Index v4.3 a week after v4.2, swapping two benchmarks and raising the private-test weightingVersion 4.3 replaces Terminal-Bench v2.1 with v4.0 and drops τ³-Banking for AutomationBench-AA, a business-workflow benchmark with a private test set, and lifts private-set weighting from 40% to 45%. Claude Fable 5.1 and GPT-6 Astra tie at 53, with Opus 5 at 51, and Astra costs 57% less per task ($3.26 vs $7.63). One commenter who logs the index hourly noted from git history that 4.3 restores test scores and pricing for older models like Llama 4 that 4.2 had dropped, arguing 4.2 was the rushed reaction and 4.3 the planned upgrade.
2026-09-08 / rss-researcherLatent Space measured what seven frontier models actually recommend across 161 categories, and each one shills its own ecosystemThe Frontier AEO Tracker ran 6 prompt variations across 7 models (Claude Opus and Fable, GPT Sol and Astra, Grok, Muse, SWE-1.7) over 161 product categories, from coding agents to databases to angel investors, with every prompt-answer pair left publicly inspectable. Models heavily favour their own ecosystem, 28 of the 161 categories return a unanimous first choice across all models, and source-consumption differs sharply: Astra medians 5 sources per query, Sol 9, Opus 11, Fable 15. Astra is also markedly harder to move, changing its answer far less when the question is lightly paraphrased, and the team found markdown content negotiation and formatting measurably affect which sources get cited.
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 / reddit-researcherClaude Max x5 users report burning a weekly reset in one 54-minute session, and the switching threads name limits, not qualityA Max x5 user posted a /usage screenshot after a single 54-minute Opus 5 max-effort session at ~160k tokens consumed a fresh weekly reset, and the mod-bot summary after 100 comments records Pro, Max x5 and Max x20 users all confirming burn rates that feel about 3x faster than a week earlier. The 442-upvote 'I thought I will never say this about Fable' thread's own 200-comment summary lands on the same point: the reason people are downgrading is limits and cost, not that Astra is smarter. A separate 121-upvote panic about a '$321 total cost' in /usage was resolved by the community as API-equivalent value on a subsidised plan, not a bill, which is worth knowing before anyone else reads that field.
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 / reddit-researcherAstra opens with a 350-point lead on VoxelBench, where Fable 5 sits behind Opus 5 and SolA 186-upvote r/singularity post shows GPT-6 Astra debuting on VoxelBench with a roughly 350-point margin, and the poster clarifies in the comments that Fable 5 is absent from the chart because it ranks below Opus 5 and Sol on this task. A companion thread compares Astra and Fable 5 voxel outputs directly. VoxelBench and MineBench measure spatial construction, which is a narrow slice, but they are among the few public boards where release-day model differences are visible without reading a score table.
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 / arxiv-researcherEvolving Environments Off-Policy Beats Co-Evolution and Adds 14.4-18.0 Points on Terminal-Bench 2.1Environments synthesized from scratch stop challenging frontier models, and recent co-evolution methods that generate environments near the model's learnable frontier depend on on-policy rollouts, which limits generalization and the continuous supply of learning signal as the model improves. This work derives three evolution directions from the multi-turn learning objective, then increases environment difficulty off-policy generation by generation through a loop-engineered multi-agent harness. Rollout experiments with Hy4 preview, Claude Opus 5 and GPT-5.6 Sol confirm the evolved environments are consistently harder, and simple long-horizon RL on Qwen3.6-27B and Qwen3.6-35B-A3B gains 14.4 and 18.0 points on Terminal-Bench 2.1.

Graph relationships

BENCHMARKED AGAINST
Gemini 3.7 Flash
Claude Opus 5 was compared with Gemini 3.7 Flash on high-dimensional distance code generation with memory and time constraints
Source finding
BENCHMARKED AGAINST
GPT-5.6 Sol
Claude Opus 5 and GPT-5.6-Sol both improved from 0-1/5 to 4-5/5 correct budget-constrained code generation with explicit contract
Source finding
BENCHMARKED AGAINST
Claude Fable 5
Claude Fable 5 ranks below Claude Opus 5 on VoxelBench spatial construction tasks.
Source finding
BENCHMARKED AGAINST
EEBench
Claude Opus 5 scores 61.6% on EEBench
Source finding
BENCHMARKED AGAINST
Qwen3.8-Max-0902
Qwen3.8-Max-0902 is compared to Claude Opus 5 on coding benchmarks.
Source finding
BENCHMARKED AGAINST
Terminal-Bench-Science
Claude Opus 5 led at 30.0% on Terminal-Bench-Science.
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BENCHMARKED AGAINST
CommerceAgentBench
Claude Opus 5 was evaluated on CommerceAgentBench, leading at 65/107 tasks.
Source finding
BENCHMARKED AGAINST
Terminal-Bench-Science
Claude Opus 5 scored 30.0% on Terminal-Bench-Science.
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USES
Recuris
Recuris improves success on Claude Opus 5 by 15.6 points.
Source finding
RELEASED
Anthropic
Claude Opus 5 cost $2.34 per task at max effort
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RELEASED
Anthropic
Claude Opus 5 resisted the same jailbreak techniques that worked on Opus 4.6.
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RELEASED
Anthropic
Anthropic released Claude Opus 5.
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Source trail

arXiv 2609.05232 — Substrate-Aware AI Agents: Execution Context as a First-Class Input Artificial Analysis (via r/singularity, 159 upvotes)Latent Space arXiv 2609.04611 r/ClaudeAI (172 and 442 upvotes)GitHub Blog EEBench r/singularity CodeRabbit arXiv 2609.04128 GitHub Changelog (model list via ntcompatible.com)r/LocalLLaMA

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