Claude Sonnet 4.5 Deep Dive: Features, Pricing, and Real-World Performance

Hey fellow AI tinkerers — I’m Hanks. I test AI tools by shoving them straight into real work, letting them break, and then seeing what if anything is worth rebuilding around. Benchmarks are interesting. Surviving reality is what matters. if you're testing tools inside real workflows, you've probably heard the buzz around Claude Sonnet 4.5.

I’ll be honest. When Anthropic dropped this in late September, my first thought wasn’t “wow, another model.” It was: “Will this survive the kind of messy, multi-step work I throw at AI every single day?”

Because here’s the thing — I don’t care about demos.

I care whether a model can make it through a 30-hour coding sprint, chew through a 150-page legal brief without hallucinating, or actually finish an agentic workflow without completely losing the plot halfway in.

So over the past few weeks, I’ve been running Sonnet 4.5 hard. Real tasks. Real failures. Real adjustments.

This isn’t a feature list. It’s what I learned when I stopped reading benchmarks — and started breaking things.

Claude Sonnet 4.5 Overview: Model, Capabilities, and Comparison

Model Architecture and Key Improvements

Let me set the scene. September 29, 2025. Anthropic releases Claude Sonnet 4.5 as the successor to Sonnet 4. On paper, it's a mid-tier model. In practice? It punches way above its weight class.

What caught my attention wasn't the marketing copy. It was the benchmark jump on SWE-bench Verified — from 72.7% to 77.2%. That's not incremental. That's "I need to test this immediately" territory.

Here's what's under the hood:

Multimodal input: Text, images, audio, video — though outputs are still text-only
200K token context window as standard (with 1M available for specialized access)
Knowledge cutoff: January 2025, so it knows recent stuff
Safety level: ASL-3, which means Anthropic baked in alignment work to reduce sycophancy and deception

But here's where I started paying real attention: they didn't just make it smarter. They made it sustained. This model can focus on complex problems for 30+ hours without shortcuts. I tested this claim. It's legit.

The other thing? Adjustable reasoning depth via API. You can tune it for quick responses or deep analysis depending on your task. That flexibility matters when you're switching between "summarize this email" and "debug this 10,000-line codebase."

Claude Sonnet 4.5 vs Opus vs Haiku: Differences Explained

Okay, so Anthropic has three main models in the Claude 4 family. Let me break down when you'd pick each one — not from specs, but from actual use.

Claude Sonnet 4.5 is where I spend most of my time. It's the Goldilocks model: smart enough for complex reasoning, fast enough for interactive work, cheap enough that I'm not sweating every API call. The 200K context window handles most of my documents without choking. When I'm building agents or debugging code, this is my go-to.

Claude Opus (like Opus 4.1) is what I reach for when Sonnet hits a wall. Finance? Law? Deep STEM reasoning? Opus flexes harder on domain-specific knowledge. Its SWE-bench score is slightly lower (74.5% vs. 77.2%), but on ultra-complex reasoning tasks where nuance matters, it pulls ahead. The trade-off? Higher cost and slightly slower responses. I use it maybe 10% of the time — only when I need that frontier intelligence and Sonnet's not cutting it.

Claude Haiku (Haiku 4.5) is the speed demon. Lightweight, dirt cheap, perfect for high-volume simple queries. Think customer support bots, quick summaries, basic classification. It matches the performance of older Sonnet versions but at way lower latency and cost. The catch? Don't ask it to build a complex agent or reason through 50 steps. That's not what it's built for.

Here's my rule of thumb:

Sonnet 4.5: Default for 90% of tasks (coding, agents, document analysis)
Opus: When Sonnet fails on ultra-complex domain reasoning
Haiku: When you need speed and volume, not depth

One more thing that surprised me: Sonnet 4.5's pricing is identical to Sonnet 4, but the performance leap is significant. That's rare in AI. Usually you pay more for better. Here, you just get better.

Claude Sonnet 4.5 Key Features and Strengths

Advanced Reasoning Ability and Accuracy

Let me tell you about the moment I realized this wasn't just another incremental update.

I threw a multi-step legal synthesis task at Sonnet 4.5 — the kind where you need to trace precedents across 12 different cases, identify conflicting opinions, and synthesize a coherent argument. Previous models would either lose the thread around step 8 or start hallucinating connections that didn't exist.

Sonnet 4.5 didn't just complete it. It sustained focus through 30+ reasoning steps without taking shortcuts.

