Claude 4.5: The AI That Just Dethroned Google

The AI Arena Just Exploded

Google’s Gemini 3 Pro barely had time to enjoy its coronation before a new challenger walked onto the stage. After just a few days of Gemini 3 Pro dominating AI Twitter threads and benchmark charts, Anthropic dropped Opus 4.5, instantly forcing a rewrite of the “who’s on top?” narrative.

Gemini 3 Pro set a brutal bar. It stunned developers with exceptional coding performance, pushed out jaw-dropping graphics through Nanaban Pro, and posted a 76.2% score on SWE-Verified, one of the most respected coding benchmarks. For a brief moment, it looked like Google had locked in the crown across reasoning, multimodal understanding, and code generation.

Opus 4.5 arrives framed as a “modest” upgrade, but at this level, modest looks monumental. On SWE-Verified, Opus 4.5 jumps to 80.9%, a sizable gap over Gemini 3 Pro’s 76.2% on a benchmark where each percentage point is painful to earn. On OS World’s computer-use benchmark, Opus 4.5 hits 66.3% versus Claude Sonnet 4.5’s 62.9%, establishing a new released-model high for actually driving a desktop environment.

Benchmarks now read like a boxing scorecard rather than a simple leaderboard. Opus 4.5 beats Gemini 3 Pro in agentic terminal coding and tool use, while slightly trailing on some “classical” exams like GPQA and MMU where Gemini and OpenAI’s latest GPT lines still trade blows. Even on long-horizon “run a business for 350 days” simulations such as Vending Bench 2, Gemini 3 Pro keeps a narrow lead—just under $5,500 in simulated profit versus just under $5,000 for Opus 4.5.

This article treats Opus 4.5 and Gemini 3 Pro as a straight head-to-head across coding, reasoning, computer use, multimodal work, and cost efficiency to see which model actually represents the state-of-the-art in late 2025. Anthropic, Google, and OpenAI now iterate so fast that “king of the hill” lasts about as long as a product keynote. For users, that arms race translates directly into cheaper tokens, smarter agents, and models that can not only write your app, but also install it, test it, and quietly run your spreadsheets while you sleep.

A New Sheriff in the World of Code

A new leaderboard quietly flipped this week on SWE-verified, one of the few coding benchmarks that actually tries to measure real software engineering instead of toy puzzles. Opus 4.5 posts an 80.9 score, clearing Gemini 3 Pro’s 76.2 by a margin big enough that it is unlikely to be noise. SWE-verified checks not just whether code compiles, but whether it passes full test suites across large, multi-file projects, so a four‑plus point gap signals more reliable end-to-end implementation.

Numbers get more tangible with the one-shot Minecraft clone Anthropic is now showing off. Opus 4.5 generated roughly 3,500 lines of code in a single pass, wiring up world generation with multiple biomes, basic crafting, and the game loop without a human stitching together partial outputs. Long-form code generation at that scale stresses everything models are bad at: keeping APIs straight, avoiding circular imports, and maintaining consistent data structures across hundreds of calls.

Anthropic also ran Opus 4.5 against a notoriously brutal internal engineering take-home exam, the kind of multi-hour assignment companies use to filter senior candidates. According to the company, Opus 4.5 outscored every human who has ever taken that test, not just on correctness but on speed and architectural quality. That result will need external replication, but it aligns with what the public coding benchmarks suggest.

Where developers will feel the shift most is in Agentic Terminal Coding. On Terminal-Bench, which measures autonomous command-line work, Opus 4.5 hits 59.3 versus Gemini 3 Pro’s 54.2, a sizeable edge when you are letting an AI run shell commands on real systems. Agentic Terminal Coding means the model plans a sequence of commands, executes them, inspects errors, and recovers without a babysitter.

For developers, that translates into safer automation of chores that used to be manual: spinning up and configuring dev environments, running and fixing migrations, tailing logs to track down regressions, or wiring cron jobs and CI scripts. Combined with its OS World lead in general computer use, Opus 4.5 starts to look less like a code autocomplete and more like a junior engineer who lives inside your terminal.

