Claude Sonnet 4.6 Tested: Near-Opus at Half Price

Claude Sonnet 4.6 Tested: Near-Opus at Half Price

I almost didn't test Sonnet 4.6.

Seriously. I was deep in an Opus 4.6 workflow — agents running, code shipping, the whole machine humming — and my first reaction when Anthropic dropped Sonnet 4.6 was "cool, I'll get to it next week." Then a friend in my Discord sent me a screenshot of a SaaS landing page Sonnet 4.6 had generated in a single prompt. Clean typography. Cohesive color system. A hero section that looked like a designer spent three hours on it.

I stopped what I was doing and opened the API console.

What followed was a 72-hour testing binge that fundamentally changed how I think about model selection for my projects. Because here's the thing — I've been an Opus loyalist since day one. Opus 4.6 is my workhorse. My daily driver. The model I trust with production code and complex agent architectures. So when I tell you Sonnet 4.6 made me question that loyalty, understand the weight of what I'm saying.

This isn't a model that's "pretty good for the price." This is a model that matches or beats Opus in specific tasks while running twice as fast and costing roughly half as much. And the million-token context window currently in beta? That changes the game for anyone building agent systems or working with large codebases.

I ran Sonnet 4.6 through every test I could think of — front-end generation, 3D simulations, browser automation, game development, SVG graphics, and full agent-driven project builds. Some results genuinely shocked me. Others revealed clear limitations you need to know about before switching.

Let me walk you through all of it.

Why Another Sonnet Matters (Even If You're Already Using Opus)

I need to address something I see constantly in developer communities: model fatigue. Every few weeks, a new model drops and everyone debates whether it's "the one." I get the cynicism. Most upgrades are incremental. A point or two on some benchmark nobody cares about in practice.

Sonnet 4.6 is different, and I can pinpoint exactly why.

The previous Sonnet 4.5 was solid. Good enough for simple tasks, fast enough for real-time applications, cheap enough to run at scale. But it had a ceiling. Complex multi-step reasoning would trip it up. Long code files would cause it to lose context and hallucinate function names that didn't exist. Agent workflows with more than 3-4 steps would sometimes go off the rails.

Sonnet 4.6 doesn't just raise that ceiling — it removes it for most practical use cases. The benchmark numbers tell part of the story: a 79.6 on the SWE-Bench verified test, state-of-the-art scores in agentic coding, and financial analysis results that compete with models twice its price tier. But benchmarks are just marketing until you run the model yourself.

So I ran it. Hard. On the exact tasks I use Opus for every day.

And the pricing context matters here. Opus 4.6 runs at roughly $6 per million input tokens and $12 per million output tokens. Sonnet 4.6 keeps the Sonnet 4.5 pricing: $3 input, $6 output. That's not a marginal savings — that's half the cost for a model that, in my testing, delivers 85-95% of Opus's capability depending on the task.

For solo developers and small teams burning through API credits? That math changes everything. But does the quality actually hold up under pressure? I had to find out, starting with the task I care most about — shipping real front-end code.

Front-End Generation: The Test That Surprised Me Most

I have a standard front-end test I run on every new model. Same prompt, every time: generate a premium SaaS landing page with a hero section, feature grid, pricing table, testimonials, and footer. I specify the color palette, typography preferences, and general vibe. Then I compare the raw HTML/CSS output.

Opus 4.6 has consistently produced the best results on this test. Clean component structure. Sophisticated spacing. Color usage that actually looks like a designer touched it. So my expectations for Sonnet 4.6 were modest — I figured it would produce something functional but obviously a tier below.

I was wrong.

The landing page Sonnet 4.6 generated had better typography than what Opus typically gives me. The font pairings were more intentional. The color gradients were smoother. The hero section had a subtle animation suggestion in the comments that, when implemented, looked genuinely premium.

Where it fell short: the responsive behavior needed more manual tweaking than Opus's output, and the footer component was slightly generic. But these are 5-minute fixes. The foundation — the part that takes the most time to get right — was exceptional.

I ran this test three more times with different design briefs. Sonnet 4.6 won two out of three. The one it lost was a dark-mode dashboard with complex data visualization components, where Opus's stronger reasoning about component hierarchy made a visible difference.

Here's my takeaway for anyone doing front-end work: if you're generating landing pages, marketing sites, or standard SaaS interfaces, Sonnet 4.6 is not just "good enough" — it might be your better option. The speed advantage alone (roughly 2x faster than Opus) means you can iterate more quickly, and the cost savings add up fast when you're doing multiple rounds of generation and refinement.