Here's what the benchmarks say:

89.1% on MMMLU (massive multitask language understanding)
83.4% on GPQA Diamond (graduate-level science questions)
100% on AIME 2025 (math Olympiad problems, when using tools)

But benchmarks don't tell you about the quality of reasoning. What I noticed in real use:

Fewer shortcuts: It reduced shortcut behaviors by 65% compared to Sonnet 4. That means it actually works through problems instead of pattern-matching to the "looks right" answer.
Adjustable depth: Via the API, you can tune reasoning effort. Need a quick take? Low setting. Need it to really think? Crank it up. I tested both extremes. Quick mode is genuinely faster without being dumb. Deep mode is… intense. Like watching it actually think.
Domain adaptation: I've used it for everything from investment analysis to debugging Rust code. The reasoning stays sharp across domains. It doesn't just memorize patterns — it builds logical chains.

Quick reality check here: this doesn't mean it's perfect. I've still seen it confidently state wrong answers on edge cases. But the error rate and the error type are different. It's less "confident nonsense" and more "reasonable but slightly off."

Large Context Window Support (200K Tokens)

Okay, so 200,000 tokens. Let me make this concrete.

That's roughly:

150,000 words
300-400 pages of dense text
An entire codebase with documentation
Multiple research papers plus your analysis notes

I first stress-tested this with a 180-page financial report. Not a summary request — I needed it to cross-reference specific clauses, identify risks, and flag inconsistencies across sections that were 100+ pages apart.

It didn't lose context. Not once.

Here's what makes this window actually useful (beyond just "bigger number"):

Context editing: This is the hidden superweapon. Anthropic says it reduces token waste by 84%. What that means in practice: you can edit your context mid-conversation without reloading everything. I was analyzing a legal document, realized I needed to swap in a different precedent case, and just... swapped it. No context reset. No "summarize what we talked about." It just kept going.

Long-horizon tasks: I've run agent workflows that spanned multiple days of conversation. The 200K window meant I could keep entire project histories in context. Customer data, previous decisions, code evolution — all right there, all the time.

Output capacity: Up to 64K tokens out. That's not just "long answer." That's "write me a comprehensive technical spec with examples, edge cases, and implementation notes." I've generated 40-page research reports in a single shot.

But here's the plot twist: there's also a 1M token option for specialized access. I haven't used it yet (don't need it for my workflows), but if you're processing massive datasets or coordinating multi-agent systems, that's your unlock.

One warning: ultra-long contexts do add latency. Not catastrophic, but noticeable. I measured roughly 2.33 seconds average, but with a 180K token context it can spike to 4-5 seconds. Still usable for research workflows. Less ideal for real-time chat.

Speed, Latency, and Performance Metrics

Let's talk about speed, because this is where Sonnet 4.5 actually surprised me.

Baseline latency: ~2.33 seconds to first token. Throughput: 191 characters per second once it starts flowing.

Is that fast? Depends on what you're comparing against. For a model this capable, yeah, it's fast. I've used it for interactive debugging sessions, and the response time feels conversational, not "waiting for the AI to wake up."

But the real speed unlock isn't raw latency. It's parallel tool execution.

Here's what I mean: I was building an agent that needed to run multiple bash commands, fetch API data, and process files simultaneously. Instead of executing tools sequentially (command 1, wait, command 2, wait...), Sonnet 4.5 can fire them in parallel. The result? I maxed out the context window with actions, not waiting.

Customer reports back this up:

44% faster vulnerability intake in security workflows
25% improved accuracy in multi-step agent tasks

That second stat is key. Speed doesn't matter if the output is garbage. The fact that accuracy improved while getting faster tells me the model isn't just rushing — it's optimized.

One thing that threw me at first: on ultra-long contexts (150K+ tokens), there's a latency bump. It's physics — more tokens = more processing. But Anthropic's context editing feature helps here. Instead of re-processing everything, you update only what changed. Practical speed boost: significant.

Claude Sonnet 4.5 Pricing and Subscription Plans

API Pricing Breakdown

Alright, let's get into the money talk. Because pricing determines whether this stays in your toolbox or becomes "that cool model I can't afford to run."

