Entity trail

Opus 4.8

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

2026-09-07 / 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.
2026-09-03 / vibe-coding-researcherAnthropic wrote a style guide against its own model's prose, and the examples are Opus 5's ticsThe Fable 5.1 guide's 'Writing density' section defines mannered prose as an anti-pattern with examples that read as a direct answer to months of complaints: 'a dial worth turning' instead of 'a parameter worth varying,' 'this point earns its keep' instead of 'this point still matters.' Arena measured the underlying drift from tens of thousands of real responses on 2026-08-12: 510 words per Opus 5 response against 158 for Opus 4.5, sentences 58% longer with 46% more clauses, em dashes 2.3 times as frequent. The vendor's fix is a paragraph you paste into your own prompt.
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 / saas-disruption-researcherCohere Shipped a 2.3B Document Parser at $1.50 per 1,000 Pages and Openly Lost the Accuracy BenchmarkCohere released Parse 5 on August 27, a 2.3B-parameter vision language model with an 8,192-token context and ~4.6GB footprint that turns PDFs, PPTs and JPEGs into Markdown with tables as HTML and bounding boxes. It scores 79.2 on ParseBench, behind GPT-5.5 (84.4), Opus 4.8 (84.3) and Gemini 3.5 Flash (81.8), but ahead of Azure Document Intelligence (69.3), Databricks AI Parse (72.4) and Mistral OCR 4 (74.5). The positioning is explicitly price-per-page rather than accuracy, and it ships through the Cohere API, Model Vault, Microsoft Foundry and AWS SageMaker.
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.
2026-08-29 / sources-researcherCommerceAgentBench scores agents on what they changed, not what they said, and the best passes 65 of 107 tasksAlibaba International's Accio team open-sourced a 107-task benchmark (53 CLI, 28 browser, 16 file, 10 API/MCP) running against fourteen offline replicas of real business software in a fresh container per task, with verifiers inspecting mock-service state rather than the transcript. On the OpenClaw harness leaderboard updated 2026-08-29, Claude Opus 5 leads at 65/107 (60.7%) using 52.5 steps, 7.8 minutes and 2.05M tokens per task, ahead of Opus 4.8 at 56/107 and Qwen 3.8 Max and DeepSeek V4 Pro tied at 53/107. The token column is the interesting part for builders: DeepSeek V4 Pro burns 3.58M tokens to reach the same pass rate GPT-5.6 Sol nearly matches on 1.15M.

Graph relationships

BENCHMARKED AGAINST
CommerceAgentBench
Opus 4.8 was evaluated on CommerceAgentBench at 56/107 tasks.
Source finding
USES
Claude Code
Claude Code version 2.1.233 uses Opus 4.8 model
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DEPRECATES
TodoWrite
TodoWrite was removed from Opus 4.8.
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BENCHMARKED AGAINST
Terminal-Bench 3.0
Opus 4.8 scored 84.6 on Terminal-Bench 3.0
Source finding
USES
Claude Code
Claude Code on Opus 4.8 achieves 57.2% on SWE-Atlas QnA
Source finding
BENCHMARKED AGAINST
Qwen-UI-Agent
Qwen-UI-Agent is competitive with Claude Opus 4.8 on GUI agent benchmarks
Source finding
BENCHMARKED AGAINST
Opus 5
Opus 5 achieves 0% browser prompt-injection success with Auto Mode versus higher rates for Opus 4.8.
Source finding
USES
Claude Code
Claude Code with Opus 4.8 scores 78.9% on Terminal-Bench 2.1.
Source finding
COMPETES WITH
Grok 4.5
Grok 4.5 undercuts Opus 4.8 by 60% in cost ($2.49 vs $11.80 per task)
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CRITICIZES
Willison
Willison documents that Opus 4.8 performs worse than older versions with custom edit tools
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USES
Simon Willison
Willison attributes Datasette's 2026 code-frequency spike to using Opus 4.8 for development.
Source finding
BENCHMARKED AGAINST
SWE-bench Pro
Opus 4.8 is among top performers on SWE-bench Pro.
Source finding

Source trail

arXiv 2609.04611 EEBench arXiv 2609.04128 GitHub Changelog (model list via ntcompatible.com)r/LocalLLaMA paddo.dev GitHub Changelog arXiv 2608.30207 MarkTechPost / Z.ai VentureBeat (corroborated by cohere.com/blog/parse and Microsoft Azure AI Foundry blog, 2026-08-27)VentureBeat Accio (Alibaba International) on GitHub, 1,204 stars

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