But front-end code is one thing. What about something truly complex — like simulating an entire operating system?

The Mac OS Simulation That Broke My Brain

This test started as a joke. A developer in my community challenged me to get Sonnet 4.6 to generate a functional Mac OS interface in the browser. "No way it handles the complexity," he said. "Too many interacting components."

Challenge accepted.

I gave Sonnet 4.6 a detailed prompt describing a Mac OS-style desktop with Finder, Safari, Notes, Mail, Photos, Terminal, Calculator, and Settings — all functional to some degree. I expected a static mockup with maybe a few clickable elements.

What I got was genuinely unsettling in its quality.

The Finder window opened and closed. You could create folders and navigate between them. Safari had a functional address bar with basic tab management. Notes let you create and edit text entries. The Calculator actually worked — every button, every operation, correct results. Settings included wallpaper customization (with actual wallpaper options), volume and brightness sliders that animated smoothly, and a spotlight-style search bar that filtered applications.

Was it a real operating system? Obviously not. But as a single-prompt generation of an interactive UI prototype? I've never seen anything like it from a model at this price point.

The music player was the detail that got me. It not only played (simulated) tracks but animated the dock icon while "music" was active. That's the kind of design detail that requires understanding context, user expectations, and visual feedback patterns — not just rendering components.

I ran the same prompt through Opus 4.6 for comparison. Opus produced a more technically sophisticated result with better error handling and cleaner state management. But Sonnet's version looked better and had more interactive polish. It's almost like Sonnet prioritized the user experience while Opus prioritized the engineering.

Different strengths. Both impressive. But only one costs $3 per million input tokens.

The real question, though — the one I was most nervous about — was how Sonnet 4.6 handles multi-step agent workflows. Because that's where I live professionally, and that's where Opus has been irreplaceable. Until now.

Agent-Driven Development: Boxelcraft and the Multi-Agent Test

This is the test that actually matters for my work. I build AI agent systems professionally. My clients pay me to architect workflows where multiple agents collaborate, write code, test it, and iterate — often autonomously. Opus 4.6 has been the backbone of these systems because it handles complex planning, long-context reasoning, and tool use better than anything else available.

So I set up the hardest test I could think of: an autonomous multi-agent deployment using Kilo Code (which, by the way, offers $25 in free credits — solid way to test this yourself). The task? Build a browser-based Minecraft clone from scratch.

The agents divided the work: one handled terrain generation, another managed game mechanics (block placement, destruction, inventory), a third worked on the UI (health bars, food meters, HUD elements), and a coordinator agent managed the overall architecture.

The result was a playable game called Boxelcraft. Terrain generation with caves. Working health and food systems. Block placement and destruction. A basic inventory system.

Was it perfect? Not even close. Performance was laggy. Some cave generation produced visual artifacts. The physics had edge cases where you'd clip through blocks. But here's the metric that matters: the agents completed the entire build autonomously. No human intervention. No manual debugging mid-process. The planning, implementation, testing, and iteration all happened within the agent swarm.

I've run similar tests with Opus 4.6, and here's the honest comparison:

That speed and cost difference is not trivial. For iterative development — where you're running agents, reviewing output, adjusting prompts, and running again — Sonnet 4.6 lets you do twice as many iterations for the same budget. And in my experience, more iterations almost always beats better single-shot quality.

Pro tip: I've started using a hybrid approach. Sonnet 4.6 for the initial build iterations (fast, cheap, gets you to 80%), then Opus 4.6 for the final polish pass (thorough, catches edge cases, produces cleaner code). This cut my agent workflow costs by about 40% with no noticeable quality drop in the final output.

There's one more capability I tested that deserves its own section — because it's the one with the most practical implications for developers who want to automate real work, not just generate demos.

Browser Automation: Where Sonnet 4.6 Genuinely Excels

I've been building browser automation systems for clients since 2023 — research bots, data scrapers, form-filling agents, monitoring dashboards. This is grunt work that eats developer time, and it's exactly where AI models can deliver massive ROI.

My test: give Sonnet 4.6 a task to create a complete browser automation setup using Python with Playwright, automate a Google search for the latest AI news, scrape the top five headlines, save them to a CSV, and display the results in a real-time dashboard.

The model generated the entire pipeline in a single response. Python script with Playwright for browser control. Proper async handling. CSV write operations with timestamps. A simple Flask dashboard that reads the CSV and displays results with auto-refresh.

What impressed me wasn't that it worked — Opus can do this too. What impressed me was how clean the code was on the first pass. The error handling was thoughtful (retry logic for network failures, graceful degradation if a scrape target changes structure). The Playwright selectors were specific enough to be robust but not so brittle that a minor DOM change would break everything.