Here's the straight breakdown for Claude Sonnet 4.5 API pricing:

Input tokens:

≤200K context: $3.00 per million tokens

200K context: $6.00 per million tokens

Output tokens:

≤200K context: $15.00 per million tokens

200K context: $22.50 per million tokens

Prompt caching (this is clutch if you're reusing context):

Write cache: $3.75/million (≤200K), $7.50/million (>200K)
Read from cache: $0.30/million (≤200K), $0.60/million (>200K)

If you assume a typical 5:1 input-to-output ratio, the blended rate works out to around $5.00 per million tokens.

Now, here's what that actually costs in real use:

I ran a batch processing job last week — 100 million input tokens, 20 million output. Total cost: $600. For context, that's about 200 hours of continuous document analysis, code generation, and agent workflows.

Is that cheap? Not if you're experimenting casually. But if you're running production workflows where accuracy matters (legal synthesis, code review, research), it's competitive. You're paying for precision, not just token throughput.

One more thing: the pricing is unchanged from Claude Sonnet 4, but the performance is significantly better. That's effectively a price cut in terms of value per dollar.

Claude Pro Subscription Features

If you're not ready to commit to API usage, there's Claude Pro: $20/month ($17 in the first month).

What you get:

Access to all Claude models, including Sonnet 4.5, Opus, and Haiku
Usage limits (not unlimited, but generous for individual use)
Rate caps to prevent bill shock
Access to features like code execution, file uploads, and long conversations

I used Claude Pro for about two months before switching to API access. The subscription is perfect for exploratory work — you can test Sonnet 4.5 on real tasks without worrying about per-token costs. But once you're running automated workflows or high-volume tasks, API pricing becomes more predictable.

There's also Claude Max, a higher tier that gives you early access to experimental features (like "Imagine with Claude") and extensions. I haven't upgraded to Max because I don't need the extras, but if you want to be on the cutting edge, that's the unlock.

Cost Comparison with Other AI Models

Okay, so how does Sonnet 4.5 stack up against the competition?

vs. GPT-5:

Sonnet 4.5: $1,050 for 100M input / 20M output
GPT-5: $325 for the same workload

GPT-5 is way cheaper. No question. But here's the kicker: on coding benchmarks like SWE-bench, Sonnet 4.5 scores 77.2% vs. GPT-5's 72.8%. That 4.4 percentage point gap might not sound huge, but in practice it's the difference between "mostly works" and "I trust this to ship."

If you're optimizing for cost and your tasks are straightforward, GPT-5 wins. If accuracy and reliability matter more (think production code, legal work, research), Sonnet 4.5 justifies the premium.

vs. Gemini 2.5 Pro:

Gemini's pricing: $1.25-$2.50 per million input tokens (cheaper than Sonnet's $3)
Gemini also has a free tier, which is huge for experimentation

But on agentic tasks, Sonnet 4.5 dominates. Example: Finance Agent benchmark — Sonnet scores 55.3%, Gemini scores 29.4%. That's not close.

My takeaway: Gemini is great for multimodal tasks and cost-conscious workflows. Sonnet 4.5 is what you use when the task complexity demands it.

So what's the bottom line? Sonnet 4.5 isn't the cheapest option. But it's priced for value, not volume. If your workflows depend on accuracy, sustained reasoning, or complex agentic behavior, the cost justifies itself pretty quickly.

Best Use Cases for Claude Sonnet 4.5

Long Document Analysis and Summarization

Let me walk you through a real scenario.

Last month, I needed to analyze a 150-page financial disclosure document. Not just "give me the highlights" — I needed risk identification, cross-referenced clauses, and inconsistencies flagged across sections that were 80+ pages apart.

I loaded the full PDF into Sonnet 4.5's 200K context window. No chunking. No summarization-then-analysis. Just the whole thing.

Here's what I asked it to do:

Identify all risk factors mentioned anywhere in the document
Cross-reference financial projections in Section 3 with assumptions in Section 9
Flag any contradictions or ambiguous language
Synthesize a coherent risk assessment

It nailed it. The output was a 12-page report with specific page references, highlighted inconsistencies, and even caught a projection mismatch I'd missed in my first read-through.

What makes Sonnet 4.5 unusually good at this:

Context retention: It didn't "forget" early sections when analyzing later ones. The 200K window meant everything stayed in working memory.
Logical coherence: The synthesis wasn't just bullet points. It built an argument, traced causal chains, and maintained narrative flow.
Precision: When it cited something, it cited the right thing. No hallucinated page numbers or fabricated quotes.