The Battle for Raw Intelligence

Raw intelligence benchmarks show a tighter fight than the coding scores suggest. On ARC-AGI-2, Anthropic says Opus 4.5 hits roughly 37–38% accuracy, more than doubling some earlier baselines and edging out Gemini 3 Pro by around 6 percentage points at similar “thinking budgets.” That result, highlighted in Anthropic’s own Claude Opus 4.5 Official Announcement, now stands as state-of-the-art for released frontier models when you care about abstract pattern discovery rather than trivia recall.

ARC-AGI-2 stresses compositional reasoning on weird, synthetic puzzles that resist memorization. When Anthropic cranks up the context used for internal “thinking” from 0 to 64K tokens, Opus 4.5’s intelligence curve climbs faster than rivals, delivering top-left performance on the cost-versus-score plots. Gemini’s unreleased Deep Think variant still posts higher raw scores, but Opus 4.5 manages its gains with far less token waste and at lower cost per task.

General-knowledge and exam-style benchmarks tell a more nuanced story. On GPQA and MMU-style “humanity’s last exam” suites, Opus 4.5 only slightly trails Gemini 3 Pro and, on some subtests, GPT 5.1. Gemini continues to look strong on long-form academic QA, dense reading comprehension, and multimodal questions that mix diagrams, charts, and text.

Computer use is where Opus 4.5 plants a clear flag. On the OS World benchmark, which measures end-to-end success at real GUI tasks—installing apps, tweaking settings, navigating file systems—Opus 4.5 hits a 66.3% success rate. That result beats the previous champion, Claude Sonnet 4.5 at 62.9%, and sets a new high-water mark for released frontier models that actually drive a desktop, not just talk about one.

No lab owns every leaderboard. Opus 4.5 leads on ARC-AGI-2, OS World, SWE-Verified, and several agentic terminal and tool-use tests, while Gemini 3 Pro or GPT models still edge ahead on certain exams, multimodal tasks, and business-agent benchmarks. Yet the pattern is clear: Opus 4.5’s jump in reasoning and computer-use competence matters more than any single win, because it translates directly into agents that can think longer, act more reliably, and stay on task in messy real-world workflows. For more information, see Claude Opus 4.5 vs. ChatGPT 5.1 vs. Google Gemini 3 Pro - Technical Comparison.

Running a Business for 350 Days

Vending Bench has quietly become one of the most revealing stress tests for modern AI: a simulated vending machine business that runs for 300–350 in-game days and demands long-horizon planning, inventory strategy, and basic financial sense. Instead of solving static puzzles, models must research products, infer customer demand, manage cash flow, and keep the machine stocked without drifting off into nonsense.

On Vending Bench 2, Gemini 3 Pro still holds the crown. It finishes just under $5,500 in profit, starting from $500 in seed capital, after nearly a year of simulated operations. That margin matters because every dollar on this benchmark comes from dozens of tiny decisions: which snacks to buy, how aggressively to restock, when to pivot away from underperforming products.

Opus 4.5 does not take first place here, but its jump is hard to ignore. The model ends around $4,967 in profit, almost 10x growth on the initial $500 and a substantial leap over Claude Sonnet 4.5’s roughly $3,800 result on the same test. In practical terms, Anthropic’s flagship now behaves more like a cautious junior operator than a confused intern that forgets what it was doing on day 120.

These long-horizon agentic benchmarks expose a different axis of capability than headline IQ scores or coding leaderboards. They measure whether a model can stay on-task for hundreds of steps, maintain a coherent business strategy, and avoid catastrophic mistakes like burning all capital on a single bad order. As models scale, the Vending Bench numbers climb, suggesting that raw parameter count and better training directly translate into more stable, less deranged decision-making over time.

Alpha Arena pushes the same idea into a harsher domain: live-ish crypto trading. Season 2 features Gemini 3 Pro and Claude Sonnet 4.5 among the contestants, but Opus 4.5 is conspicuously absent from the official roster. A high-performing “mystery model” currently sitting in second place, just behind GPT 5.1, has already sparked speculation that Anthropic is quietly battle-testing Opus 4.5’s risk appetite before putting its name on the leaderboard.