I deployed this pipeline and let it run for 48 hours. Zero crashes. The CSV accumulated data reliably. The dashboard stayed responsive.

For comparison, when I gave Opus the same task, it produced slightly more sophisticated code — better logging, more configurable parameters, a more polished dashboard design. But the Sonnet version was production-ready without modification, and it was generated in roughly half the time.

# Sonnet 4.6's approach to the scraper — clean and practical
async def scrape_ai_headlines(page):
    await page.goto("https://news.google.com/search?q=artificial+intelligence")
    await page.wait_for_selector("article h3", timeout=10000)

    headlines = await page.eval_on_selector_all(
        "article h3",
        "elements => elements.slice(0, 5).map(el => el.innerText)"
    )

    timestamp = datetime.now().isoformat()
    with open("headlines.csv", "a", newline="") as f:
        writer = csv.writer(f)
        for headline in headlines:
            writer.writerow([timestamp, headline])

    return headlines

Nothing fancy. Nothing over-engineered. Just solid, working code that does exactly what was asked. And honestly? That's what I want from an AI model 90% of the time. I don't need it to architect a distributed microservice. I need it to write the automation script that saves me two hours of manual work — quickly, cheaply, and correctly.

But I'd be doing you a disservice if I only talked about where Sonnet shines. I found real limitations too, and you need to know about them before making any decisions.

Where Sonnet 4.6 Falls Short (The Honest Assessment)

I've been positive about this model, and it deserves the praise. But there are specific areas where it clearly trails Opus 4.6, and pretending otherwise would be dishonest.

SVG and complex graphics generation. I ran a battery of SVG tests — butterflies, robots, a pelican riding a bicycle, a PS5 controller. Sonnet produced decent results. Recognizable shapes, reasonable color choices, acceptable detail levels. But side-by-side with Opus? The difference is obvious. Opus generates SVGs with finer detail, better proportionality, and more sophisticated use of gradients and shadows. If visual fidelity matters for your use case, Opus is still the clear winner here.

Deep multi-step reasoning with ambiguity. When a task has clear specifications, Sonnet 4.6 executes brilliantly. But when requirements are vague and the model needs to make judgment calls — "build something that feels premium" or "handle edge cases appropriately" — Opus makes better decisions. It's the difference between a solid mid-level developer and a senior architect. Both write working code, but the senior makes better choices about what to build and how to structure it.

Long-form code refactoring. Despite the million-token context window (which is genuinely impressive and works well for reading and analyzing code), Sonnet occasionally loses coherence when refactoring large files. It might rename a function in one section but miss a reference to it 200 lines later. Opus handles this more reliably. Not perfectly — no model does — but noticeably better.

The hallucination gap has narrowed but isn't closed. Anthropic claims reduced hallucinations in Sonnet 4.6, and my testing supports this. It hallucinates less than Sonnet 4.5. But it still hallucinates more than Opus 4.6, particularly with API signatures and library-specific syntax. I caught it inventing a Playwright method that doesn't exist in one test. Opus rarely makes that kind of mistake.

Here's the framework I've landed on after all this testing:

Use Sonnet 4.6 when:

• Speed matters more than perfection
• You're iterating quickly and will review output
• The task is well-specified with clear requirements
• Cost is a factor (it always is, let's be honest)
• You need real-time or near-real-time generation

Use Opus 4.6 when:

• The task requires deep architectural reasoning
• Ambiguity is high and judgment calls matter
• You need maximum code quality on the first pass
• Visual fidelity is critical (SVGs, complex UI)
• You're doing the final polish on something important

This isn't an either/or decision. It's a portfolio strategy. And that's the real insight I want to leave you with.

The Million-Token Context Window Changes Everything (Almost)

I saved this for the second half because it's the feature with the most long-term implications, even if it's still in beta.

A million tokens of context. Let me put that in perspective. The average novel is about 80,000-100,000 words, or roughly 130,000-160,000 tokens. A million tokens is roughly six full novels. Or, more relevantly: your entire codebase, all your documentation, your project requirements, your test suites, and your deployment configs — all held in a single context window.

I tested this with a real project: a 340-file Laravel application with approximately 45,000 lines of code. I loaded the entire codebase into Sonnet 4.6's context and asked it to identify potential security vulnerabilities across the project.

The model found four genuine issues I hadn't caught in my own audit. One was a mass-assignment vulnerability in a model that had been there for eight months. Another was an improperly scoped API route that exposed data from other tenants in a multi-tenant setup.

