Opus 4.5 Just Obliterated Gemini for Coding

The New AI Arms Race for Developers

AI coding assistants no longer feel like futuristic toys; they feel like IDE extensions you actually rely on. With models like Opus 4.5 and Gemini 3 Pro shipping within weeks of each other, developers now live in a permanent upgrade cycle, constantly asking whether their current model is quietly taxing their productivity with subtle bugs, sluggish responses, or bland boilerplate code.

Each release promises the same thing: fewer hallucinations, better reasoning, smarter tool use. Opus 4.5 slashed its pricing to around $5 per million input tokens and $25 per million output tokens, roughly a third of its old rate, yet it still costs more than double Gemini 3 Pro. That gap forces a hard question: does premium reasoning and autonomy actually translate into a faster shipped product?

Rob Shocks frames that question bluntly in his video, “I Built the Same App with Cursor, Gemini 3 and Opus 4.5 (The Clear Winner).” Developers do not care about leaderboard bragging rights; they care whether a model can take a vague product idea and turn it into a working micro-SaaS without babysitting every function. The real decision is not “Which model is smarter?” but “Which one ships more reliable code for less money and time?”

To answer that, Shocks ditches synthetic benchmarks and builds the exact same micro-SaaS from scratch with each model inside Cursor, using no manual coding. Both models get the same high-level voice prompt, the same project context, and access to the same tool stack, including a browser for live previews and console checks. That setup turns the comparison into a controlled A/B test for real developer workflows, not just contrived coding puzzles.

The methodology tracks several concrete metrics:

• Planning quality and task breakdown
• Raw throughput and latency for each step
• Tool calling behavior (browser, tests, console)
• Final UI quality, responsiveness, and bug count

By holding everything except the underlying model constant, the experiment surfaces how Opus 4.5 and Gemini 3 Pro actually behave when asked to plan, design, implement, and self-test a production-style micro-SaaS.

Price vs. Power: The New Math

Price cuts turned Opus 4.5 from a “break-glass-only” model into something developers can actually afford to leave on all day. Input tokens dropped to around $5 per million and output to $25 per million, down from a punishing $15 / $75. That shift alone reclassifies Opus from a special-occasion debugging nuke to a plausible default assistant in tools like Cursor and VS Code.

Gemini 3 Pro still undercuts it hard. Depending on tier, Google’s model lands at well under half that rate per million tokens, so Opus 4.5 remains more than 2x the price for comparable usage. For teams watching burn in multi-developer environments, that delta adds up to thousands of dollars a month.

So the question becomes: does Opus 4.5’s performance justify paying a “Claude tax” for day-to-day coding? In Rob Shocks’ tests, Opus 4.5 consistently produced cleaner architectures, better UI, and more reliable autonomous tool use, even when it took longer wall-clock time. When a model can ship a micro-SaaS front to back with fewer retries, the extra token cost often disappears into saved engineer hours.

Developers do this math subconsciously: one hour of senior dev time can cost more than tens of millions of tokens. If Opus 4.5 prevents a single wild-goose-chase bug hunt or rewrite per week, the premium easily pays for itself. That calculus skews even harder toward Opus in high-stakes work—production migrations, complex refactors, or multi-service debugging.

Throughput complicates the value equation further. Shocks calls out model throughput—how fast tokens stream back—as a surprisingly huge factor in satisfaction. A snappy model encourages tight prompt–edit–prompt loops; a sluggish one nudges you to tab away and context-switch.

Opus 4.5 holds its own here, with responsive streaming that feels close to the “instant” bar Haiku and Cheetah set. Gemini 3 Pro often lands in a similar “moderate difference” range, but when Opus is both faster to respond and more likely to get the code right on the first or second try, that speed compounds its quality advantage. Over a full workday, those seconds turn into dozens of extra meaningful iterations.

Beyond Benchmarks: Raw Performance in the Real World

Benchmarks say Gemini 3 Pro and Claude Opus 4.5 are basically peers. Independent testing from Artificial Analysis pegs Gemini 3 Pro at 73 on its general index, with Claude and GPT 5.1 High both at 70, and their coding scores sit within a few points of each other. On paper, that reads like a dead heat.