This works for:

Legal briefs and case law analysis
Academic literature reviews
Technical documentation synthesis
Multi-source research compilation

One caveat: if your document is poorly structured or full of jargon without context, Sonnet will still struggle. Garbage in, garbage out. But for well-written, dense material? It's the best tool I've used.

Coding Assistance and Debugging Support

Okay, this is where Sonnet 4.5 really flexes.

SWE-bench Verified score: 77.2%. That's state-of-the-art. But let me tell you what that looks like in practice.

I was debugging a Rust project — about 8,000 lines of code across 40 files. The bug was subtle: a memory leak that only appeared under specific concurrency conditions. Previous AI assistants either couldn't hold the entire codebase in context or gave me generic "check your pointers" advice.

Sonnet 4.5:

Loaded the full codebase (fit comfortably in the 200K window)
Traced the data flow across multiple modules
Identified the specific closure that wasn't releasing references
Generated a patch with tests

Time to fix: 18 minutes. My previous attempt (manually): 3 hours.

Here's what it's genuinely good at:

Autonomous feature building: I've given it feature specs and watched it generate pull requests, write tests, and even refactor adjacent code for consistency. The 30+ hour sustained focus claim? Not marketing. I've seen it work through complex implementations without losing the plot.

Multi-language support: Python, Java, Rust, TypeScript — it handles all of them well. I've noticed slightly better performance on Python and JavaScript (more training data, I assume), but the Rust and Java work was still solid.

Error rate on code editing: 0% according to Anthropic's benchmarks. In my experience, that's... mostly accurate. It doesn't randomly break working code. The changes it makes are deliberate and traceable.

Integration: Works with VS Code via API, and I've heard it plays nicely with GitHub Copilot workflows (though I haven't tested that personally).

Where it's less helpful:

Greenfield architecture decisions: It can implement what you spec, but it won't challenge your architectural choices. If your design is flawed, it'll build the flaw.
Obscure frameworks: If you're working with a niche library that's not well-documented, it'll struggle more than with mainstream tools.

Writing, Editing, and Content Generation

This is where the reduced misalignment work really shows.

I used to get frustrated with AI writing tools because they'd either be overly formal ("It is important to note...") or weirdly sycophantic ("Great question! You're absolutely right..."). Sonnet 4.5 feels... different. More direct. Less performative.

Here's what I use it for:

Technical writing: I've generated entire technical specs, architecture docs, and API documentation. The output is clear, structured, and actually useful. It doesn't pad with fluff. It doesn't over-explain simple concepts. It just writes what needs to be written.

Editing and refinement: I'll draft something rough, then ask Sonnet to tighten it. The edits are smart — it removes redundancy, clarifies ambiguous phrasing, and fixes logical flow issues. It's like having an editor who actually understands what you're trying to say.

Content generation: Blog posts, research summaries, even scripts. The tone is flexible — you can push it toward technical, conversational, or formal, and it adapts without sounding forced.

What sets it apart from other models:

Less sycophancy: It doesn't constantly agree with you. If you ask it to edit something that's fine as-is, it'll tell you. That might sound small, but it saves time — you're not second-guessing whether the AI is just being polite.

Better reasoning in writing: When I ask it to argue a position or synthesize conflicting sources, it actually builds a logical case. It doesn't just summarize — it thinks through the argument structure.

Content length: With 64K token output capacity, you can generate seriously long-form content in one shot. I've produced 30-page research reports, multi-chapter drafts, and comprehensive guides without needing to stitch together multiple responses.

One thing to watch out for: if you're writing for a very specific brand voice or highly creative fiction, you'll still need to edit heavily. Sonnet is great at clarity and structure, but it's not going to nail a quirky, personality-driven voice without a lot of prompt engineering.

Claude Sonnet 4.5 Limitations and Weaknesses

Known Performance Gaps and Edge Cases

Alright, real talk time. Because if I just hyped the wins without calling out the failures, this wouldn't be useful.

Safety classifiers are overly sensitive: This is the most annoying limitation. Sonnet 4.5 has ASL-3 safety guardrails baked in, which is great for preventing misuse. But sometimes they fire on completely harmless content.

Example: I was writing a technical article about chemical processes (totally benign, educational context). The model flagged it as potentially CBRN-related and refused to continue. I rephrased, tried again, same block. Anthropic has reduced false positives by 10x since initial release, but they're still there.