Could I have found these with a manual audit? Probably — eventually. But the speed at which the model cross-referenced files, traced data flows across controllers, models, and middleware, and identified the interaction patterns that created the vulnerabilities? That would have taken me days of focused work. Sonnet did it in under three minutes.

The strategic planning capabilities are equally impressive. I fed Sonnet 4.6 a complete project specification (12,000 words), the existing codebase, and a list of new features. It generated an implementation plan that correctly identified dependency chains I'd missed, suggested an execution order that minimized conflicts, and flagged three features that would require database migrations affecting production data.

The limitation: the million-token context is in beta, and I noticed degraded performance toward the edges of very long contexts. When I pushed past 800,000 tokens, responses became slightly less precise. References to code in the "middle" of the context (not the beginning or end) were sometimes vague or incorrect. This is a known issue with long-context models and it's improving, but it's worth knowing about.

For my workflow, the practical sweet spot is about 500,000-600,000 tokens. Enough to hold a substantial codebase with room for conversation history. That alone is transformative for agent-based development.

My New Model Strategy (And What It Costs)

Let me give you real numbers from my last month of development work, because abstract comparisons are useless without concrete data.

Before Sonnet 4.6 (Opus-only workflow):

• Monthly API spend: ~$380
• Average iterations per feature: 3-4
• Agent build time for medium complexity task: ~40 minutes
• Production bugs from AI-generated code: 6-8 per month

After adopting hybrid Sonnet/Opus strategy:

• Monthly API spend: ~$220
• Average iterations per feature: 5-6 (more iterations, same quality)
• Agent build time for medium complexity task: ~25 minutes
• Production bugs from AI-generated code: 4-5 per month

The spend dropped 42%. The bug count dropped by roughly a third. And I'm actually iterating more, which means catching issues earlier in the development cycle rather than in production.

The workflow looks like this in practice:

1. Exploration phase (Sonnet 4.6): Quick prototype, test the approach, validate the architecture. Fast and cheap.
2. Implementation phase (Sonnet 4.6): Build out the feature with agent-driven development. Multiple iterations.
3. Review phase (Opus 4.6): Final code review, edge case analysis, security audit. Thorough and precise.
4. Deployment: Ship with confidence.

Steps 1 and 2 account for about 70% of my API usage. Running those on Sonnet instead of Opus is where the savings come from. Step 3, where precision matters most, stays on Opus.

Quick wins if you want to try this today:

• Sign up for Kilo Code's free $25 credit to test Sonnet 4.6 without committing budget
• Run your most common prompt through both Sonnet and Opus, compare side by side
• Try LM Arena for blind comparisons — you might be surprised how often you can't tell which is which
• If you're using Claude Code, experiment with switching your default model for routine tasks

What This Means for the Next Six Months

I want to close with something I've been thinking about since finishing these tests.

Anthropic has essentially made near-Opus intelligence available at the mid-tier price point. That's not just a product update — it's a signal about where the industry is heading. The gap between "the best model" and "the affordable model" is collapsing. Six months from now, the model that costs $3 per million tokens will probably match what today's $15 model does.

For developers, this means the barrier to building sophisticated AI-powered systems keeps dropping. The agent architectures I build today — which require careful cost optimization and model routing — will be trivially cheap to run by next year. The browser automation pipelines, the code generation workflows, the multi-agent build systems — all of this becomes accessible to solo developers and small teams who couldn't justify the API costs before.

But here's the thing I keep coming back to: cheaper models don't make better developers. They make more AI-generated code available, which means the skill that matters most is the ability to evaluate, debug, and improve that code. The developers who thrive in this landscape aren't the ones who can prompt best — they're the ones who understand what the model is doing well enough to know when it's wrong.

I tested Sonnet 4.6 for 72 hours. It impressed me. It saved me money. It changed my workflow. But the moment I caught it inventing a nonexistent Playwright method — the moment I recognized the hallucination because I know Playwright's API — that was the reminder.

The model is the tool. You're still the builder.

If you're using Opus exclusively right now and haven't tried the hybrid approach, do it this week. Run your standard workflow on Sonnet for the exploration and build phases, switch to Opus for the review. Track your costs and quality for two weeks. I think you'll be surprised by the numbers — just like I was when my friend sent me that screenshot and I stopped what I was doing to investigate.

That landing page, by the way? I showed it to a client. They thought a human designer built it. The model that generated it cost me about four cents.

What does your current AI workflow cost you — and what would you build differently if that cost dropped by half?

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