AI model
Opus 5 is an Anthropic large language model in the Claude Opus line, used for coding through Claude Code and benchmarked against rival frontier models.
Pine AI's cloud computer for agents resolved 27.4% of SaaS-Bench tasks in Pine's own test, below two rivals at 31.1% and 29.2%. Its higher step score and much lower token bill come from whole systems running different models, so the computer's own contribution has not been measured.
Reality
Glow says AI coding agents asked for review screenshots put over 13,000 internal images from 300-plus organizations into public GitHub repositories. Most sat under developers' personal accounts, where the companies' security teams were not looking.
Perspective Coverage
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AppZen says its ZenLM Plus finance models led seven frontier models on five of six expense-audit controls in a test the company ran itself. Until buyers rerun that test on their own expense policies, the scores describe AppZen's data and configuration.
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Huntress ran seven Rails tasks past Claude Fable 5.1 and watched it copy hand-rolled code out of its own repository. Changing the harness, in three cheap steps, took API recall from 48 percent to 100.
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A LessWrong team fixed an eight-character murder plot before any agent spoke, then graded monitors on what they reported and what they missed. The best one fully recovered under half the rubric's facts.
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Jev returns typed answers with probability scores and no free-form text, and TypeSafe prices a decision at four hundredths of a cent. Its accuracy figures are scored against other models' probabilities.
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CommentBench splits human comments on AI-safety posts and drafts into target points, filters out the ones a model could not reach without extra context, and has Opus 5 judge which of the rest a model hit.
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Vercel's September Production Index shows the gateway's average price per token down 23.2% in August, a third straight decline, and the median heavy-usage team paying 7.6% less, so most of the saving came from switching models.
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A LessWrong write-up planted invalidating flaws in ML experiment logs and asked models to write the conference abstract. A second model scored the disclosure on three levels, and the published example is one before-and-after pair.
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Nineteen days after the same model name produced repetition loops and phantom missing files, the served checkpoint refused 59.2% of concealed-hazard tasks. That makes third-party refusal testing a running job, not a procurement step.
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Johann Rehberger got code execution in up to 80% of attempts by making one tool fail and letting the agent improvise. Anthropic says Auto Mode is working as designed, which leaves the sandbox decision on your desk.
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Aikido spent 11.7 billion tokens rediscovering 32 fresh CVEs with ten models, three attempts each. The number that should move a scanning budget is the marginal cost of the second and third pass.
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SemiAnalysis puts 40-50% of 2027's new capacity under OpenAI and Anthropic contracts, with revenue per megawatt now well clear of deployment cost. That makes inference a residual market.
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The frontier price cut concedes that buyers now price intelligence per task. The discount expires in a quarter; Thomson Reuters' own domain model does not.
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Anthropic's promotional lift to weekly limits was set to end around 19 August. The invoice does not change, which is exactly why this repricing is hard to see.
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A production test across 15 models put seven of them inside a one-point spread on pass rate. On constrained payroll work, the price premium bought speed, not correctness.
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