Rise of the AI Orchestrator

Rise of the AI orchestrator might be the most important thing Anthropic quietly shipped with Opus 4.5. Instead of treating a single giant model as the end-all brain, Opus 4.5 increasingly behaves like a manager that plans, delegates, and reviews work done by smaller, cheaper models such as Haiku 4.5. That pattern shows up in long-horizon tasks like Vending Bench, where sustained coherence over 300–350 simulated days matters more than any single response.

Multi-agent setups now consistently beat single-agent baselines on complex research-style workloads. Give one Opus 4.5 instance a broad brief—survey a scientific field, map competitors, draft a product spec—and it can spin up Haiku 4.5 sub-agents to scrape documents, summarize papers, and test ideas in parallel. Benchmarks that stress long-running, tool-heavy workflows, from Vending Bench 2 to OS World–style computer use, reward that division of labor with higher success rates and fewer derailments.

Economic logic drives this architecture as much as raw capability. Running Opus 4.5 for every token of every subtasks wastes expensive capacity on boilerplate summarization and rote transformations that Haiku 4.5 can handle for a fraction of the cost. An orchestrator model that only “thinks hard” when planning, decomposing problems, or resolving conflicts, and otherwise offloads execution, scales more like a human manager coordinating a team than a lone overqualified contractor doing everything.

That manager–team pattern generalizes beyond search and research. In coding, an Opus 4.5 orchestrator can design the system, define interfaces, and then spawn Haiku 4.5 agents to implement modules, write tests, and run Terminal-Bench–style tool commands, before performing final integration and review. For creative work, a top-level model can outline a campaign, while sub-agents draft copy variants, storyboard visuals, and adapt content to platforms.

Business analysis may shift the most. An orchestrator can direct one agent to pull messy web data into spreadsheets via Claude for Chrome, another to clean and structure it in Claude for Excel, and a third to run scenarios and sanity-check conclusions. As these orchestration patterns harden, “using AI” starts to look less like chatting with a single model and more like hiring a virtual firm led by a single, very capable director.

Where Gemini 3 Pro Still Reigns Supreme

Multimodal remains Gemini 3 Pro’s home turf. While Opus 4.5 pushes past it on code and abstract reasoning, Gemini 3 Pro still delivers cleaner, more reliable results when text, images, and layout all matter at once, especially in production workflows that mix screenshots, charts, and embedded media.

Graphics generation shows the sharpest gap. Google’s Nanaband Pro, bundled into Gemini 3 Pro, produces “absolutely incredible” illustrations and UI mockups that feel closer to a dedicated image model than a bolted-on extra. Opus 4.5, by contrast, still behaves like a text-first system that can look at images rather than a true visual native.

Video comprehension is another area where Gemini 3 Pro pulls away. It can track objects and people across clips, follow scene changes, and answer granular questions about what happens at specific timestamps with higher consistency than Opus. For teams summarizing meetings, annotating training footage, or mining user research videos, Gemini 3 Pro remains the safer bet.

Document-heavy workflows tilt the same way. Feed Gemini 3 Pro a 200-page annual report full of dense tables, charts, and diagrams, and it will usually preserve structure, cross-reference figures, and keep visual context intact. Opus 4.5 can parse PDFs, but Gemini 3 Pro tends to make fewer mistakes when numbers live inside complex visual layouts. For more information, see Anthropic Claude Opus 4.5 Official Announcement.

Dynamic web UI generation may be Gemini 3 Pro’s most underrated advantage. It can read a design spec, generate responsive HTML/CSS/JS, and iterate on layout with a designer in the loop, using screenshots as a shared language. Paired with Nanaband Pro, it can prototype entire flows—landing pages, dashboards, marketing sites—without leaving a single chat thread.

That mix of strengths makes Gemini 3 Pro the default choice for: - Creative professionals building visuals, storyboards, and interactive mockups - Data analysts living in slide decks, BI dashboards, and visually rich PDFs - Developers shipping interactive web apps and internal tools that hinge on UI polish

Anyone evaluating these trade-offs should start with the official capability matrix in the **Google DeepMind Gemini Official Documentation**, then layer on cost, latency, and how much of their workload is truly visual-first versus text- or code-heavy.

