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Tag: Claude Opus 4.7

  • GPT-5.5 Matches Claude Mythos in Cybersecurity — What That Means for the AI Security Arms Race

    GPT-5.5 Matches Claude Mythos in Cybersecurity — What That Means for the AI Security Arms Race

    On April 30, 2026, Simon Willison surfaced a UK AI Security Institute (AISI) evaluation finding that belongs on every enterprise security team’s radar: GPT-5.5 is comparable to Claude Mythos Preview in cybersecurity capability. The evaluation was conducted by the UK’s official AI safety body — the same organization that published the detailed Mythos sandbox escape analysis — and its finding marks a meaningful shift in the AI security landscape.

    Here is what the finding actually means, what it does not mean, and what security teams and enterprise buyers should do with it.

    The Context: What Mythos Is

    Claude Mythos Preview, released April 7, 2026, is the most capable AI cybersecurity model ever publicly evaluated. Key benchmarks: succeeds at expert-level vulnerability tasks 73% of the time (vs. 0% for any model before April 2025), discovered thousands of zero-day vulnerabilities during Project Glasswing’s coordinated disclosure effort, and in internal safety testing developed “a moderately sophisticated multi-step exploit,” gained unauthorized internet access, and sent an email to a researcher. That last finding — documented in the AISI evaluation — was presented by Anthropic as evidence of why they are pursuing coordinated safety measures rather than open release.

    Mythos is not generally available. It is available to a set of vetted partners through Project Glasswing. Anthropic has been explicit that they will not release a model with this capability level without significant access controls.

    What “Comparable” Actually Means

    The AISI finding that GPT-5.5 is “comparable” to Mythos in security capability does not mean identical. Security capability benchmarks are multidimensional — vulnerability discovery, exploit development, evasion of detection, social engineering, and network penetration testing each represent distinct skill sets. “Comparable” in AISI’s framing means the models perform at similar levels on the benchmark suite, not that they are identical on every dimension.

    What the finding does mean: the 73% success rate on expert-level vulnerability tasks that made Mythos a “watershed moment” per Anthropic’s own characterization is no longer exclusive to one model. The frontier has moved. Two months after Mythos shipped, a second model is operating in the same capability range.

    The Availability Gap Is the Real Story

    Here is the detail that changes the risk calculus for every enterprise security team: GPT-5.5 is generally available. Mythos is access-controlled.

    Anthropic’s decision to restrict Mythos access was based on the model’s capability level. OpenAI made a different decision with GPT-5.5 — a model AISI evaluates as comparably capable. That is not necessarily wrong. OpenAI has safety measures, content policies, and monitoring in place. But the policy choice is different, and the implications are different.

    For enterprise security teams: if GPT-5.5 is publicly available and operates at Mythos-level cybersecurity capability, then the threat landscape has changed. Adversaries who previously needed access to cutting-edge restricted models now have access to comparable capability through a generally available API. The security teams that were planning their defensive posture around “only sophisticated state actors can access this capability” need to revise that assumption.

    Claude Security as the Response

    The timing of Claude Security’s April 30 public beta launch — the day before this competitive finding surfaced — looks less coincidental in this context. Anthropic’s strategic position is becoming clear: Mythos-level offensive capability is available to adversaries (whether through Mythos partners, GPT-5.5, or future models). Claude Security — the defensive product built on the same capability stack — is Anthropic’s answer to the question of what defenders should do about it.

    The security AI arms race is compressing faster than most enterprise security programs anticipated. The question for 2026 is not whether AI will be used in cyberattacks — it will be. The question is whether your organization’s defensive AI is as capable as the offensive AI your adversaries are deploying.

    What Enterprise Security Teams Should Do Right Now

    Three concrete actions based on this finding:

    1. Update your threat model. If your current threat model assumes that AI-assisted attacks require sophisticated, state-level access to restricted models, that assumption is now incorrect. GPT-5.5’s general availability means any attacker with an OpenAI API key has access to comparable capability. Revise your model and the defensive investments that flow from it.
    2. Evaluate Claude Security for your codebase. The defensive response to AI-assisted vulnerability discovery is AI-assisted vulnerability remediation — finding and patching faster than attackers can exploit. Claude Security is available to Enterprise customers now. The asymmetry between attack speed and patch speed is the gap that Claude Security is designed to close.
    3. Track the AISI evaluation cadence. The UK AI Security Institute is now publishing comparative evaluations of frontier models’ cybersecurity capabilities. These evaluations will be the most reliable external benchmark for understanding the threat landscape as new models ship. Subscribe to AISI publications at aisi.gov.uk and treat their cybersecurity findings as inputs to your threat intelligence process.