Reality looks different when you’re actually shipping code. Rob Shocks’ Cursor tests highlight throughput—how fast tokens hit your screen—as the hidden stat that reshapes the entire developer experience. Once you’ve used a model that streams near-instantly, slower responses feel like latency tax on your attention.

Faster models don’t just feel nicer; they change how you work. With Opus 4.5 running in Cursor, Shocks can fire off a vague instruction, watch the model sketch a plan in ~19 seconds, then course-correct every few minutes as it iterates. That tight feedback loop encourages a guided, conversational workflow instead of giant, fragile one-shot prompts.

Gemini 3 Pro keeps up on headline completion times—its initial plan for the same task landed in 27 seconds and the page build wrapped in about 4 minutes 22 seconds. But Opus 4.5 spent extra minutes autonomously opening a browser, taking screenshots, checking console logs, and even reworking mobile breakpoints, turning a ~5-minute design pass into a ~9-minute, fully sanity-checked flow. Speed here isn’t just “how fast it finishes,” but “how much high-value work it does per minute.”

That difference sets the stage for a more demanding real-world test. Shocks kicks off with a deliberately vague, voice-prompted request: build a full marketing landing page with only high-level guidance. The challenge is simple: see which model can take a fuzzy product idea, infer structure, and ship a visually coherent, production-ready layout with minimal hand-holding. For more on Opus 4.5’s design goals and trade-offs, Anthropic’s own breakdown lives at Introducing Claude Opus 4.5 - Anthropic.

First Blood: The Landing Page Showdown

Cursor’s first trial was simple on paper: build a marketing landing page for a fictional app called InstaPlan using a single high-level voice prompt, no manual coding, and plan mode enabled. Same prompt, same environment, two runs—one with Opus 4.5, one with Gemini 3 Pro—stopwatch running on both.

Opus 4.5 immediately treated the vague brief as a requirements-gathering exercise. It fired back four to five clarifying questions about target users, brand tone, sections, and calls to action, then expanded those answers into a detailed multi-step plan: layout, color system, typography, hero section, feature grid, testimonials, pricing, and responsive states.

Gemini 3 Pro took a leaner route. It responded with just two follow-up questions and produced a noticeably shorter, more concise plan with eight to-dos, focusing on a standard hero, features, and CTA stack. On paper, that looked efficient—less back-and-forth, fewer moving parts, faster path to code.

Raw timing numbers seemed to back Gemini 3 Pro. Its run clocked in at about 4 minutes 22 seconds from prompt to “done,” while Opus 4.5 didn’t wrap until roughly 9 minutes. If you only stare at the stopwatch, Gemini 3 Pro appears more than twice as fast for the same “build a landing page” task.

That headline, however, completely hides what Opus 4.5 actually did with the extra five minutes. After generating the page in around 4–5 minutes—the same ballpark as Gemini 3 Pro—Opus autonomously triggered Cursor’s browser tool, opened the live preview, captured screenshots, and started validating its own work.

Under the hood, Opus 4.5 ran a mini QA pass: it scanned the rendered layout, checked console logs for errors, and then iterated. Cursor’s logs showed it testing responsive breakpoints, deciding the mobile layout “wasn’t working the way it likes,” and pushing follow-up edits to fix spacing, stacking, and typography on smaller screens.

Gemini 3 Pro, in contrast, never touched the browser tool at all. It shipped a clean but AI-generic layout—no autonomous testing, no console checks, no mobile tuning. Opus 4.5 spent its extra minutes acting like a junior front-end engineer; Gemini 3 Pro behaved like a fast code generator and stopped there.

Opus's Shocking Design Superiority

Opus 4.5 didn’t just edge out Gemini 3 Pro on design; it embarrassed it. Gemini’s InstaPlan landing page looked like something from a generic template mill: big hero, rounded buttons, soft gradients, and safe typography. Clean, yes, but aggressively AI generic—the kind of layout that felt impressive six months ago and now blends into every boilerplate SaaS mockup on Dribbble.