The Billion-Dollar Question: Cost vs. IQ

Call it an intelligence curve or a pricing curve, but frontier models now live on a graph with two axes: raw capability and what Anthropic calls a “thinking budget.” Push more tokens through the model—8K, 16K, 32K, 64K of deliberate reasoning—and performance climbs, but cost rises nonlinearly. The industry now optimizes not just for peak scores, but for how much IQ you get per dollar at each of those steps.

Anthropic’s own charts plot this on a logarithmic cost axis. Each move to the right represents a big jump in compute spend, yet Opus 4.5’s “salmon” curve hugs the top-left of ARC-AGI2: high scores at relatively low cost per task. Google’s unreleased Gemini 3 Deep Think pushes higher still, but at a much steeper cost point, while released Gemini 3 Pro trails Opus 4.5 at comparable thinking budgets.

That positioning feeds a bolder claim from Anthropic CEO Dario Amodei: comparable outcomes to rival labs using roughly one-tenth the capital expenditure. If accurate, that advantage compounds—cheaper experimentation, more training runs, and faster iteration on things like tool use and agentic behavior. Opus 4.5’s state-of-the-art ARC-AGI2 and OS World scores suggest that efficiency is showing up not just in the P&L, but in benchmarks.

For buyers, the cost-benefit story splits along task lines. On pure reasoning—SWE-Verified coding (80.9 vs Gemini 3 Pro’s 76.2), Terminal-Bench, ARC-AGI2, long-horizon agent tasks like Vending Bench—Opus 4.5 often reaches a target quality with fewer wasted tokens than Gemini’s Deep Think-style modes. If you care about unit economics on complex back-end systems, agents, or automated ops, Opus 4.5 likely yields lower effective cost per solved task.

Flip to multimodal and the calculus changes. Gemini 3 Pro’s image, video, and document handling, plus generation via tools like Nanaban Pro, can compress entire workflows into a single, slightly more expensive call that replaces multiple text-only steps. For anything dominated by visual IO—UI mocks, marketing assets, slide decks, video understanding—Gemini 3 Pro often wins on cost per deliverable, even if Opus 4.5 remains cheaper per token of “thinking.”

Your Desktop, Now Supercharged

Benchmarks only matter if they show up in products, and Anthropic is wasting no time. Alongside Opus 4.5, the company is rolling out Claude for Chrome and Claude for Excel, two features that effectively turn benchmark wins in computer use and long-horizon planning into something you can run on a laptop at work.

Claude for Chrome leans directly on Opus 4.5’s 66.3% success rate on the OS World computer-use benchmark, now the best among released frontier models. Instead of just summarizing a page, Claude can take control of the browser: click through multi-step flows, fill forms, navigate dashboards, and pull data from poorly structured sites that mix text, images, and odd layouts.

That matters for the kinds of tasks benchmarks like Vending Bench try to simulate. Researching products, comparing prices, tracking inventory, or watching competitors across dozens of tabs becomes a delegated job for an AI orchestrator that can stay coherent over hundreds of steps, not just a chat window that answers questions.

Claude for Excel aims at the other half of office drudgery: numbers and structure. Opus 4.5 can ingest large, messy spreadsheets, explain what each sheet and formula does, trace dependencies across workbooks, and surface anomalies that would normally demand a human analyst staring at pivot tables for hours.

Beyond explanation, Anthropic is clearly targeting analysis and planning. Claude for Excel can take raw exports, normalize columns, generate calculated fields, build charts, and then synthesize trends and recommendations—exactly the kind of multi-step, tool-heavy workflow where Opus 4.5 already outperforms Gemini 3 Pro in agentic tool use and terminal-style tasks.

Anthropic is also aligning access with where this matters most. Claude for Chrome is rolling out to all Max users, while Claude for Excel is expanding in beta to Max, team, and enterprise customers, the groups most likely to live inside browser-based SaaS and sprawling financial models. For more information, see Gemini 3.0 vs GPT-5.1 vs Claude 4.5 vs Grok 4.1: Comprehensive AI Model Comparison.

Taken together, these launches show Anthropic productizing specific strengths: state-of-the-art computer use, strong spreadsheet handling, and long-running, coherent task management. Opus 4.5 is not just scoring higher on synthetic tests; it is quietly wiring those capabilities into the everyday software stack that runs modern work.