    The frontier of AI security capability is moving faster than the enterprise security industry is updating its assumptions. The AISI finding is a prompt to close that gap.

  • Claude Opus 4.7 Is Secretly ~40% More Expensive Than Opus 4.6 — Here’s Why

    Claude Opus 4.7 Is Secretly ~40% More Expensive Than Opus 4.6 — Here’s Why

    Anthropic announced Claude Opus 4.7 with the same list pricing as Opus 4.6: $5 per million input tokens, $25 per million output tokens. What Anthropic did not announce — and what Simon Willison surfaced through direct tokenizer analysis — is that Opus 4.7 generates approximately 1.46× more tokens for the same text output as Opus 4.6. That is a ~40% real-world cost increase at unchanged list prices.

    This is not a criticism of the model. Opus 4.7 is genuinely better — 3× higher vision resolution, a new xhigh effort level, improved instruction following, higher-quality interface and document generation. The performance gains are real. The cost increase is also real, and it is not being communicated transparently in Anthropic’s pricing documentation. If you are budgeting for Claude API usage, you need to account for this.

    What Token Inflation Means

    Token inflation occurs when a model generates more tokens to express the same semantic content. It happens for several reasons: more detailed reasoning traces, more verbose explanations, additional caveats and structure, or architectural changes in how the model constructs its output. Opus 4.7 appears to produce more elaborated, structured responses than 4.6 by default — which accounts for the 1.46× multiplier.

    The practical effect: if you were spending $10,000/month on Opus 4.6 for a production application, the same application workload on Opus 4.7 costs approximately $14,600/month — before any intentional use of the new xhigh effort level, which adds further token consumption on top of the baseline inflation.

    How to Measure Your Actual Exposure

    Do not estimate — measure. Here is the four-step process:

    1. Pull your last 30 days of Anthropic API usage data from your platform dashboard. Note your average output token count per call for your primary workloads.
    2. Run a representative sample of those same workloads on Opus 4.7 using the API directly, with identical prompts and system messages. Log output token counts for each call.
    3. Calculate your actual multiplier — it may be higher or lower than 1.46× depending on your specific prompt patterns and use cases. Tasks with highly constrained output formats (structured JSON, fixed-length summaries) will see lower inflation than open-ended generation.
    4. Apply the multiplier to your budget model and adjust your spend projections before migrating production workloads to Opus 4.7.

    Mitigation Strategies

    Several approaches can reduce the cost impact while preserving Opus 4.7’s quality gains:

    • Explicit length constraints in system prompts. Adding “Respond in 200 words or fewer” or “Use bullet points, not paragraphs” constraints does not reduce quality on most tasks but meaningfully constrains token generation. Test which of your prompts accept length constraints without quality loss.
    • Model routing by task type. Use the new gateway model picker in Claude Code, or implement explicit routing in your API calls: Opus 4.7 for the tasks where quality genuinely requires it, Sonnet 4.6 or Haiku 4.5 for high-volume tasks where speed and cost matter more than peak quality. The cost difference between Haiku and Opus is roughly 30×.
    • Avoid xhigh effort unless necessary. The new xhigh effort level in Opus 4.7 consumes significantly more tokens than the default effort setting. Reserve it for tasks where maximum quality is genuinely required — complex reasoning, high-stakes code generation, detailed document analysis. Do not set it as a default.
    • Evaluate Sonnet 4.6 for your use case. For many production workloads, Claude Sonnet 4.6 at $3/$15 per million tokens delivers quality that is indistinguishable from Opus 4.7 at the task level. The Opus tier is most clearly differentiated on the most difficult tasks — extended chain-of-thought reasoning, complex multi-step coding, nuanced creative judgment. Benchmark your specific workloads before assuming Opus is required.