Gemini 3 Pro shipped a page that would pass as a decent MVP wireframe, not a polished product. No memorable branding, no standout visual hierarchy, no micro-interactions or flair. In a world where anyone can prompt out a Tailwind starter in 30 seconds, “run of the mill” design is basically a bug.

Opus 4.5, by contrast, produced what Rob Shocks called “one of the best designs I’ve seen generated by AI.” The InstaPlan page came with a custom logo that cleverly fused an “I” and a “P,” rather than a random icon from a stock set. Shadow effects, spacing, and layout felt intentional rather than auto-generated, giving the page actual visual weight and a premium feel.

Cursor’s autonomous browser checks amplified that polish. Opus didn’t just dump HTML and CSS; it opened the browser, took screenshots, checked console logs, and iterated. It even tested breakpoints and then adjusted the layout when mobile behavior “wasn’t working the way it likes,” treating responsive design as a first-class requirement, not an afterthought.

Deliverables told an even sharper story. Opus generated a structured project with a detailed README, clear sections, and a coherent plan that asked multiple clarifying questions up front. The output felt like a starter repo you could hand to a junior dev and say, “Ship this.”

Gemini 3 Pro, meanwhile, delivered a basic project skeleton and a shorter, more generic plan with only two follow-up questions and eight to-dos. It skipped browser-based validation entirely inside Cursor, suggesting weaker tool-calling behavior in this setup. You got code, but not a productized experience.

Time-to-output numbers almost don’t matter in that context. Opus took around 9 minutes end-to-end versus Gemini’s roughly 4 minutes and 22 seconds, but about half of Opus’s time went into automated testing and refinement. For a landing page that actually looks client-ready, that extra few minutes from Opus 4.5 feels less like latency and more like free design labor.

The Core Challenge: Building a Real Micro-SaaS

Opus’s real test came with a second challenge: stop decorating InstaPlan and actually ship a product. Instead of another static landing page, the brief upgraded to a real micro-SaaS backend that could survive first contact with users, APIs, and browser console errors. Cursor stayed as the playground, but the expectations jumped from “nice UI” to “working pipeline.”

The spec sounded simple but hid plenty of failure modes. InstaPlan needed to accept an image upload from the browser, forward that file to an external model via the Gemini 3 Pro Image Preview API on Open Router, then return structured analysis the frontend could render. That meant handling multipart uploads, API auth, error states, and latency without the whole thing collapsing into a 500.

To keep the models honest, the prompt didn’t just say “build the backend.” Rob Shocks wired in concrete requirements: use Next.js, use the App Router, and expose a single API route that accepts an image and calls Open Router. The system prompt dropped a partial implementation, including the fetch call and headers, and asked the model to fill in the missing logic cleanly.