The Threshold of Autonomy

Autonomy now has a working definition inside labs: R&D4. In Anthropic’s taxonomy, that marks the point where an AI can “fully automate the work of an entry-level remote-only researcher” across literature review, experiment design, basic analysis, and writeups, with only light human oversight. It is not generic “AGI”; it is the point where an AI can be dropped into a Notion workspace and Jira board and simply do the job.

Anthropic explicitly says Opus 4.5 does not clear that bar. The model still lacks broad situational judgment, especially when requirements shift mid-project or when stakeholders disagree. It also struggles with the messy parts of real research work: resolving ambiguous instructions, pushing back on bad ideas, and coordinating with multiple humans who have conflicting priorities.

The caveat buried in Anthropic’s own release is more interesting than the disclaimer. With “highly effective scaffolding”—planning layers, memory systems, tool APIs, and human-in-the-loop checks—Anthropic says models like Opus 4.5 are “not very far away” from R&D4. In practice, that means orchestration frameworks that break work into sub-tasks, route them to cheaper models like Haiku 4.5, and keep a long-horizon agenda intact over hundreds of steps.

Developers are already wiring this up. Agentic stacks that pair Opus 4.5 with vector search, code execution, and browser control via tools like the Anthropic Python SDK Repository can run multi-day research loops: scrape papers, summarize methods, generate experiments, and update a lab notebook autonomously. The constraint is no longer raw IQ alone, but how well the scaffolding constrains and audits that intelligence.

Google’s Alpha Evolve project offers a preview of where this goes. In early reports, Google wrapped an older, weaker model in a tight evolutionary loop—automated hypothesis generation, simulation, evaluation, and selection—and still managed to surface genuinely novel scientific results. The breakthrough did not come from a single giant brain, but from a system that treated the model as a component in a larger autonomous pipeline.

Opus 4.5 plus robust scaffolding looks like the same pattern pointed at general knowledge work. Once R&D4 is crossed, “entry-level researcher” stops being a job description and becomes a system configuration.

Your Next Move in the AI Arms Race

AI teams now face a straightforward fork in the road: match each model to the work that actually makes or saves money. Benchmarks like SWE-Verified (Opus 4.5 at 80.9 vs Gemini 3 Pro at 76.2) and Vending Bench 2 (Gemini 3 Pro just under $5,500 vs Opus 4.5 just under $5,000) now translate directly into product choices, staffing plans, and cloud bills.

Choose Opus 4.5 for: - Advanced coding: long-horizon refactors, framework migrations, and multi-repo debugging where SWE-Verified and Terminal-Bench scores matter. - Agentic orchestration: an Opus “orchestrator” delegating to Claude Sonnet and Haiku 4.5 for cheaper sub-tasks, especially on OS World-style computer-use workflows. - Complex reasoning: ARC-AGI-2–level abstract problems, multi-day research, and R&D4-style “entry-level researcher” automation where thinking tokens dominate over raw output volume.

Choose Gemini 3 Pro for: - Multimodal work: dense PDFs, UI mockups, and visually complex dashboards where its image and document understanding still lead. - Creative generation: marketing campaigns, storyboards, and high-fidelity graphics via systems like Nanaban Pro. - Video and dynamic media: timeline reasoning, scene analysis, and mixed text-image-video projects that Opus 4.5 cannot yet match end-to-end.

Strategy for practitioners: standardize on a dual-stack. Use Opus 4.5 as the reasoning and coding backbone, especially for agents that run for hours or days, and route anything visual, cinematic, or brand-facing to Gemini 3 Pro. Wrap both behind a usage router that looks at task type, context size, and latency budget, then picks the cheapest model that hits your quality bar.

Rapid, leapfrogging releases from Anthropic, Google, and others have erased any notion of a durable monopoly on state-of-the-art AI. Intelligence curves now update on a 60–90 day cadence, not a multi-year one, and every new model reshuffles which tasks can be profitably automated.

Six months from now, expect at least one more tier of autonomy: agents that not only run your “entry-level researcher” workflows but also design, launch, and A/B test products across web, mobile, and data stacks—while you quietly swap in whichever lab’s model sits at the new top of the curve.