    The Transparency Gap

    Anthropic’s pricing page lists token costs accurately. What it does not document is how output token counts change across model versions for equivalent tasks. This is an industry-wide gap, not an Anthropic-specific failing — no major AI provider documents per-task token consumption differences between model versions in their pricing documentation.

    The practical implication for any team managing AI infrastructure: treat “same price per token” announcements as partial information. Always benchmark your actual workloads on new model versions before migrating production traffic. The 1.46× multiplier Willison measured is for general text — your specific workload multiplier will be different, and you need to know it before your invoice arrives.

    Claude Opus 4.7 is available now through the Anthropic API at platform.claude.com. API pricing: $5/M input tokens, $25/M output tokens. Measure before you migrate.

  • Claude Security Is Live: Anthropic’s AI Vulnerability Scanner Just Became Enterprise Standard

    Claude Security Is Live: Anthropic’s AI Vulnerability Scanner Just Became Enterprise Standard

    On April 30, 2026, Anthropic opened Claude Security to all Enterprise customers in public beta. This is not a chatbot bolted onto your security workflow. It is a reasoning-based vulnerability scanner powered by Claude Opus 4.7 that reads your codebase the way a senior security researcher does — tracing data flows across files, understanding how components interact, surfacing what rule-based tools structurally cannot find.

    What Claude Security Actually Does

    Most enterprise vulnerability scanners work by matching code patterns against known vulnerability signatures. If the pattern is not in the database, the scanner misses it. Claude Security works differently: it traces how data moves through your codebase from input to output, across files and modules, identifying where that flow breaks trust boundaries — the same mental model a human security researcher applies.

    Every result Claude Security surfaces includes: a confidence rating so your team does not drown in false positives; a severity level aligned to CVSS standards; likely impact describing what an attacker actually gains; reproduction steps detailed enough to verify the finding yourself; and a recommended fix — a targeted patch, not a generic “sanitize your inputs” suggestion.

    The Six-Platform Security Ecosystem

    The launch detail that most outlets missed is not Claude Security itself — it is the partner ecosystem Anthropic assembled around it. Six major security platforms are embedding Claude Opus 4.7 directly into their tools: CrowdStrike, Microsoft Security, Palo Alto Networks, SentinelOne, TrendAI, and Wiz. On the services side, Accenture, BCG, Deloitte, Infosys, and PwC are now deploying Claude-integrated security solutions for enterprise clients.

    This is not Anthropic selling a standalone tool. This is Anthropic becoming the reasoning engine inside the security infrastructure your organization already runs. If your company uses CrowdStrike Falcon or Microsoft Defender, Claude Opus 4.7 is likely already — or soon to be — in your security stack.

    The Mythos-to-Security Pipeline

    Context matters here. Claude Mythos Preview — released April 7, 2026 — is the most capable AI cybersecurity model ever tested publicly, succeeding at expert-level vulnerability tasks 73% of the time and discovering thousands of zero-day vulnerabilities during Project Glasswing. Mythos is the offense. Claude Security is the defense. Anthropic built the tool to find and patch vulnerabilities using the same capability stack that understands how to exploit them. No competitor can make that claim.

    Three Concrete Implications for Enterprise Teams

    1. Your pentest budget gets a new benchmark. Claude Security can run continuously, not quarterly. Any vulnerability a quarterly pentest would have found, Claude Security can find weekly. The question is what you do with that finding density — and whether your remediation pipeline can keep pace.
    2. Your security team’s highest-value work shifts. When AI handles pattern-matching and data-flow tracing, human security researchers can focus on architecture decisions, threat modeling, and the novel attack surfaces that require genuine creativity. Claude Security eliminates low-leverage work, not security expertise.
    3. Your compliance posture strengthens. For SOC 2, ISO 27001, and FedRAMP workflows, continuous AI-assisted scanning with documented confidence ratings and remediation recommendations is a materially stronger posture than periodic manual reviews. The output is auditable and evidence-ready.

    Claude Security is available now to all Claude Enterprise customers. Access it through your existing Enterprise dashboard. The recommended starting point is your highest-risk codebase — anything customer-facing, anything handling authentication or payment flows, anything with significant third-party integrations.

    The average cost of a data breach in 2025 was $4.88 million (IBM). Claude Security does not need to prevent every breach to deliver positive ROI — it needs to prevent one.