The core snippet looked something like this inside `app/api/analyze/route.ts`:

```ts export async function POST(req: Request) { const formData = await req.formData(); const file = formData.get("image") as File;

const openRouterRes = await fetch("https://openrouter.ai/api/v1/chat/completions", { method: "POST", headers: { "Authorization": `Bearer ${process.env.OPENROUTER_API_KEY}`, "Content-Type": "application/json", }, body: JSON.stringify({ model: "google/gemini-3.0-pro-preview", messages: [{ role: "user", content: [{ type: "input_image", image_url: "..." }] }], }), });

// model fills in parsing, validation, and response } ```

Opus immediately treated this like a product spec, not a leetcode puzzle. It fired back clarifying questions: how robust should validation be, what error copy should users see, and should the output feel like a lightweight assistant or a dense project brief? It even asked about rate limiting and whether to persist results or keep everything stateless.

Gemini 3 Pro took a different tack. It skipped discovery and dropped a short, confident plan: define the API route, wire up Open Router, return JSON, then “hook it to the UI.” No questions about complexity, no pushback on edge cases, and no attempt to scope non-functional requirements. On paper, both models knew Next.js; only one acted like a senior engineer.

For readers who want raw numbers, Claude Opus 4.5 Benchmarks - Vellum AI shows how this kind of planning advantage shows up in tooling and latency metrics.

Tool-Calling: The Invisible Skill That Changes Everything

Tool-calling quietly became the biggest skill gap between Opus 4.5 and Gemini 3 Pro once the InstaPlan build moved beyond pretty landing pages and into actual app logic. Inside Cursor, Opus behaved like a junior engineer who understands the whole stack, not just the code editor in front of it.

Cursor exposes a browser, a dev server, and other tools that models can invoke autonomously. Opus 4.5 immediately leaned into that: it spun up the development server, opened the browser preview, and started iterating against the live app without being explicitly told to do so.

During the landing page test, Opus not only generated the UI in about 4–5 minutes, it then spent several more minutes using the browser tool to take screenshots, inspect console logs, and tweak layout issues. It even detected broken mobile breakpoints and pushed its own fixes, all while the stopwatch ticked up to roughly 9 minutes total.

That same behavior carried over to the micro-SaaS backend. Opus treated Cursor’s tools as part of its action space: run the server, hit routes, observe errors, adjust code, repeat. Autonomous testing and refinement turned a static code dump into something much closer to an end-to-end build pipeline.

Gemini 3 Pro, by contrast, looked almost blind to its surroundings. In both the design and app-building runs, it never invoked the browser tool at all, despite having access to it under the same Cursor configuration.

Instead of booting the dev server itself, Gemini 3 Pro left the human to do the boring glue work: open a terminal, run the server, manually refresh the preview, copy errors back into the chat. The model produced code, but it did not orchestrate the environment around that code.

That gap might sound like a small UX quirk; it is not. Effective tool-calling is a proxy for whether a model can handle complex, multi-step workflows without a human constantly shepherding it from step to step.

Every time a model autonomously runs a server, opens a browser, inspects logs, and retries, it collapses a dozen micro-interruptions that normally derail a developer’s focus. Across a day of prototyping and debugging, that compounds into hours saved and a fundamentally different ceiling on what “no-code” AI-assisted development can actually ship.

When Things Go Wrong: AI as a Debugging Partner

Real-world app builds never go smoothly, and InstaPlan was no exception. Partway through wiring up the backend, the whole stack started throwing 500s on every request to the scheduling endpoint. No stack trace, no helpful error message—just a generic server error from what should have been a straightforward API call.

Instead of spelunking blindly through files, the developer asked Opus 4.5 to instrument the code with more detailed logging. Cursor handed control to the model, which added granular logs around the external API client, environment variable loading, and request payload validation. Within one more run, the server console turned from a black box into a step-by-step execution diary.

Those logs immediately exposed something subtle: the app booted “successfully,” but the external planning API client never received a valid key. Opus scanned the new output, cross-referenced the configuration code with the .env template it had generated earlier, and flagged that `INSTAPLAN_API_KEY` was coming through as `undefined`. Its next move was telling: it didn’t just blame “missing config,” it suspected a mismatch between the environment variable name in code and in the .env file.

A quick comparison later, Opus called the shot like a senior engineer doing a code review. The .env file used `INSTAPLANN_API_KEY`—one extra “N” buried in a wall of variables. That single-character typo caused every downstream 500. Opus highlighted the exact line, proposed the corrected spelling, and reminded the developer to restart the dev server so Node would reload the environment.