This mostly affects:

Technical writing in chemistry, biology, or security
Legal or policy discussions involving sensitive topics
Certain historical or academic contexts

Workaround: rephrase your prompt to be more obviously educational/research-focused. Or switch to Opus if you're hitting consistent blocks (Opus has slightly less aggressive filtering).

No native audio or video output: Sonnet 4.5 can ingest audio and video (multimodal input), but outputs are text-only. If you need voice synthesis or video generation, you're chaining it with another tool. Not a dealbreaker, but worth knowing.

Knowledge cutoff: January 2025: This isn't a flaw per se, but it means current events after early 2025 are outside its training. If you're asking about breaking news or recent developments, you'll need to provide context or use a search-augmented setup.

Higher latency on ultra-long contexts: I mentioned this earlier, but it's worth repeating. If you're pushing 150K+ tokens into the context window, expect 4-5 second response times. That's fine for research or batch processing. Less fine for real-time interactive use.

Not optimized for free-tier high-volume use: If you're on Claude Pro and trying to run hundreds of queries a day, you'll hit rate limits. The subscription is built for moderate individual use, not production-scale automation. For high volume, you need API access (and the budget to match).

When to Consider Opus or Other Models

Here's my decision tree for when I switch away from Sonnet 4.5:

Use Opus if:

You're working on deep domain expertise tasks (advanced STEM, legal synthesis, finance)
Sonnet's benchmark scores aren't cutting it (e.g., GPQA Diamond: Sonnet 83.4%, Opus likely higher)
You need that extra layer of frontier intelligence for nuanced reasoning
Cost isn't the primary constraint

Example: I was working on a complex legal opinion that required synthesizing case law across three jurisdictions, identifying conflicts, and building a predictive argument. Sonnet 4.5 got me 80% there, but the final synthesis felt... shallow. I switched to Opus. The output was noticeably deeper, with better handling of edge cases and conflicting precedents.

Use Haiku if:

You need speed and volume over depth
Tasks are simple: classification, summarization, basic Q&A
You're running high-volume production workloads where cost matters
Latency is more important than reasoning depth

Example: customer support automation, tagging/classification pipelines, quick document summaries.

Use GPT-5 if:

Cost is the top priority and accuracy can be "good enough"
Your tasks are straightforward (not agentic, not ultra-complex reasoning)
You're experimenting and don't want to burn budget

Use Gemini 2.5 Pro if:

You need multimodal output (video, audio generation)
You want a free tier for testing
Your tasks are more creative/generative than analytical

My honest take: Sonnet 4.5 is my default for 90% of work. But I keep Opus and Haiku in the toolkit for the edges. And I track GPT-5 pricing because if my workflows get more predictable and less complex, I might switch for cost reasons.

How to Access Claude Sonnet 4.5

Access via Claude.ai Platform

The easiest way to start: just go to claude.ai.

You'll get access through:

Web interface: Works on any browser. Clean, minimal, fast.
Mobile apps: iOS and Android. Same experience, optimized for smaller screens.

Free tier is available, which is huge if you're just testing. You can run queries, upload files, and get a feel for the model without committing to a subscription.

But here's what you unlock with paid plans (Claude Pro or Max):

Code execution: You can run Python scripts directly in the chat. This is clutch for data analysis or debugging.
File uploads: PDFs, CSVs, images — just drag and drop.
Long conversations: The free tier has stricter rate limits. Paid plans let you go deeper without hitting walls.

I started on the free tier to validate that Sonnet 4.5 could handle my workflows. Once I confirmed it was worth the investment, I upgraded to Pro. No regrets.

Using Claude Sonnet API

If you're building tools, automations, or integrating Claude into your own systems, the API is your path.

Model ID: "claude-sonnet-4-5"

You can access it through:

Anthropic's direct API: Full control, pay-as-you-go pricing
Amazon Bedrock: If you're already in the AWS ecosystem
Google Cloud Vertex AI: For GCP users

The API also supports developer tools like:

Agent SDK: For building custom AI agents with tool use, memory, and multi-step workflows
Prompt caching: Reuse context across calls to save tokens (and money)
Context editing: Update parts of your context without reloading everything

I've been using the API for a few weeks now, and the flexibility is unmatched. You can fine-tune request parameters (like reasoning depth, temperature, output length) in ways the web interface doesn't expose.

One tip: if you're just getting started with the API, test on small workloads first. It's easy to burn through tokens if you're not careful with context size and output settings.

