Three Months on Claude Opus 4.6: What It's Actually Like, Plus the Pricing (April 2026)
Translated from the original Korean post. 한국어 원문 보기 →
What changed in the Claude 4.6 lineup
I was cleaning up my working setup when I realized my main model had rolled over again. As of April 2026, Anthropic's Claude lineup is three branches: Opus 4.6, Sonnet 4.6, Haiku 4.5. Another generation up, and the difference is not subtle.
Two things really changed. Opus 4.6 and Sonnet 4.6 both support a 1M token context, and Anthropic's own docs now tell 3.5-generation users to move to the 4.6 line. At that point 3.5 is legacy in everything but name.
I've been running Opus 4.6 as my daily driver for the past three months. Here's what I wrote down, plus a look at the pricing structure. Not the marketing copy — the view from someone who actually paid for it.
The 4.6 series, looked at structurally
Putting Opus 4.6 next to 3.5 Sonnet isn't really a fair comparison. They're not the same product at different sizes. They're different classes of tool.
The changes that actually mattered in daily use: the 1M token context means I can drop in a whole report or a whole codebase without losing the thread. Reasoning holds up better when I drag a branchy problem through step after step. Korean handles cultural nuance and slang without stumbling. Coding goes past isolated functions into project-level structure. Reading images, documents, and charts got more precise. And in long-form writing, the world stays consistent — characters and context don't drift as the conversation stretches out.
From an operations standpoint, the 1M context is the big one. Not because "you can stuff more in," but because it changes the shape of the pipeline. Before, long documents meant a mandatory preprocessing step: chunk it, then stitch the pieces back together. Context leaks out along the way, and once you're working from summaries of summaries, accuracy goes with it. A big enough context deletes that preprocessing layer outright.
Context size isn't a performance number. It's an architectural variable that removes a layer.
Sonnet 4.6 is lighter than Opus and still more than enough for everyday work. On a cost basis there's a pretty wide band where Sonnet is the sensible pick. Haiku 4.5 has settled into short, frequent jobs where response time is the whole game.
2026 pricing, structure first
Prices only mean something if they're exact, so here they are as-is. Current as of April 2026.
Claude Pro for individuals is $20/month billed monthly, or $17/month billed annually ($200 up front). You get both Opus 4.6 and Sonnet 4.6, the 1M token context, and image analysis. There's a monthly usage cap, but it's generous enough for normal use.
Claude Max, for power users, starts at $100/month and scales with usage. Higher limits, priority handling for faster responses, early access to new features.
Claude Team, for teams and companies, is per seat. Standard Seat is $20/month annual, $25/month monthly. Premium Seat is $100/month annual, $125/month monthly. Team collaboration features, admin tools, and priority support come with it.
The developer API is pay-per-token. Opus 4.6 is $5/MTok input and $25/MTok output. Sonnet 4.6 is $3/MTok input, $15/MTok output. Haiku 4.5 is $1/MTok input, $5/MTok output.
For daily personal use, Pro on annual billing is the easy answer. Reaching Opus 4.6 for $17 a month is a good deal.
One thing worth separating, though. A $17 subscription and API token billing are different animals. A subscription is flat up to a cap; the API is metered. If you're a person using it casually, the subscription wins by a mile. The moment you start running automation or batch jobs, API charges pile up fast. Especially on Opus, where output runs $25/MTok. Figuring out whether your usage is conversational or batch is step one of any cost planning.
Three months with Opus 4.6
Notes from running it as my main model on an annual Pro subscription.
Documents first. I can upload a 100-page planning doc whole and ask for the three key points, and it pulls them out fairly accurately. Back in 3.5 I had to split documents up, which broke the connective tissue and produced summaries that missed the point. With 4.6 it all goes in at once, and the difference in comprehension is obvious. This is where losing that preprocessing layer paid off most.
For coding it's a solid partner from structural design through debugging. Legacy refactoring impressed me the most — it takes in the whole structure at once and proposes improvements, cutting down the several days a person needs just to get familiar with a codebase. I don't take those proposals at face value, of course. I use it to sketch the big picture quickly, and I make the call. That line didn't move once in three months.
Creative work holds up too. Hand it character setups or a plot and the flow comes out natural. World consistency is clearly better than the previous generation, and the longer the piece, the more it shows.
The downsides are real. Pro subscribers have usage caps, and Opus burns tokens, so heavy sessions do hit the limit. The performance comes with latency, so when I'm in a hurry I switch to Sonnet 4.6. And anything past the training cutoff is still out of reach, which means pairing it with search-based tools.
