Entity trail

Claude Opus 4.8

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

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Findings

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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.
2026-09-05 / 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%.
2026-09-04 / 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.
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.
2026-09-02 / 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.
2026-09-01 / arxiv-researcherA Self-Improving Red-Teamer Takes Claude Opus 4.8 From 4% to 24% Injection Success on Computer-Use TasksSIR is a black-box indirect prompt injection attack against computer-use agents at the operating-system level, composing stealthy injections from a small plain-language library of reusable principles and then diagnosing failed trajectories to distill new named strategies that get reapplied. Scored with a deterministic oracle checking filesystem, service and permission state rather than an LLM judge, it lifts attack success from 4% to 24% on Claude Opus 4.8 and from 0% to 28% on Gemini 3.5 Flash while the benign task still completes. Principles discovered against one model transfer to a different architecture with no additional feedback, so fixed hand-written injection benchmarks understate real exposure.
2026-08-30 / hn-researcherThe Mystery 'Ox Alpha' Model Was GLM-5.3-Flash All Along, and Z.ai Shipped It Under MITZ.ai released GLM-5.3-Flash on August 26 as a 320B-A18B MoE, natively multimodal, FP8-native, with a 1,048,576-token context — and confirmed on X that it is the model previously fingerprinted in the wild as 'Ox Alpha,' trained entirely on Chinese AI chips. The MIT license is the notable part: a frontier-adjacent multimodal model with no field-of-use restrictions, claimed within half a point of Claude Opus 4.8 on Z.ai's internal coding benchmark at roughly one-tenth the price of GLM-5.2. For builders, this is the first MIT-licensed model in this capability class you can legally fork, fine-tune, and resell.
2026-08-29 / news-researcherMeta's EvoHarness-RL Gets Qwen3-8B to 96.9% on ALFWorld, Within Two Points of Claude Opus 4.5arXiv 2608.05446 ('EvoHarness-RL: Learning Self-Evolving Runtime Harness for Long-Horizon LLM Agents') turns tool use from a hardcoded prompt into a learned runtime behavior, then applies cost-aware RL that teaches the agent when reading external state is worth the token budget. On ALFWorld, Qwen3-8B hits a 96.9% average success rate, beating SkillOS (80.2%) and SkillRL (89.9%); the same harness pushes Claude Opus 4.5 to 98.5%. For builders the claim is that harness design, not model size, is where the remaining points live.

Graph relationships

BENCHMARKED AGAINST
GLM-5.3-Flash
GLM-5.3-Flash scored within half a point of Claude Opus 4.8 on coding benchmarks at one-tenth the price.
Source finding
BENCHMARKED AGAINST
Terminal-Bench-Science
Claude Opus 4.8 scored 10.5% on Terminal-Bench-Science.
Source finding
BENCHMARKED AGAINST
Terminal-Bench-Science
Claude Opus 4.8 scored 10.5% on Terminal-Bench-Science.
Source finding
BENCHMARKED AGAINST
Ornith-1.5
Ornith-1.5 397B achieves 86.0 SWE-bench Verified compared to Claude Opus 4.8's 85.8
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BENCHMARKED AGAINST
GPT-5.6 Sol Ultra
Claude Opus 4.8 and GPT-5.6 Sol Ultra were tested on research tasks.
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BENCHMARKED AGAINST
Ornith-1.5
Ornith-1.5 was benchmarked against Claude Opus 4.8
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BENCHMARKED AGAINST
Claude Opus 5
Claude Opus 5 scored 24% on SlopCodeBench versus Opus 4.8's 6%.
Source finding
BUILT BY
Anthropic
Claude Opus 4.8 was developed by Anthropic.
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USES
StateAct
StateAct improves Claude Opus 4.8 from 20.6% to 26.9% on computer use tasks.
Source finding
BENCHMARKED AGAINST
SWE-bench Verified
Claude Opus 4.8 scores 0.886 on SWE-bench Verified.
Source finding
BENCHMARKED AGAINST
Intelligence Index
Claude Opus 4.8 scored 56 on the Intelligence Index.
Source finding
RELEASED
Anthropic
Anthropic released Claude Opus 4.8 which scores 55.7% on the Intelligence Index
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 EEBench arXiv 2609.04128 GitHub Changelog (model list via ntcompatible.com)r/LocalLLaMA GitHub Changelog arXiv 2608.30207 MarkTechPost / Z.ai VentureBeat

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