Using Claude via Macaron for Personal Workflows

Here's where I need to be transparent: I work on Macaron, so I'm biased. But I'm also using it daily, so I can tell you exactly why it matters for Sonnet 4.5 workflows.

Macaron is designed to make AI tools work in real tasks — not just demos. Here's how I use it with Sonnet 4.5:

Seamless API integration: I don't want to write boilerplate code every time I need to call Sonnet. Macaron handles authentication, request formatting, and error handling. I just define the task and let it run.

Workflow persistence: I can save entire conversation histories, agent states, and execution logs. This is critical for long-running projects where I need to pick up where I left off days later.

Multi-model orchestration: Sometimes I need Sonnet for reasoning, Haiku for quick checks, and Opus for deep dives. Macaron lets me switch models mid-workflow without rebuilding the context.

Cost tracking: I can see exactly how much each task costs in real-time. No surprises at the end of the month.

If you're building personal workflows (research pipelines, content systems, agent automation), register for Macaron to get optimized access tailored for Sonnet users. It's what I use to make sure my tests are reproducible and my workflows stay stable.

FAQ: Claude Sonnet 4.5 Features, Pricing, and Usage

Q: What is the context window for Claude Sonnet 4.5?

200,000 tokens as standard. There's also a 1M token option for specialized use cases, but you need to request access for that. For reference, 200K tokens is roughly 150,000 words or 300-400 pages of text.

Q: How does Sonnet 4.5 compare to GPT-5?

Sonnet 4.5 is better at coding (77.2% vs. 72.8% on SWE-bench) and agentic tasks. It's also more expensive — about 3x the cost for equivalent workloads. GPT-5 wins on price and is fine for simpler tasks, but if accuracy and sustained reasoning matter, Sonnet is worth the premium.

Q: Is Claude Sonnet 4.5 safe for production use?

Yes, with caveats. It has ASL-3 safety guardrails, which are solid for preventing misuse. But you might hit false positives on technical or sensitive content (chemistry, security topics, etc.). Test thoroughly before deploying in production, and have a fallback plan if the safety classifiers block legitimate use cases.

Q: What are the best alternatives to Sonnet 4.5?

GPT-5: If cost is your top priority and tasks are straightforward
Gemini 2.5 Pro: If you need multimodal output or a free tier
Claude Opus: If you need deeper domain expertise and frontier reasoning
Claude Haiku: If you need speed and volume over depth

Each has trade-offs. Sonnet 4.5 is the best all-around pick for coding, agents, and long-document work.

Q: How much does API access cost?

Input: $3 per million tokens (≤200K context), $6 (>200K)
Output: $15 per million tokens (≤200K), $22.50 (>200K)
Blended rate (5:1 input/output): ~$5 per million tokens

For a typical workload (100M input, 20M output), you're looking at about $600.

Q: Can I use Sonnet 4.5 for free?

Yes, via claude.ai's free tier. You'll have rate limits and fewer features, but it's enough to test the model on real tasks. If you need higher volume or advanced features (code execution, file uploads), upgrade to Claude Pro ($20/month).

Q: What's the difference between Claude Pro and API access?

Claude Pro is a flat $20/month subscription with usage limits. Good for individual use, exploration, and moderate workflows.

API access is pay-as-you-go based on token usage. Better for production automation, high-volume tasks, or if you need programmatic control.

I used Pro for testing, then switched to API once my workflows scaled.

Q: Does Sonnet 4.5 support audio or video output?

No. It can ingest audio and video (multimodal input), but outputs are text-only. If you need voice synthesis or video generation, you'll need to chain it with another tool.

So what's the bottom line?

Claude Sonnet 4.5 is the model I keep coming back to. Not because it's perfect — it's not. But because it handles the messy, multi-step, sustained-reasoning tasks that most AI models flake out on.

If you're building agents, debugging complex code, or analyzing long documents, this is the tool. The 200K context window, parallel tool execution, and reduced shortcut behavior make it feel less like "fancy autocomplete" and more like "actual reasoning assistant."

The pricing isn't cheap, but it's justified if accuracy matters. And the fact that performance improved while pricing stayed flat? That's a win.

I'm running all my primary workflows through Macaron now — it's how I stress-test, track costs, and keep workflows reproducible. If you're ready to see whether Sonnet 4.5 fits your work, register for Macaron and run your own tests. Low cost to start, easy to bail if it doesn't work.

Your call. But if you're serious about making AI tools actually work in production, this is where I'd start.