After three months I'd give Opus 4.6 about a 95. At $17 a month on annual billing, that's satisfying. But it's a score with a condition attached: personal, conversational use. Change the usage pattern and both the satisfaction and the cost move.
What Moltbot actually is: OpenClaw
There's some confusion around Moltbot, so let me straighten it out. People take it for a nickname for Claude or a community meme. It's the former project name for OpenClaw. Per the official docs, it started as Clawd, became Moltbot, and is now OpenClaw.
OpenClaw today is a personal AI assistant platform that hooks into chat platforms like WhatsApp, Telegram, and Slack and does real work there. Claude API on the backend, running tasks across multiple channels.
There's a structural point worth pinning down. OpenClaw is not a model. It's an orchestration layer sitting on top of one. If Claude is the engine, OpenClaw is closer to the wiring panel that connects that engine to a bunch of channels and routes work to it. Filing Moltbot under "nickname for Claude" flattens the model and the platform above it into the same layer. They do different jobs.
Where the 2026 AI race stands
The April 2026 market is crowded. Here's where the main players are:
| 회사 | 현재 주력 모델 | 월 구독료 | 특징 |
|---|---|---|---|
| Anthropic | Claude Opus 4.6 / Sonnet 4.6 | $17-20 | Safety + long context |
| OpenAI | GPT-5.3 기본 + GPT-5.4 Pro | $20 | Generality + creativity |
| Gemini 3.1 Pro | $20 | Search integration + real-time info | |
| NAVER | HyperCLOVA X / CLOVA X | TBD | Korean-specialized + local info |
OpenAI retired the GPT-4o and GPT-4.1 families on February 13, 2026, and now runs on GPT-5.3 and GPT-5.4. Google stepped up again with Gemini 3.1 Pro.
What stands out is that the major services have all converged on roughly $20 a month, as if they agreed on it. Price isn't a differentiator anymore, so the competition has shifted to where each one is strong. Safety and long context. Generality and creativity. Search integration. Korean specialization. Everyone's holding a different card.
Picking a model by task
Based on running several models side by side for three months, here's how I've been combining them.
For research and analysis, Claude Opus 4.6 is first because of the 1M context and reasoning depth, with GPT-5.4 Thinking as the backup when I need step-by-step reasoning. For programming, Opus 4.6 again leads on understanding large codebases, with GPT-5.4 Pro second when I need a creative way out. For creative and planning work the order flips: GPT-5.4 Pro first for creativity, Claude Sonnet 4.6 second when I care about structural completeness. For real-time search, Gemini 3.1 Pro with Google search built in comes first, ChatGPT's web browsing second. For Korean-specific work, Opus 4.6 is ahead on cultural context, and I add NAVER HyperCLOVA X when I need domestic information.
Rather than forcing one model to do everything, keeping Opus 4.6 as the main and attaching task-specific backups turned out to be the practical setup. Wiring together tools with different strengths, pipeline-style, beats betting everything on a single monolith.
Second half of 2026
Things look like they'll move faster from here. On the technical side: multimodal integration handling text, image, voice, and video together; personalization through per-user learning and adaptation; real-time processing that analyzes live data and reacts on the spot; and AI workers — agents — that carry out actual work.
On the market side, NAVER, Kakao, and Samsung are entering global competition in earnest, heating up the Korean AI market, and the efficiency fight inside the $20 regime gets sharper. Industry-specific AI spreads into professional domains like medicine, law, and education, alongside a privacy-first current built on local processing and data protection.
From an operations seat, agents carry the most weight. AI that answers and AI that does the work are different problems. The second one drags along permissions, accountability, audit logs, and rollback on failure. That's the phase where the infrastructure around the model, not the model itself, becomes the real work. Six months from now the picture will probably look different again.
Does holding a 95-point tool make me a 95?
That's three months of running Opus 4.6 as my main model. Getting a tool at this level for $17 a month on annual billing is, on cost structure alone, a good era to be working in.
The case for it, one more time: a 1M token context that handles large documents and codebases, strong Korean comprehension and cultural context, and reasoning that works complex problems out step by step. Stable, consistent performance on long jobs at the $17-20 price point.
One thing I want to be clear about. Better tools raise the average quality of what comes out, but if you lean on that average and hand over your judgment too, your own capability never accumulates. AI does sketch the big picture fast. Weighing the trade-offs on top of that sketch, catching the risks, making the final call — still on the person.
Holding a 95-point tool doesn't make me a 95. The tool's performance and the judgment of whoever's using it are separate layers.
Lately, though, I mix those two layers up more often than I'd like. Where the model's picture ended and my judgment began — I went back through three months of conversation logs and still couldn't draw the line. No idea what I'll think of this post six months from now.
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