This is where advanced reasoning separates Opus 4.5 from a generic code generator. The model didn’t just patch symptoms or blindly retry the request. It formed a hypothesis, used logging as a diagnostic tool, and traced the failure across code, runtime behavior, and configuration—exactly how a human senior dev approaches a weird production bug.

As a debugging partner, Opus operated less like autocomplete and more like an always-on staff engineer who notices what you fat-fingered at 1 a.m.

The Final Verdict: Quality Over Haste

Speed crown goes to Gemini 3 Pro. Across both tests, Gemini consistently shipped first: roughly 4 minutes for the InstaPlan landing page and noticeably quicker iterations during backend work. If you only measure wall-clock generation time, Gemini looks like the obvious pick.

Quality flips that story. Opus 4.5 produced a landing page that looked like something a human product designer would actually ship: custom logo, thoughtful spacing, responsive tweaks, and mobile breakpoint fixes it discovered and patched on its own. Gemini’s version, finished in about the same raw time, never opened the browser, never validated the layout, and landed squarely in “AI generic” territory.

The micro-SaaS backend made the gap wider. Opus structured the project more cleanly, leaned on autonomous tool-calling, and ran its own checks instead of waiting for human prodding. When a misconfigured API key triggered a 500 error, Opus behaved like a senior engineer, walking through logs, isolating the configuration issue, and proposing a robust fix.

Gemini moved faster but demanded more manual shepherding: more nudges, more explicit instructions, more human-driven testing. That “fast” model starts to look slow when you factor in the extra cycles spent debugging, refactoring, and re-running flows it never validated itself.

For professional teams, the trade-off stops being “speed vs. features” and becomes raw output speed vs. total project time. Opus costs more per million tokens and often spends extra minutes planning, testing, and revising. Those minutes buy you fewer regressions, less brittle UI, and a backend you do not immediately want to rewrite.

Developers who care about shipped quality, not just demo speed, will save time and money with Opus once you account for the full lifecycle: design, implementation, testing, and maintenance. For a deeper dive into this shift, Claude Opus 4.5 vs Gemini 3 Pro: The Week That Changed AI Forever captures how quickly the ground just moved.

Your Next Move: Choosing Your AI Co-Pilot

Choosing an AI co-pilot now looks less like picking a single IDE and more like assembling a stack. Gemini 3 Pro and Opus 4.5 both clear the “good enough” bar on benchmarks, but their behavior under load makes them suited to very different kinds of developers.

If you optimize for cost and volume, Gemini 3 Pro still wins. It costs less than half of Opus 4.5 per million tokens, so teams hammering an API with thousands of requests per day will feel that delta on their invoice, not their IDE.

Speed-focused builders also lean Gemini 3 Pro. When you’re cranking out quick CRUD tools, internal dashboards, or throwaway prototypes, Gemini’s tendency to ship something “90% fine” in fewer minutes beats Opus’s more deliberate passes. Pair it with heavy multimodal work—video analysis, image-heavy workflows, documentation with diagrams—and Gemini’s 1M-token context and strong vision stack become hard to ignore.

Professional developers targeting production-grade apps should treat Opus 4.5 as their default. Its tool-calling in Cursor—opening browsers, screenshotting, checking console logs, then fixing layout and breakpoint issues—behaved like a junior engineer who actually reads the diff. For debugging 500s, untangling state, and refactoring hairy services, Opus 4.5’s deeper reasoning and more reliable autonomous loops paid off in fewer broken builds.

If UI and UX quality matter, Opus 4.5 is the current front-runner. In the InstaPlan test, it spent ~9 minutes, including self-testing, to generate a page that looked like something a human designer might ship. Gemini 3 Pro finished in ~4 minutes but delivered a run-of-the-mill “AI generic” layout that already feels dated.

Smart teams will stay model-agnostic. Use tools like Cursor to slot in Gemini 3 Pro for cheap, fast, multimodal-heavy work, and Opus 4.5 when correctness, polish, and maintainability decide whether you sleep or ship. The only sustainable strategy in this arms race: assume your stack is temporary and keep swapping in whatever model best fits each task.

Frequently Asked Questions

Is Opus 4.5 better than Gemini 3 Pro for coding?

For complex app development and UI design, tests show Opus 4.5 produces higher-quality, more complete results, including self-testing. Gemini 3 Pro is faster for initial generation but may require more manual work and produces more generic designs.

Why is the price of Opus 4.5 still a factor if it's better?

Despite a significant price drop, Opus 4.5 is still more than double the cost of Gemini 3 Pro. For developers on a tight budget, Gemini offers strong performance at a much lower price point, making it a viable alternative.

What is AI 'tool calling' and why does it matter for developers?

Tool calling is an AI's ability to use external tools, like a web browser or a terminal. In the test, Opus 4.5 used the browser to autonomously test its own code, a crucial capability for automated workflows that Gemini failed to demonstrate.

Can I use both Opus 4.5 and Gemini 3 Pro for development?

Yes. Platforms like Cursor allow developers to switch between different AI models. This enables you to leverage the unique strengths of each model, using Opus for complex logic and Gemini for faster, simpler tasks or multimodal inputs.